Editorial Preface

It is with great pleasure that we present the 4th regular issue of Volume 10 of the International Journal of Integrated Engineering (IJIE). This edition features the latest findings and research in the area of Civil and Environmental Engineering, Electrical and Electronic Engineering and Mechanical, Materials and Manufacturing Engineering.We would like to extend our sincere gratitude and appreciation for the enthusiastic and vigorous support and contributions from the Editorial Board and Reviewers of IJIE for taking time and effort to review manuscripts. As no manuscript is accepted or rejected without careful reading by experts in a particular area to which the paper is related. The experts have maintained a high standard of scholarship and we believe the readers of this Journal deserves.It is our hope that this fine collection of articles will be a valuable resource for International Journal of Integrated Engineering (IJIE) readers and will stimulate further research in the area of Civil and Environmental Engineering, Electrical and Electronic Engineering and Mechanical, Materials and Manufacturing Engineering. We strongly encourage authors to submit their articles and readers to provide feedback. In order to access the online version of this issue along with archived editions please visit our website http://penerbit.uthm.edu.my/ojs/index.php/ijie/.We would like to thank all the authors who have contributed manuscripts in IJIE and those who are awaiting their manuscripts for publication in subsequent issues

Inferring export orientation from corporate websites

This is an author's accepted manuscript of an article published in: “Applied Economics Letters"; Volume 21, Issue 7, 2014; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/13504851.2013.872752The purpose of this article is to infer indicators about the export orientation of firms from the analysis of their corporate websites. Using a dataset of manufacturing firms, two logistic regressions were performed and compared: one considering some firm structural variables, and another considering some web-based variables. Results showed that the website features are good predictors of the export orientation of firms, performing as well as the classic economic variables.Blázquez Soriano, MD.; Doménech I De Soria, J. (2014). Inferring export orientation from corporate websites. Applied Economics Letters. 21(7):509-512. doi:10.1080/13504851.2013.872752S509512217Bonaccorsi, A. (1992). On the Relationship Between Firm Size and Export Intensity. Journal of International Business Studies, 23(4), 605-635. doi:10.1057/palgrave.jibs.8490280DA, Z., ENGELBERG, J., & GAO, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461-1499. doi:10.1111/j.1540-6261.2011.01679.xDzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167-175. doi:10.1016/j.frl.2011.10.003Freund, C. L., & Weinhold, D. (2004). The effect of the Internet on international trade. Journal of International Economics, 62(1), 171-189. doi:10.1016/s0022-1996(03)00059-xGirma, S., Greenaway, avid, & Kneller, R. (2004). Does Exporting Increase Productivity? A Microeconometric Analysis of Matched Firms. Review of International Economics, 12(5), 855-866. doi:10.1111/j.1467-9396.2004.00486.xLee, J., & Morrison, A. M. (2010). A comparative study of web site performance. Journal of Hospitality and Tourism Technology, 1(1), 50-67. doi:10.1108/17579881011023016Murphy, J., & Scharl, A. (2007). An investigation of global versus local online branding. International Marketing Review, 24(3), 297-312. doi:10.1108/02651330710755302Nassimbeni, G. (2001). Technology, innovation capacity, and the export attitude of small manufacturing firms: a logit/tobit model. Research Policy, 30(2), 245-262. doi:10.1016/s0048-7333(99)00114-6Preis, T., Reith, D., & Stanley, H. E. (2010). Complex dynamics of our economic life on different scales: insights from search engine query data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), 5707-5719. doi:10.1098/rsta.2010.0284Spence, M. M. (2003). Small Business Economics, 20(1), 83-103. doi:10.1023/a:1020200621988Varian, H. R. (2010). Computer Mediated Transactions. American Economic Review, 100(2), 1-10. doi:10.1257/aer.100.2.1Wholey, J. S., & Hatry, H. P. (1992). The Case for Performance Monitoring. Public Administration Review, 52(6), 604. doi:10.2307/97717

Increasing Children's Character Overt Behaviours by Neuro Pedagogy-Based Play

The research aims to improve the character of children aged 4-6 years by implementing the way the individual interacts with the environment. Qualitative research methods were used in this study, where researchers investigated the open behavior of children aged 4-6 years during neuropedagogy-based play, as well as the open behavior of preschool teachers' professional characters in managing play. The participants in this research were 20 children aged 4-6 years and 4 teachers who were chosen deliberately. The results of the study revealed that all the children's overt character behaviors improved after participating in neuropedagogy-based play. There was not a single child who was classified as having open early behavior. On the other hand, preschool teachers consistently demonstrate the implications of their professional behavior. Based on research findings, it is proven that neuro-pedagogy-based play is beneficial for preschool children and teachers. Extrapolated from this interpretation, curriculum designers and teaching practices are informed that neuro pedagogy-based play can strengthen children's character, this is necessary in preparing future generations who can overcome all challenges in the future. Keywords: neuro pedagogy-based play, open character behavior, children aged 4-6 years References: Bandura A (1997) Social Learning Theory. New Jersey: Prentice  Hall, Inc A Paramount Communication Company Englewood Cliff Beibert M. Hanna & Hasellhorn  Marcus (2016). Individual Differences in Moral Development : Does Intelligence Really Affect Children’s Moral Emotion? Department of Education and Human Development. German Institute for International Educational Research, Frankfurt am Main. Germany Bonomo Virginia (2017) Brain-Based Learning Theory. Journal of Education and Human Development, Vol 6 No 1(Online) DOI: 10.15640/jehd.v6n1a3. URL: http://doi.org/10.15640/jehd.v6na3 Bransford J, Brown A, and Cocking, (2000). How People Learn: Brain, Mind and Experience & School. Washington, DC: National Academy Press Budiartati Emmy. Y & Jamaris Martini  (2018) Music Instructional to Develop Character Value for Early Childhood at Fishery Community Tambak Lorok Semarang City. Journal of Non-Formal Education 4, 47-56 Carter R, Aldridge S, Pge M, & Parker S ( 2009 ) The Human Brain Book. New York, NY: DK Publishing Chojak. M (2018) Neuropedagogy as a Scientific Discipline: Interdisciplinary Description of the Theoretical Basis for the Development of a Research  Field. World Academy of Science, Engineering and Technology. International Journal of Education and Pedagogical Science . Vol 12, No 8, 2018 Chudler E.C (2005) Brain Plasticity What is it?. http://www.faculty.washington.edu/chudler/plast.html Collin Gallian & Dixson Hazel. (1993). Integrated Learning Planned Curriculum Unit (Bookshelf tt) Collin. JW (2007) The Neuroscience and Learning. The Journal of Neuroscience Nursing: Journal of The American Association of Neuroscience Nurses, 39 (5) pp 395-400 Dick Walter & Carey Lou (1985). The Systematic Design of Instruction.  Glenview, Illinois, London England: Scoot, Foresman and Company Fogerty Robin (1991). How to Integrate Curricula. Palaline: IRI/Skylight Training and Publishing Inc. p xii Friedman Issac, Grobgeld Etty & Teichman Weinberg Ariela ( 2016). Neuropedagpgy in Teacher Education. The Mofet Institute: International Portal of Teacher Education. Haghigh Maryam (2013). The Effect of Brain Learning on Iranian EFL Learners ‘Achievement and Retain. Procedia-Social and Behavioral Science, vol 70, page 508-516 Hayes, B. Grant & Hagedorn, W. Bryce (2000).” A Case of Character Education.” Journal of Humanistic Counseling, Education and Development 39, (1) 2-4 Henniger L. Mitchel (2013) Teaching Young Children. One Lake Street, Upper Saddle River, USA: Pearson Education Intan Ahmad (2019) Pidato Rektor Wisuda Tahun Akademik 2018-2019. Jakarta: Unversitas Negeri Jakarta, 1-12 Keles Esra & Cepni Salih,(2006 ) Brain and Learning. Journal of Turkish Science Education, Volume 3, Issue 2, December 2006 Lapina SJ & Colombo JA. (2009 )  Proverty and Brain Development During Cilhdhood: An Approach from Cognitive Psychology and Neuroccience. Washsington, DC. Amarivan Psychological Association Likona, Thomas ( 1996) Eleven Principles for Effective Character Education. Journal of Moral Education 25 (1) :93-100 Martini Jamaris (2016) Empowering Logical Mathemetical of the 4-6 Years Old Children through the Neurosensory Instructional Approach. American Journal of Educational Research. Vol 4. No 10, 768-776.  DOI: 10.12691/education4-10-10 Martini Jamaris & Sofia Hartati (2017) The Role of Undergraduate Students ‘Self-Regulation and Its Influence on their Academic Achievements. International Journal of Multidisciplinary and Current Research 5 May/June 2017, 544-552 Martini Jamaris, Edwita & Trisna Mulyeni (2019) Instructional Model Based Neuro Pedagogy for Character Education of the 4-6 Years Old Children. Research Report. Jakarta  LPPM Universitas Negeri Jakarta. Nass L. Martin (2017) The Superego and Moral Development in the Theories of Freud and Piaget. The Psychoanalytic Study of the Child, Volume 21, 1966,Issue1 1. Contribution of Psychoanalysis Theory. http://doi.org/10.1080/00797308. 1966. 11823252 O’Rahilly R, Mueller F ( 2008) Significant Feature in The Early Prenatal Development of The Human Brain.   Annal Of Anatomy Porter Nancy (1972) Kohlberg and Moral Development. Journal of  Moral Education volume 1 1972, Issue2. Publish on line  7 Juli 2016, http://doi.org/10.1080/030572472001­0206 Rushton. Stephen, Rushon - Jualo Anne & larkin Elizabeth (2019) Neuroscience, Play and Early Childhood Education: Connections, Implications and Assessment.  Early Childhood Education Journal 37: 351-361. DOI 10.1007/s10643-009-0359-3 Santrock, W. John (1996 ) Child Development. Chicago: Brown & Benchmark Seravac, Zoran & Jevanovic Jelena ( 2012) Adaptive Neuro-Fizzy Pedagogical Recommender. Expert Systems with Applications. Volume 39, Issue 10. , 9797-9806 Shearer, Banton (2018). Multiple Intelligences in Teaching and Education Lessons Learned from Neuroscience. Journal of Intelligence, Vol 6, No 38; doi: 10.3390/jintellgence6030038 Soldz ,Stephen (1988) The Construction of Meaning: Kagan, Piaget, and Psychoanalysis. Journal of Contemporary Psychotherapy 18 (1) p 46-59 the phenomena of mind.Nature.com/scitable/blog/brain ) Voytek, Brown (2013) Brain Matricts: How measuring brian biology can explain. White E. Rachel (2012) Power of Play. Minnesota: Minnesota Children’s Museum   &nbsp

Factors Affecting Teacher Readiness for Online Learning (TROL) in Early Childhood Education: TISE and TPACK

This study aims to find empirical information about the effect of Technological Pedagogical Content Knowledge (TPACK), and Technology Integration Self Efficacy (TISE) on Teacher Readiness for Online Learning (TROL). This study uses a quantitative survey method with path analysis techniques. This study measures the readiness of kindergarten teachers in distance learning in Tanah Datar Regency, West Sumatra Province, Indonesia with a sampling technique using simple random sampling involving 105 teachers. Empirical findings reveal that; 1) there is a direct positive effect of Technology Integration Self Efficacy on Teacher Readiness for Online Learning; 2) there is a direct positive effect of PACK on Teacher Readiness for Online Learning; 3) there is a direct positive effect of Technology Integration Self Efficacy on TPACK. If want to improve teacher readiness for online learning, Technological Pedagogical Content Knowledge (TPACK) must be improved by paying attention to Technology Integration Self Efficacy (TISE). Keywords: TROL, TPACK, TISE, Early Childhood Education References: Abbitt, J. T. (2011). An Investigation of the Relationship between Self-Efficacy Beliefs about Technology Integration and Technological Pedagogical Content Knowledge (TPACK) among Preservice Teachers. Journal of Digital Learning in Teacher Education, 27(4), 134–143. Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1813180 Adnan, M. (2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://doi.org/10.33902/JPSP.2020261309 Alqurashi, E. (2016). Self-Efficacy in Online Learning Environments: A Literature Review. Contemporary Issues in Education Research (CIER), 9(1), 45–52. https://doi.org/10.19030/cier.v9i1.9549 Amir, H. (2016). Korelasi Pengaruh Faktor Efikasi Diri Dan Manajemen Diri Terhadap Motivasi Berprestasi Pada Mahasiswa Pendidikan Kimia Unversitas Bengkulu. Manajer Pendidikan, 10(4). Anderson, T. (2008). The theory and practice of online learning. Athabasca University Press. Anggraeni, N., Ridlo, S., & Setiati, N. (2018). The Relationship Between TISE and TPACK among Prospective Biology Teachers of UNNES. Journal of Biology Education, 7(3), 305–311. https://doi.org/10.15294/jbe.v7i3.26021 Ariani, D. N. (2015). Hubungan antara Technological Pedagogical Content Knowledge dengan Technology Integration Self Efficacy Guru Matematika di Sekolah Dasar. Muallimuna: Jurnal Madrasah Ibtidaiyah, 1(1), 79–91. Birisci, S., & Kul, E. (2019). Predictors of Technology Integration Self-Efficacy Beliefs of Preservice Teachers. Contemporary Educational Technology, 10(1). https://doi.org/10.30935/cet.512537 Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S. R., Al-freih, M., Pete, J., Olcott, D., Rodes, V., Aranciaga, I., Bali, M., Alvarez, A. V, Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., Coëtlogon, P. De, … Paskevicius, M. (2020). UVicSPACE: Research & Learning Repository Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126. Brinkley-Etzkorn, K. E. (2018). Learning to teach online: Measuring the influence of faculty development training on teaching effectiveness through a TPACK lens. The Internet and Higher Education, 38, 28–35. https://doi.org/10.1016/j.iheduc.2018.04.004 Butnaru, G. I., Niță, V., Anichiti, A., & Brînză, G. (2021). The effectiveness of online education during covid 19 pandemic—A comparative analysis between the perceptions of academic students and high school students from romania. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095311 Carliner, S. (2003). Modeling information for three-dimensional space: Lessons learned from museum exhibit design. Technical Communication, 50(4), 554–570. Cengiz, C. (2015). The development of TPACK, Technology Integrated Self-Efficacy and Instructional Technology Outcome Expectations of pre-service physical education teachers. Asia-Pacific Journal of Teacher Education, 43(5), 411–422. https://doi.org/10.1080/1359866X.2014.932332 Chou, P., & Ph, D. (2012). Effect of Students ’ Self -Directed Learning Abilities on Online Learning Outcomes: Two Exploratory Experiments in Electronic Engineering Department of Education. 2(6), 172–179. Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Burton, R., Glowatz, M., Magni, P. A., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1). https://doi.org/10.37074/jalt.2020.3.1.7 Dolighan, T., & Owen, M. (2021). Teacher efficacy for online teaching during the COVID-19 pandemic. Brock Education Journal, 30(1), 95. https://doi.org/10.26522/brocked.v30i1.851 Dong, Y., Chai, C. S., Sang, G.-Y., Koh, J. H. L., & Tsai, C.-C. (2015). Exploring the Profiles and Interplays of Pre-service and In-service Teachers’ Technological Pedagogical Content Knowledge (TPACK) in China. International Forum of Educational Technology & Society, 18(1), 158–169. Donitsa-Schmidt, S., & Ramot, R. (2020). Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching, 46(4), 586–595. https://doi.org/10.1080/02607476.2020.1799708 Elas, N. I. B., Majid, F. B. A., & Narasuman, S. A. (2019). Development of Technological Pedagogical Content Knowledge (TPACK) For English Teachers: The Validity and Reliability. International Journal of Emerging Technologies in Learning (IJET), 14(20), 18. https://doi.org/10.3991/ijet.v14i20.11456 Ghozali, I. (2011). Aplikasi multivariate dengan program IBM SPSS 19. Badan Penerbit Universitas Diponegoro. Giles, R. M., & Kent, A. M. (2016). An Investigation of Preservice Teachers ’ Self-Efficacy for Teaching with Technology. 1(1), 32–40. https://doi.org/10.20849/aes.v1i1.19 Gil-flores, J., & Rodríguez-santero, J. (2017). Computers in Human Behavior Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449. https://doi.org/10.1016/j.chb.2016.11.057 Habibi, A., Yusop, F. D., & Razak, R. A. (2019). The role of TPACK in affecting pre-service language teachers’ ICT integration during teaching practices: Indonesian context. Education and Information Technologies. https://doi.org/10.1007/s10639-019-10040-2 Harris, J. B., & Hofer, M. J. (2011). Technological Pedagogical Content Knowledge (TPACK) in Action. Journal of Research on Technology in Education, 43(3), 211–229. https://doi.org/10.1080/15391523.2011.10782570 Hatlevik, I. K. R., & Hatlevik, O. E. (2018). Examining the relationship between teachers’ ICT self-efficacy for educational purposes, collegial collaboration, lack of facilitation and the use of ICT in teaching practice. Frontiers in Psychology, 9(JUN), 1–8. https://doi.org/10.3389/fpsyg.2018.00935 Hung, M. L. (2016). Teacher readiness for online learning: Scale development and teacher perceptions. Computers and Education, 94, 120–133. https://doi.org/10.1016/j.compedu.2015.11.012 Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers and Education, 55(3), 1080–1090. https://doi.org/10.1016/j.compedu.2010.05.004 Juanda, A., Shidiq, A. S., & Nasrudin, D. (2021). Teacher Learning Management: Investigating Biology Teachers’ TPACK to Conduct Learning During the Covid-19 Outbreak. Jurnal Pendidikan IPA Indonesia, 10(1), 48–59. https://doi.org/10.15294/jpii.v10i1.26499 Karatas, M. A.-K. (2020). COVID - 19 Pandemisinin Toplum Psikolojisine Etkileri ve Eğitime Yansımaları. Journal of Turkish Studies, Volume 15(Volume 15 Issue 4), 1–13. https://doi.org/10.7827/TurkishStudies.44336 Kaymak, Z. D., & Horzum, M. B. (2013). Relationship between online learning readiness and structure and interaction of online learning students. Kuram ve Uygulamada Egitim Bilimleri, 13(3), 1792–1797. https://doi.org/10.12738/estp.2013.3.1580 Keser, H., Karaoğlan Yılmaz, F. G., & Yılmaz, R. (2015). TPACK Competencies and Technology Integration Self-Efficacy Perceptions of Pre-Service Teachers. Elementary Education Online, 14(4), 1193–1207. https://doi.org/10.17051/io.2015.65067 Kim, J. (2020). Learning and Teaching Online During Covid-19: Experiences of Student Teachers in an Early Childhood Education Practicum. International Journal of Early Childhood, 52(2), 145–158. https://doi.org/10.1007/s13158-020-00272-6 Koehler, M. J., Mishra, P., & Cain, W. (2013). What is Technological Pedagogical Content Knowledge (TPACK)? Journal of Education, 193(3), 13–19. https://doi.org/10.1177/002205741319300303 Lee, Y., & Lee, J. (2014). Enhancing pre-service teachers’ self-efficacy beliefs for technology integration through lesson planning practice. Computers and Education, 73, 121–128. https://doi.org/10.1016/j.compedu.2014.01.001 Mallillin, L. L. D., Mendoza, L. C., Mallillin, J. B., Felix, R. C., & Lipayon, I. C. (2020). Implementation and Readiness of Online Learning Pedagogy: A Transition To Covid 19 Pandemic. European Journal of Open Education and E-Learning Studies, 5(2), 71–90. https://doi.org/10.46827/ejoe.v5i2.3321 Mishra, P. (2019). Considering Contextual Knowledge: The TPACK Diagram Gets an Upgrade. Journal of Digital Learning in Teacher Education, 35(2), 76–78. https://doi.org/10.1080/21532974.2019.1588611 Moorhouse, B. L. (2020). Adaptations to a face-to-face initial teacher education course ‘forced’ online due to the COVID-19 pandemic. Journal of Education for Teaching, 46(4), 609–611. https://doi.org/10.1080/02607476.2020.1755205 Mulyadi, D., Wijayatingsih, T. D., Budiastuti, R. E., Ifadah, M., & Aimah, S. (2020). Technological Pedagogical and Content Knowledge of ESP Teachers in Blended Learning Format. International Journal of Emerging Technologies in Learning (IJET), 15(06), 124. https://doi.org/10.3991/ijet.v15i06.11490 Murtaza, G., Mahmood, K., & Fatima, N. (2021). Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students The Journal of Academic Librarianship Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students. The Journal of Academic Librarianship, 47(3), 102346. https://doi.org/10.1016/j.acalib.2021.102346 Mustika, M., & Sapriya. (2019). Kesiapan Guru IPS dalam E-learning Berdasarkan: Survei melalui Pendekatan TPACK. 32–35. https://doi.org/10.1145/3306500.3306566 Niess, M. L. (2011). Investigating TPACK: Knowledge Growth in Teaching with Technology. Journal of Educational Computing Research, 44(3), 299–317. https://doi.org/10.2190/EC.44.3.c Oketch, & Otchieng, H. (2013). University of Nairobi, H. A. (2013). E-Learning Readiness Assessment Model in Kenyas’ Higher Education Institutions: A Case Study of University of Nairobi by: Oketch, Hada Achieng a Research Project Submitted in Partial Fulfillment of the Requirement of M. October. Pamuk, S., Ergun, M., Cakir, R., Yilmaz, H. B., & Ayas, C. (2015). Exploring relationships among TPACK components and development of the TPACK instrument. Education and Information Technologies, 20(2), 241–263. https://doi.org/10.1007/s10639-013-9278-4 Paraskeva, F., Bouta, H., & Papagianni, A. (2008). Individual characteristics and computer self-efficacy in secondary education teachers to integrate technology in educational practice. Computers and Education, 50(3), 1084–1091. https://doi.org/10.1016/j.compedu.2006.10.006 Putro, S. T., Widyastuti, M., & Hastuti, H. (2020). Problematika Pembelajaran di Era Pandemi COVID-19 Stud Kasus: Indonesia, Filipina, Nigeria, Ethiopia, Finlandia, dan Jerman. Geomedia Majalah Ilmiah Dan Informasi Kegeografian, 18(2), 50–64. Qudsiya, R., Widiyaningrum, P., & Setiati, N. (2018). The Relationship Between TISE and TPACK among Prospective Biology Teachers of UNNES. Journal of Biology Education, 7(3), 305–311. https://doi.org/10.15294/jbe.v7i3.26021 Reflianto, & Syamsuar. (2018). Pendidikan dan Tantangan Pembelajaran Berbasis Teknologi Informasi di Era Revolusi Industri 4.0. Jurnal Ilmiah Teknologi Pendidikan, 6(2), 1–13. Reski, A., & Sari, K. (2020). Analisis Kemampuan TPACK Guru Fisika Se-Distrik Merauke. Jurnla Kreatif Online, 8(1), 1–8. Ruggiero, D., & Mong, C. J. (2015). The teacher technology integration experience: Practice and reflection in the classroom. Journal of Information Technology Education, 14. Santika, V., Indriayu, M., & Sangka, K. B. (2021). Profil TPACK Guru Ekonomi di Indonesia sebagai Pendekatan Integrasi TIK selama Pembelajaran Jarak Jauh pada Masa Pandemi Covid-19. Duconomics Sci-Meet (Education & Economics Science Meet), 1, 356–369. https://doi.org/10.37010/duconomics.v1.5470 Semiz, K., & Ince, M. L. (2012). Pre-service physical education teachers’ technological pedagogical content knowledge, technology integration self-efficacy and instructional technology outcome expectations. Australasian Journal of Educational Technology, 28(7). https://doi.org/10.14742/ajet.800 Senthilkumar, Sivapragasam, & Senthamaraikannan. (2014). Role of ICT in Teaching Biology. International Journal of Research, 1(9), 780–788. Setiaji, B., & Dinata, P. A. C. (2020). Analisis kesiapan mahasiswa jurusan pendidikan fisika menggunakan e-learning dalam situasi pandemi Covid-19 Analysis of e-learning readiness on physics education students during Covid-19 pandemic. 6(1), 59–70. Siagian, H. S., Ritonga, T., & Lubis, R. (2021). Analisis Kesiapan Belajar Daring Siswa Kelas Vii Pada Masa Pandemi Covid-19 Di Desa Simpang. JURNAL MathEdu (Mathematic Education Journal), 4(2), 194–201. Sintawati, M., & Indriani, F. (2019). Pentingnya Technological Pedagogical Content Knowledge (TPACK) Guru di Era Revolusi Industri 4.0. Seminar Nasional Pagelaran Pendidikan Dasar Nasional (PPDN), 1(1), 417–422. Sojanah, J., Suwatno, Kodri, & Machmud, A. (2021). Factors affecting teachers’ technological pedagogical and content knowledge (A survey on economics teacher knowledge). Cakrawala Pendidikan, 40(1), 1–16. https://doi.org/10.21831/cp.v40i1.31035 Subhan, M. (2020). Analisis Penerapan Technological Pedagogical Content Knowledge Pada Proses Pembelajaran Kurikulum 2013 di Kelas V. International Journal of Technology Vocational Education and Training, 1(2), 174–179. Sum, T. A., & Taran, E. G. M. (2020). Kompetensi Pedagogik Guru PAUD dalam Perencanaan dan Pelaksanaan Pembelajaran. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 4(2), 543. https://doi.org/10.31004/obsesi.v4i2.287 Suryawati, E., Firdaus, L. N., & Yosua, H. (2014). Analisis keterampilan technological pedagogical content knowledge (TPCK) guru biologi SMA negeri kota Pekanbaru. Jurnal Biogenesis, 11(1), 67-72. Suyamto, J., Masykuri, M., & Sarwanto, S. (2020). Analisis Kemampuan Tpack (Technolgical, Pedagogical, and Content, Knowledge) Guru Biologi Sma Dalam Menyusun Perangkat Pembelajaran Materi Sistem Peredaran Darah. INKUIRI: Jurnal Pendidikan IPA, 9(1), 46. https://doi.org/10.20961/inkuiri.v9i1.41381 Tiara, D. R., & Pratiwi, E. (2020). Pentingnya Mengukur Kesiapan Guru Sebagai Dasar Pembelajaran Daring. Jurnal Golden Age, 04(2), 362–368. Trionanda, S. (2021). Analisis kesiapan dan pelaksanaan pembelajaran matematika jarak jauh berdasarkan profil TPACK di SD Katolik Tanjungpinang tahun ajaran 2020 / 2021. In Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika, 6, 69–76. Tsai, C.-C., & Chai, C. S. (2012). The ‘third’-order barrier for technology-integration instruction: Implications for teacher education. Australasian Journal of Educational Technology, 28(6). https://doi.org/10.14742/ajet.810 Wahyuni, F. T. (2019). Hubungan Antara Technological Pedagogical Content Knowledge (Tpack) Dengan Technology Integration Self Efficacy (Tise) Guru Matematika Di Madrasah Ibtidaiyah. Jurnal Pendidikan Matematika (Kudus), 2(2), 109–122. https://doi.org/10.21043/jpm.v2i2.6358 Wang, L., Ertmer, P. A., & Newby, T. J. (2014). Journal of Research on Technology in Education Increasing Preservice Teachers’ Self-Efficacy Beliefs for Technology Integration. Journal of Research on Technology in Education, 36(3), 37–41. https://doi.org/10.1080/15391523.2004.10782414 Warden, C. A., Yi-Shun, W., Stanworth, J. O., & Chen, J. F. (2020). Millennials’ technology readiness and self-efficacy in online classes. Innovations in Education and Teaching International, 00(00), 1–11. https://doi.org/10.1080/14703297.2020.1798269 Widarjono, A. (2015). Analisis Multivariat Terapan edisi kedua. UPP STIM YKPN. Wiresti, R. D. (2021). Analisis Dampak Work from Home pada Anak Usia Dini di Masa Pandemi Covid-19. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 5(1), 641653. https://doi.org/10.31004/obsesi.v5i1.563 Yildiz Durak, H. (2019). Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interactive Learning Environments, 1–27. https://doi.org/10.1080/10494820.2019.1619591 Yudha, F., Aziz, A., & Tohir, M. (2021). Pendampingan Siswa Terdampak Covid-19 Melalui Media Animasi Sebagai Inovasi Pembelajaran Online. JMM (Jurnal Masyarakat Mandiri), 5(3), 964–978. Yurdugül, H., & Demir, Ö. (2017). An investigation of Pre-service Teachers’ Readiness for E-learning at Undergraduate Level Teacher Training Programs: The Case of Hacettepe University. The Case of Hacettepe University. &nbsp

Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models

[EN] Access to the small bowel by means of an enteroscope is difficult, even using current devices such as single-balloon or double-balloon enteroscopes. Exploration time and patient discomfort are the main drawbacks. The prototype 'Endoworm' analysed in this paper is based on a pneumatic translation system that, gripping the bowel, enables the endoscope to move forward while the bowel slides back over its most proximal part. The grip capacity is related to the pressure inside the balloon, which depends on the insufflate volume of air. Different materials were used as in vitro and ex vivo models: rigid polymethyl methacrylate, flexible silicone, polyester urethane and ex vivo pig small bowel. On measuring the pressure-volume relationship, we found that it depended on the elastic properties of the lumen and that the frictional force depended on the air pressure inside the balloons and the lumen's elastic properties. In the presence of a lubricant, the grip on the simulated intestinal lumens was drastically reduced, as was the influence of the lumen's properties. This paper focuses on the Endoworm's ability to grip the bowel, which is crucial to achieving effective endoscope forward advance and bowel foldingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development FundTobella, J.; Pons-Beltrán, V.; Santonja, A.; Sánchez-Diaz, C.; Campillo Fernandez, AJ.; Vidaurre, A. (2020). Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 234(5):1-10. https://doi.org/10.1177/09544119209014141102345Iddan, G., Meron, G., Glukhovsky, A., & Swain, P. (2000). Wireless capsule endoscopy. Nature, 405(6785), 417-417. doi:10.1038/35013140Yamamoto, H., Sekine, Y., Sato, Y., Higashizawa, T., Miyata, T., Iino, S., … Sugano, K. (2001). Total enteroscopy with a nonsurgical steerable double-balloon method. Gastrointestinal Endoscopy, 53(2), 216-220. doi:10.1067/mge.2001.112181Arnott, I. D. R., & Lo, S. K. (2004). REVIEW: The Clinical Utility of Wireless Capsule Endoscopy. Digestive Diseases and Sciences, 49(6), 893-901. doi:10.1023/b:ddas.0000034545.58486.e6Hosoe, N., Takabayashi, K., Ogata, H., & Kanai, T. (2019). Capsule endoscopy for small‐intestinal disorders: Current status. Digestive Endoscopy, 31(5), 498-507. doi:10.1111/den.13346Fukumoto, A., Tanaka, S., Shishido, T., Takemura, Y., Oka, S., & Chayama, K. (2009). Comparison of detectability of small-bowel lesions between capsule endoscopy and double-balloon endoscopy for patients with suspected small-bowel disease. Gastrointestinal Endoscopy, 69(4), 857-865. doi:10.1016/j.gie.2008.06.007Akerman, P. A., Agrawal, D., Chen, W., Cantero, D., Avila, J., & Pangtay, J. (2009). Spiral enteroscopy: a novel method of enteroscopy by using the Endo-Ease Discovery SB overtube and a pediatric colonoscope. Gastrointestinal Endoscopy, 69(2), 327-332. doi:10.1016/j.gie.2008.07.042Moreels, T. G. (2017). Update in enteroscopy: New devices and new indications. Digestive Endoscopy, 30(2), 174-181. doi:10.1111/den.12920Pasha, S. F. (2012). Diagnostic yield of deep enteroscopy techniques for small-bowel bleeding and tumors. Techniques in Gastrointestinal Endoscopy, 14(2), 100-105. doi:10.1016/j.tgie.2012.02.001Lenz, P., & Domagk, D. (2012). Double- vs. single-balloon vs. spiral enteroscopy. Best Practice & Research Clinical Gastroenterology, 26(3), 303-313. doi:10.1016/j.bpg.2012.01.021Baniya, R., Upadhaya, S., Subedi, S. C., Khan, J., Sharma, P., Mohammed, T. S., … Jamil, L. H. (2017). Balloon enteroscopy versus spiral enteroscopy for small-bowel disorders: a systematic review and meta-analysis. Gastrointestinal Endoscopy, 86(6), 997-1005. doi:10.1016/j.gie.2017.06.015Menciassi, A., & Dario, P. (2003). Bio-inspired solutions for locomotion in the gastrointestinal tract: background and perspectives. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1811), 2287-2298. doi:10.1098/rsta.2003.1255Zarrouk, D., Sharf, I., & Shoham, M. (2011). Analysis of Wormlike Robotic Locomotion on Compliant Surfaces. IEEE Transactions on Biomedical Engineering, 58(2), 301-309. doi:10.1109/tbme.2010.2066274Poon, C. C. Y., Leung, B., Chan, C. K. W., Lau, J. Y. W., & Chiu, P. W. Y. (2015). Design of wormlike automated robotic endoscope: dynamic interaction between endoscopic balloon and surrounding tissues. Surgical Endoscopy, 30(2), 772-778. doi:10.1007/s00464-015-4224-8Kassim, I., Phee, L., Ng, W. S., Feng Gong, Dario, P., & Mosse, C. A. (2006). Locomotion techniques for robotic colonoscopy. IEEE Engineering in Medicine and Biology Magazine, 25(3), 49-56. doi:10.1109/memb.2006.1636351Kim, Y.-T., & Kim, D.-E. (2010). Novel Propelling Mechanisms Based on Frictional Interaction for Endoscope Robot. Tribology Transactions, 53(2), 203-211. doi:10.1080/10402000903125337Massalou, D., Masson, C., Foti, P., Afquir, S., Baqué, P., Berdah, S.-V., & Bège, T. (2016). Dynamic biomechanical characterization of colon tissue according to anatomical factors. Journal of Biomechanics, 49(16), 3861-3867. doi:10.1016/j.jbiomech.2016.10.023Egorov, V. I., Schastlivtsev, I. V., Prut, E. V., Baranov, A. O., & Turusov, R. A. (2002). Mechanical properties of the human gastrointestinal tract. Journal of Biomechanics, 35(10), 1417-1425. doi:10.1016/s0021-9290(02)00084-2Hoeg, H. D., Slatkin, A. B., Burdick, J. W., & Grundfest, W. S. (s. f.). Biomechanical modeling of the small intestine as required for the design and operation of a robotic endoscope. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065). doi:10.1109/robot.2000.844825Terry, B. S., Passernig, A. C., Hill, M. L., Schoen, J. A., & Rentschler, M. E. (2012). Small intestine mucosal adhesivity to in vivo capsule robot materials. Journal of the Mechanical Behavior of Biomedical Materials, 15, 24-32. doi:10.1016/j.jmbbm.2012.06.018Kim, J.-S., Sung, I.-H., Kim, Y.-T., Kwon, E.-Y., Kim, D.-E., & Jang, Y. H. (2006). Experimental investigation of frictional and viscoelastic properties of intestine for microendoscope application. Tribology Letters, 22(2), 143-149. doi:10.1007/s11249-006-9073-0Lyle, A. B., Luftig, J. T., & Rentschler, M. E. (2013). A tribological investigation of the small bowel lumen surface. Tribology International, 62, 171-176. doi:10.1016/j.triboint.2012.11.018De Simone, A., & Luongo, A. (2013). Nonlinear viscoelastic analysis of a cylindrical balloon squeezed between two rigid moving plates. International Journal of Solids and Structures, 50(14-15), 2213-2223. doi:10.1016/j.ijsolstr.2013.03.028Sliker, L. J., Ciuti, G., Rentschler, M. E., & Menciassi, A. (2016). Frictional resistance model for tissue-capsule endoscope sliding contact in the gastrointestinal tract. Tribology International, 102, 472-484. doi:10.1016/j.triboint.2016.06.003Zhang, C., Liu, H., & Li, H. (2014). Experimental investigation of intestinal frictional resistance in the starting process of the capsule robot. Tribology International, 70, 11-17. doi:10.1016/j.triboint.2013.09.019Zhang, C., Liu, H., & Li, H. (2013). Modeling of Frictional Resistance of a Capsule Robot Moving in the Intestine at a Constant Velocity. Tribology Letters, 53(1), 71-78. doi:10.1007/s11249-013-0244-5Zhang, C., Liu, H., Tan, R., & Li, H. (2012). Modeling of Velocity-dependent Frictional Resistance of a Capsule Robot Inside an Intestine. Tribology Letters, 47(2), 295-301. doi:10.1007/s11249-012-9980-1Woo, S. H., Kim, T. W., Mohy-Ud-Din, Z., Park, I. Y., & Cho, J.-H. (2011). Small intestinal model for electrically propelled capsule endoscopy. BioMedical Engineering OnLine, 10(1), 108. doi:10.1186/1475-925x-10-108Sliker, L. J., & Rentschler, M. E. (2012). The Design and Characterization of a Testing Platform for Quantitative Evaluation of Tread Performance on Multiple Biological Substrates. IEEE Transactions on Biomedical Engineering, 59(9), 2524-2530. doi:10.1109/tbme.2012.2205688Sánchez-Diaz, C., Senent-Cardona, E., Pons-Beltran, V., Santonja-Gimeno, A., & Vidaurre, A. (2018). Endoworm: A new semi-autonomous enteroscopy device. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 232(11), 1137-1143. doi:10.1177/0954411918806330Persson, B. N. J., & Spencer, N. D. (1999). Sliding Friction: Physical Principles and Applications. Physics Today, 52(1), 66-68. doi:10.1063/1.882557Gerson, L. B., Flodin, J. T., & Miyabayashi, K. (2008). Balloon-assisted enteroscopy: technology and troubleshooting. Gastrointestinal Endoscopy, 68(6), 1158-1167. doi:10.1016/j.gie.2008.08.012Glozman, D., Hassidov, N., Senesh, M., & Shoham, M. (2010). A Self-Propelled Inflatable Earthworm-Like Endoscope Actuated by Single Supply Line. IEEE Transactions on Biomedical Engineering, 57(6), 1264-1272. doi:10.1109/tbme.2010.2040617Baek, N.-K., Sung, I.-H., & Kim, D.-E. (2004). Frictional resistance characteristics of a capsule inside the intestine for microendoscope design. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 218(3), 193-201. doi:10.1243/095441104323118914Kwon, J., Cheung, E., Park, S., & Sitti, M. (2006). Friction enhancement via micro-patterned wet elastomer adhesives on small intestinal surfaces. Biomedical Materials, 1(4), 216-220. doi:10.1088/1748-6041/1/4/007Kim, B., Lee, S., Park, J. H., & Park, J.-O. (2005). Design and Fabrication of a Locomotive Mechanism for Capsule-Type Endoscopes Using Shape Memory Alloys (SMAs). IEEE/ASME Transactions on Mechatronics, 10(1), 77-86. doi:10.1109/tmech.2004.842222Terry, B. S., Lyle, A. B., Schoen, J. A., & Rentschler, M. E. (2011). Preliminary Mechanical Characterization of the Small Bowel for In Vivo Robotic Mobility. Journal of Biomechanical Engineering, 133(9). doi:10.1115/1.400516

Relationship between roll-off occurrence and spatial distribution of dehydrated tissue during RF ablation with cooled electrodes

Purpose: To study the relationship between roll-off (sudden increase in impedance) and spatial distribution of dehydrated tissue during RF ablation using a cooled electrode (temperatures around 100°C). Methods: We used a double approach: (1) theoretical modelling based on the finite element method, and (2) 20 ablations using an experimental study on ex vivo excised bovine liver in which we measured impedance progress and temperature at three points close to the electrode surface: 0.5 (T1), 1.5 (T2) and 2.5 (T3) mm from the tip. T2 was located exactly at the centre of the 30 mm long electrode. Results: Temperatures at T1 and T3 quickly rose to 100°C (at ≈20 and 40 s, respectively), while at the rise at T2 was somewhat slower, stabilized around 50 s and reached a maximum value of 99°C at about 60 s. Impedance reached a minimum of 65 Ω (plateau), began increasing at 50 s and continued rising throughout the procedure, reaching a value equal to the initial value at 70 s. Likewise, computed impedance dropped to ≈73 Ω (plateau), began increasing at 50 s and reached an impedance value equal to the initial value at ≈78 s, which approximately coincided with the time when the entire zone surrounding the electrode was within the 100°C isotherm. Conclusion: There is a close relationship between the moment at which roll-off occurs and the time when the entire electrode is completely encircled by the dehydrated tissue. The mid-electrode zone is the last in which tissue desiccation occurs.This work received financial support from the Spanish Plan Nacional de I+D+I del Ministerio de Ciencia e Innovacion, grant no. TEC2008-01369/TEC and FEDER Project MTM2010-14909. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain. The authors alone are responsible for the content and writing of the paper.Trujillo Guillen, M.; Alba Martínez, J.; Berjano, E. (2012). Relationship between roll-off occurrence and spatial distribution of dehydrated tissue during RF ablation with cooled electrodes. International Journal of Hyperthermia. 28(1):62-68. https://doi.org/10.3109/02656736.2011.631076S6268281Poon, R. T.-P., Fan, S.-T., Tsang, F. H.-F., & Wong, J. (2002). Locoregional Therapies for Hepatocellular Carcinoma: A Critical Review From the Surgeon’s Perspective. Annals of Surgery, 235(4), 466-486. doi:10.1097/00000658-200204000-00004Solbiati, L., Livraghi, T., Goldberg, S. N., Ierace, T., Meloni, F., Dellanoce, M., … Gazelle, G. S. (2001). Percutaneous Radio-frequency Ablation of Hepatic Metastases from Colorectal Cancer: Long-term Results in 117 Patients. Radiology, 221(1), 159-166. doi:10.1148/radiol.2211001624Ahmed, M., Brace, C. L., Lee, F. T., & Goldberg, S. N. (2011). Principles of and Advances in Percutaneous Ablation. Radiology, 258(2), 351-369. doi:10.1148/radiol.10081634Pereira, P. L., Trübenbach, J., Schenk, M., Subke, J., Kroeber, S., Schaefer, I., … Claussen, C. D. (2004). Radiofrequency Ablation: In Vivo Comparison of Four Commercially Available Devices in Pig Livers. Radiology, 232(2), 482-490. doi:10.1148/radiol.2322030184Li, X., Zhang, L., Fan, W., Zhao, M., Wang, L., Tang, T., … Liu, Y. (2011). Comparison of microwave ablation and multipolar radiofrequency ablation, both using a pair of internally cooled interstitial applicators: Results inex vivoporcine livers. International Journal of Hyperthermia, 27(3), 240-248. doi:10.3109/02656736.2010.536967Burdío, F., Tobajas, P., Quesada-Diez, R., Berjano, E., Navarro, A., Poves, I., & Grande, L. (2011). Distant Infusion of Saline May Enlarge Coagulation Volume During Radiofrequency Ablation of Liver Tissue Using Cool-tip Electrodes Without Impairing Predictability. American Journal of Roentgenology, 196(6), W837-W843. doi:10.2214/ajr.10.5202Burdío, F., Navarro, A., Berjano, E. J., Burdío, J. M., Gonzalez, A., Güemes, A., … Grande, L. (2008). Radiofrequency hepatic ablation with internally cooled electrodes and hybrid applicators with distant saline infusion using an in vivo porcine model. European Journal of Surgical Oncology (EJSO), 34(7), 822-830. doi:10.1016/j.ejso.2007.09.029Burdío, F., Berjano, E. J., Navarro, A., Burdío, J. M., Güemes, A., Grande, L., … de Gregorio, M. A. (2007). RF tumor ablation with internally cooled electrodes and saline infusion: what is the optimal location of the saline infusion? BioMedical Engineering OnLine, 6(1), 30. doi:10.1186/1475-925x-6-30Haemmerich D, Mathematical modeling of impedance controlled radiofrequency tumor ablation and ex-vivo validation. Buenos Aires, Argentina: Proceedings of the 32nd Annual International Conference of the IEEE EMBS, 2010, pp. 1605–1608Arata, M. A., Nisenbaum, H. L., Clark, T. W. I., & Soulen, M. C. (2001). Percutaneous Radiofrequency Ablation of Liver Tumors with the LeVeen Probe: Is Roll-off Predictive of Response? Journal of Vascular and Interventional Radiology, 12(4), 455-458. doi:10.1016/s1051-0443(07)61884-3Haemmerich, D., Chachati, L., Wright, A. S., Mahvi, D. M., Lee, F. T., & Webster, J. G. (2003). Hepatic radiofrequency ablation with internally cooled probes: effect of coolant temperature on lesion size. IEEE Transactions on Biomedical Engineering, 50(4), 493-500. doi:10.1109/tbme.2003.809488McGahan, J. P., Loh, S., Boschini, F. J., Paoli, E. E., Brock, J. M., Monsky, W. L., & Li, C.-S. (2010). Maximizing Parameters for Tissue Ablation by Using an Internally Cooled Electrode. Radiology, 256(2), 397-405. doi:10.1148/radiol.09090662Berjano, E. J., Burdío, F., Navarro, A. C., Burdío, J. M., Güemes, A., Aldana, O., … Gregorio, M. A. de. (2006). Improved perfusion system for bipolar radiofrequency ablation of liver: preliminary findings from a computer modeling study. Physiological Measurement, 27(10), N55-N66. doi:10.1088/0967-3334/27/10/n03Pätz, T., Kröger, T., & Preusser, T. (2009). Simulation of Radiofrequency Ablation Including Water Evaporation. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 1287-1290. doi:10.1007/978-3-642-03882-2_341Berjano, E. J. (2006). BioMedical Engineering OnLine, 5(1), 24. doi:10.1186/1475-925x-5-24Jo, B., & Aksan, A. (2010). Prediction of the extent of thermal damage in the cornea during conductive keratoplasty. Journal of Thermal Biology, 35(4), 167-174. doi:10.1016/j.jtherbio.2010.02.004Pearce, J., Panescu, D., & Thomsen, S. (2005). Simulation of diopter changes in radio frequency conductive keratoplasty in the cornea. Modelling in Medicine and Biology VI. doi:10.2495/bio050451Abraham, J. P., & Sparrow, E. M. (2007). A thermal-ablation bioheat model including liquid-to-vapor phase change, pressure- and necrosis-dependent perfusion, and moisture-dependent properties. International Journal of Heat and Mass Transfer, 50(13-14), 2537-2544. doi:10.1016/j.ijheatmasstransfer.2006.11.04

Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model

Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. These cross-media features then classified using logistic regression to predict sentiment values which categorized into three classes: positive, negative and neutral. Simulation results showed that the combination of unigram text features and 56-length images features achieves the highest accuracy. The accuracy achieved is 87.2%. Keywords : Instagram, sentiment analysis, Cross-media Bag-of-Words Model (CBM), logistic regression, classification Bibliography [1] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in Proceedings of the 21st ACM International Conference on Multimedia, ser. MM '13. New York, NY, USA: ACM, 2013, pp. 223–232. [2] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871– 1874, Jun. 2008. [3] E. Ferrara, R. Interdonato, and A. Tagarelli, “Online popularity and topical interests through the lens of instagram,” in Proceedings of the 25th ACM Conference on Hypertext and Social Media, ser. HT '14. New York, NY, USA: ACM, 2014, pp. 24–34. [4] N. Gunawardena, J. Plumb, N. Xiao, and H. Zhang, “Instagram hashtag sentiment analysis,” in University of Utah CS530/CS630 Conference of Machine Learning 2013. Universiti of Utah CS530/CS630 Conference of Machine Learning 2013, 2013. [5] J. Han, Data Mining: Concepts and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005. [6] M. Hu and B. Liu, “Mining and summarizing customer reviews,” inProceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2004, pp. 168–177.[7] Y. Hu, L. Manikonda, and S. Kambhampati, “What we instagram: A first analysis of instagram photo content and user types,” International AAAI Conference on Weblogs and Social Media, 2014. [8] L. S. Huey and R. Yazdanifard, “How instagram can be used as a tool in social networking marketing,” Help College of Art and Technology Malaysia, Tech. Rep., 2014. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition(2nd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2009. [10] S. G. K and S. Joseph, “Text classification by augmenting bag of words ( bow ) representation with co-occurrence feature,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, pp. 34–38, 1 2014. [11] B. B. Kachru, The Alchemy of English: The Spread, Functions and Models of Non-native Englishes. Champaign: University of Illinois Press, 1990. [12] S. S. Keerthi and C.-J. Lin, “Asymptotic behaviors of support vector machines with gaussian kernel,” Neural Comput., vol. 15, no. 7, pp. 1667–1689, Jul. 2003. [13] M. Koppel and J. Schler, “The importance of neutral examples for learning sentiment,” in In Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations, 2005.[14] A. Kowcika, A. Gupta, K. Sondhi, N. Shivhre, and R. Kumar, “Sentiment analysis for social media,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 7 2013. [15] F.-F. Li and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02, ser. CVPR '05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 524–531. [16] B. Liu, Sentiment Analysis and Opinion Mining. Morgan and Claypool Publisher, 2012. [17] W.-Y. Ma and K.-J. Chen, “A bottom-up merging algorithm for chinese unknown word extraction,” in Proceedings of the Second SIGHAN Workshop on Chinese Language Processing - Volume 17, ser. SIGHAN '03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 31–38. [18] Z. McCune, “Consumer production in social media networks: A case study of the instagram iphone app,” Ph.D. dissertation, Dr. John Thompson, 2011. [19] W. Medhata, A. Hassanb, and H. Korashyb, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, 2014. [20] L.-P. Morency, R. Mihalcea, and P. Doshi, “Toward multimodal sentiment analysis: Harvesting opinion from the web,” in International Conference on Multimodal Interface, 2011. [21] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), N. C. C. Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias, Eds. Valletta, Malta: European Language Resources Association (ELRA), may 2010.[22] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundation and Trends in Information Retrieval, vol. 2, no. 1-2, p. 4, 2008. [23] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, ser. EMNLP '02. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002, pp. 79–86. [24] C.-Y. J. Peng, K. L. Lee, and G. M. Ingersol, “An introduction to logistic regression analysis and reporting,” The Journal of Educational Research, vol. 96, no. 1, September/October 2002. [25] V. V. Piyush Bansal, Romil Bansal, “Towards deep semantic analysis of hashtags,” 37th European Conference on Information Retrieval, 2015. [26] R. Plutchik, Emotion: A Psycho-evolutionary Synthesis. Harper and Row, 1980. [27] S. Poria, A. Hussain, and E. Cambria, “Beyond text based sentiment analysis: Towards multi-modal systems,” University of Stirling, Stirling FK9 4LA, UK, Tech. Rep., 2013. [Online]. Available: http://www.cs.stir.ac.uk/~spo/publication/resources/cogcomp.pdf [28] E. Praseyto, Data Mining Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.[29] A. Qazi, R. G. Raj, M. Tahir, E. Cambria, and K. B. S. Syed, “Enhancing business intelligence by means of suggestive reviews,” The Scientific World Journal, vol. 2014, June 2014. [30] R. Schapire, “Machine learning algorithms for classification,” Princeton University, Tech. Rep. [31] S. Siersdorfer, E. Minack, F. Deng, and J. Hare, “Analyzing and predicting sentiment of images on the social web,” in Proceedings of the International Conference on Multimedia, ser. MM '10. New York, NY, USA: ACM, 2010, pp. 715–718. [32] T. H. Silva, P. O. S. V. de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro, “A comparison of foursquare and instagram to the study of city dynamics and urban social behavior,” in Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, ser. UrbComp '13. New York, NY, USA: ACM, 2013, pp. 4:1–4:8. [33] P. N. Stuart Russell, Artificial Intelligence A Modern Approach, M. Hirsch, Ed. New Jersey: Pearson Education, 2010. [34] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267– 307, Jun. 2011. [35] C.-F. Tsai, “Bag-of-words representation in image annotation: A review,” ISRN Artificial Intelligence, p. 19, 2012. [36] A. J. Viera and J. M. Garrett, “Understanding interobserver agreement: The kappa statistic,” Family Medicine, vol. 37, no. 5, pp. 360–363, May 2005. [37] M. Wang, D. Cao, L. Li, S. Li, and R. Ji, “Microblog sentiment analysis based on cross-media bag-of-words model,” in Proceedings of International Conference on Internet Multimedia Computing and Service, ser. ICIMCS '14. New York, NY, USA: ACM, 2014, pp. 76:76–76:80. [38] A. Westerski, “Sentiment analysis: Introduction and the state of the art overview,” Universidad Politecnica de Madrid, Spain, Tech. Rep., 2009. [39] F. Yu, L. Cao, R. Feris, J. Smith, and S.-F. Chang, “Designing category-level attributes for discriminative visual recognition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [40] L. Yu and H. Liu, “Efficiently handling feature redundancy in high-dimensional data,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '03. New York, NY, USA: ACM, 2003, pp. 685–690. [41] J. Yuan, S. Mcdonough, Q. You, and J. Luo, “Sentribute: Image sentiment analysis from a mid-level perspective,” in Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, ser. WISDOM '13. New York, NY, USA: ACM, 2013, pp. 10:1– 10:8