ASSISTANCE AND MAKING VCO (VIRGIN COCONUT OIL)

The manufacture of Virgin Coconut Oil and LPPM and PM are in line with Law Number 12 of 2012 concerning Higher Education Article 45, which emphasizes that research and community service in higher education is directed at developing science and technology, as well as improving people's welfare and national competitiveness. With the VCO making program, LPPM and PM aim to avoid and fill in activities in an effort to isolate themselves, the only thing is to socialize and teach the people of Gampong Matang Neuheun, Nurussalam District, East Aceh Regency in the framework of making virgin coconut oil (VCO) as an alternative activity , so that people are not lonely and bored and traveling, and indirectly can prevent the spread of Covid-19. The service method, namely; a normative approach, empirical and using primary and secondary data, and processed descriptively qualitatively. As for the manufacturing steps; preparation of practicum tools, preparation of raw materials / old heads, splitting coconut, grating coconut, squeezing coconut into coconut milk, filtering coconut milk, depositing coconut milk in gallons of RO, , storing coconut milk in bottles / jars for 12 hours until it becomes VCO with levels 100% pure. the benefits of VCO, for health and beauty and to cure various diseases; stinging and moist as immunity naturally. The point is that in 2021, all PKM Teams will be more active and creative in the field of research and service as well as being participatory with village residents and in line with the mission of the Tridarma of Higher Education.Pembuatan Virgin Coconut Oil dan LPPM dan PM, memang sejalan dengan  Undang-undang Nomor 12 Tahun 2012 tentang Pendidikan Tinggi Pasal 45 yaitu menegaskan bahwa penelitian dan pengabdian di perguruan tinggi diarahkan untuk mengembangkan ilmu pengetahuan dan teknologi, serta meningkatkan kesejahteraan masyarakat dan daya saing bangsa. Dengan program pembuatan VCO, LPPM dan PM tujuannya untuk menghindari dan mengisi kegiatan dalam usaha mengisolasi diri satu-satunya adalah mensosialisasi dan mengajarkan kepada masyarakat Gampong Matang Neuheun, Kecamatan Nurussalam, Kabupaten Aceh Timur dalam rangka pembuat minyak kelapa murni (VCO) sebagai suatu kegiatan  alternatif, supaya masyarakat menjadi tidak sepi dan bosan serta berpergian, dan secara tidak langsung dapat mencegah penyebaran Covid-19 tersebut. Metode pengabdiannya yaitu; pendekatan normatif, empiris dan menggunakan data-data primer dan skunder, serta diolah secara deskriptif kualitatif. Adapun langkah pembuatannya; persiapan alat praktikum, persiapan bahan baku/kepala tua, pembelahan kelapa, pemarutan kelapa, pemerasan kelapa menjadi santan, penyaringan santan, pengendapan santan dalam galon RO, pengadukan satan dengan mixer, penyimpanan santan dalam botol/toples selama 12 jam sampai menjadi VCO dengan kadar murni 100%. manfaat VCO, untuk kesehatan dan kecantikan dan obat penyembuhan berbagai penyakit; perih dan lembab sebagai kekebalan tubuh secara alami. Saranya pada 2021, semua Tim PKM lebih giat dan kreatif dibidang penelitian dan pengabdian serta partisipatif dengan warga gampong dan sejalan dengan misi Tridarma Perguruan Tinggi

The Development and Evaluation of Experience-Based Factory Model for Software Development Process

Knowledge, and experiences in software development have been accumulated over time throughout the project lifecycle. Previous studies have shown that the management of knowledge and experiences in software development has always been an issue. Therefore, the knowledge transfer and information flow are inefficient, misinterpretation, and inconsistencies always occur between individuals or teams, and the organization fails to learn from past projects. It is understood that efficient knowledge and experience management for software development organizations is crucial for the purpose of sharing and future reuse. This paper discusses the prototype development for a proposed model, which is based on the experience factory approach, to manage knowledge and experiences for the software development process. Discussions include the system functionalities and design, infrastructure requirements, and implementation approach. The efficiency and effectiveness of the prototype are evaluated as survey research based on Jennex & Olfman knowledge management success model. Rasch analysis is used for data reliability and validity. Results show positive feedback on the model’s efficiency and effectiveness. Additionally, as agreed by most respondents, the top three of the model contributions are: to encourage learning organization, to prevent knowledge loss and to aid in decision making

Architectural design of experience based factory model for software development process in cloud computing: integration with workflow and multi-agent system

A model which is based on experience factory approach has been proposed earlier, called EBF-SD, to overcome the limitations of experience management in software development domain. An application prototype, which is then called SDeX, is developed based on the proposed model. The study on correlation analysis indicates that automation do have positive relationship with other components: knowledge management, cloud, collaboration and portal. This paper further discusses the high level prototype development with the emphasis on the architectural design. Automation features are incorporated in the design in which workflow system and intelligent agents are integrated, and the facilitation of cloud environment is empowered to further support the automation

Regression analysis on experience based factory model for software development process

Software organizations are still struggling to reuse the best of their knowledge and experiences in future projects. Often, when there are changes on employee re-assignment, a lot of time and effort are spent for knowledge transfer activities. This however may not assure that all knowledge and experiences are well transferred and shared; some could be missing or misplaced. In this study, a model has been proposed for managing knowledge and experiences based on experience factory approach to provide a more efficient and effective experience management for software development community. Experience Factory is an infrastructure that aims for reuse of products, processes and experiences gained during a system life cycle. A set of components have been identified as the predictors of the model which eventually forms the two main organizations: project organization and experience factory organization. This study further has gone through a correlational survey research to verify the relationship between the identified predictors towards the experience factory goals. Reliability analysis has been conducted to validate the measures, while correlation and regression analyses have been carried out to examine the relationship between the constructs and the goals. Results reveal that reliability of the model is high and construct validity is satisfactory. Experience factory organization is found having more positively significant towards experience goals as compared to project organization; however, there is no significant impact towards the model due to inexistence of causal relation

Towards developing lessons learned and experience based factory in software development

Software development is a highly knowledge intensive activity. During the software life cycle, knowledge and experiences are accumulated over time. One of the main knowledge sources in software development is lessons learned. Even though lessons learned have been a common practice for process improvement, however, there are arguments that lessons learned are not used effectively and organizations continuously fail to learn from past projects. In this paper, we propose utilizing lessons learned by transforming it into an experience base incorporated with software engineering life cycle for the purpose of sharing and future reuse. An initial model is formulated based on literature analysis and a preliminary study. The goal of the study is to assess the experts' perception on the model formulation in terms of its importance in each software development phase, and which knowledge element is more valuable as future reuse. Additionally, results from the study show that such model can bring positive impact to individuals as well as organizations

Towards developing collaborative experience based factory model for software development process in cloud computing environment

Knowledge and experience are important assets for software organizations. In today’s global software development trends, development teams are no longer located in single premise; they are spreading across national and geographic boundaries. As a software project progresses, more and more activities are involved which results with the accumulation of knowledge and experiences. Maintaining and reusing of past experiences are vital; and it is even more crucial for distributed teams. In order to sustain in today’s competitive advantages, organizations should prepare a well collaborative solution for managing software development knowledge and experiences to maximize sharing and future reuse. Numerous attempts have been invested by researchers to overcome the issues on knowledge management in software development; however, the emphasis on the actual experiences collected throughout the development phases is limited. Furthermore, there are not many solutions offering comprehensive collaborative solution for managing software development experiences. In this paper, we propose a model for managing software development experiences including its tacit and explicit knowledge based on experience factory approach. The model is adapted for cloud computing environment with the goal to provide efficient and effective collaborative solution for knowledge access, sharing and reuse by capitalizing the cloud’s resources and infrastructure. A systematic literature review has been conducted to investigate the current issues of knowledge management in software development and to analyze available approaches and solutions. The findings are quantitatively and qualitatively evaluated to support the model formulation

PREDICTION OF LIFE EXPECTANCY FOR ASIAN POPULATION USING MACHINE LEARNING ALGORITHMS

Predicting life expectancy has become more important nowadays as life has become more vulnerable due to many factors, including social, economic, environmental, education, lifestyle, and health condition. A lot of studies on life expectancy have been carried out. However, studies focusing on the Asian population are limited. This study presents machine learning algorithms for life expectancy based on the Asian population dataset. Comparisons are made between tree classifier models, namely, J48, Random Tree, and Random Forest. Cross validations with 10 and 20 folds are used. Results show that the highest accuracy is obtained with Random Forest with 84% accuracy with 10-fold cross-validation. This study further identifies the most significant factors that influence life expectancy prediction, which includes socioeconomic factors and educational status, health conditions and infectious disease

MATHVISION PROTOTYPE USING PREDICTIVE ANALYTICS

Malaysia is currently going towards Industrial Revolution (IR) 4.0 which makes Science, Technology, Engineering and Mathematics (STEM) subjects become more crucial. IR 4.0 covers a lot of aspects especially in digital transformation in manufacturing, and this certainly requires strong mathematical knowledge. To achieve this goal, students need to have a good foundation in Mathematics subject. However, due to the increased number of students nowadays, teachers are facing challenges to track students’ progress efficiently. In this study, a predictive model has been developed that aims to assist Mathematics teachers in monitoring their students. The prototype, called MathVision, can track students’ progress effectively in each topic and subtopic of Mathematics subject and predict the grades that students will obtain based on the history result. A total of 207 instances was collected among Form 5 students from a government school to represent the samples for the modelling task. The Multiclass Decision Forest algorithm appeared to be the best predictive model with 95.16% accuracy, as compared to Boosted Decision Tree, Logistic Regression, and Neural Network. Flutter framework and Firebase services were used for front-end and back-end system respectively, and Microsoft Power BI was used for data visualization. The result of prototype testing showed that MathVision could predict students’ grade for Quiz 2 based on Quiz 1 performance. MathVision is also capable for real-time prediction that guarantees an immediate response time which can help Mathematics teachers to support students who need further assistance in this subject based on the prediction given. For MathVision’s future improvement, the number of instances needs to increase, and more significant variables need to be added

INFRASTRUCTURE REQUIREMENTS FOR EXPERIENCE BASED FACTORY MODEL IN SOFTWARE DEVELOPMENT PROCESS IN A COLLABORATIVE ENVIRONMENT

This paper discuss the infrastructure requirement of the model named Experienced Based Facto-ry Model for Software Development Process (EBF-SD) in order to ensure the implementation of the model will be able support collaborative environment effectively and efficiently

Experience based factory model for software development process: item construct validation on questionnaire design

Software development is a highly intensive knowledge process. Information, data, knowledge and experiences are accumulated daily and it is crucial that they are managed appropriately for the purpose of sharing and future reuse. Today, software development has spread across geographical boundaries; therefore, the need for knowledge retention has risen, and the need for collaboration among the community of practice has been further in demand. Based on this motivation, we posit a model of managing the experiences of software development process by using experience based factory approach. An initial conceptual model has been constructed based on relevant theoretical frameworks which include knowledge management, experience factory, software development process, community of practice, technology and infrastructure, and influences from managerial and organizational levels. Based on the literature review, questionnaire items have been designed to form the identified latent constructs. A pilot survey has been conducted to verify the questionnaire items and the results are tested against Rasch measurement analysis. By using Rash logit measures, the items quality is ensured. Findings indicate that the item fitness is good (0.73), outfit and infit mean square values are very much close to 1, and Z-standardized value is within the expected range. Unidimensionality shows that there is no visible secondary dimension even though the scale category structure is rather high. Nonetheless, there are several misfitting items which are further calibrated and revised for future study. The initial model will be the basis of the future model development