Digitalization and entrepreneurship intention

Abstract. This study aims to identify the link between digitalization and entrepreneurial intentions based on Information and Communication Technology. It is essential to assess the impact of relational support and educational support on entrepreneurial intentions. Sometimes, the relational structure positively affects the improvements of the start-up business. Moreover, the demographic factors such as gender, age, relationship status and residence give significant support to start a business. In addition to that, structural support, such as financial and business opportunities tend to lead somebody’s ideas to be an entrepreneur. Identifying the factors that affect the entrepreneurial intentions needs to be determined to give necessary support, motivations and financial aids, even in university education. It helps entrepreneurs eliminate drawbacks and enhance their performance since it is the trending and economical way of being an entrepreneur. However, the digitalization on entrepreneurial intentions is not evaluated at the university level and furthermore; it has not been applied structural equation modelling to identify the factors that affect entrepreneurial intentions and digitalization in university education. The data which was gathered via survey was dissected contrasting with one another variables and the likenesses product taken up and stuck up for data mining. Finally, identified major points were assessed and quantitatively investigated for finding an answer for the distinguished effects. Our study has found that the three independent variables (Educational structure, Relational Structure, Structural Structure) are significantly associated with entrepreneurial intention. However, six different hidden factors were identified accordingly these three variables, which were not achieved in previous studies. Hence, it may fill the gap between previous studies in future. Finally, this study concluded an excellent platform statistically to realize the mediating effect of behavioural control and personal attitudes on entrepreneurial intentions

Evaluation of machine learning techniques for intrusion detection in software defined networking

Abstract. The widespread growth of the Internet paved the way for the need of a new network architecture which was filled by Software Defined Networking (SDN). SDN separated the control and data planes to overcome the challenges that came along with the rapid growth and complexity of the network architecture. However, centralizing the new architecture also introduced new security challenges and created the demand for stronger security measures. The focus is on the Intrusion Detection System (IDS) for a Distributed Denial of Service (DDoS) attack which is a serious threat to the network system. There are several ways of detecting an attack and with the rapid growth of machine learning (ML) and artificial intelligence, the study evaluates several ML algorithms for detecting DDoS attacks on the system. Several factors have an effect on the performance of ML based IDS in SDN. Feature selection, training dataset, and implementation of the classifying models are some of the important factors. The balance between usage of resources and the performance of the implemented model is important. The model implemented in the thesis uses a dataset created from the traffic flow within the system and models being used are Support Vector Machine (SVM), Naive-Bayes, Decision Tree and Logistic Regression. The accuracy of the models has been over 95% apart from Logistic Regression which has 90% accuracy. The ML based algorithm has been more accurate than the non-ML based algorithm. It learns from different features of the traffic flow to differentiate between normal traffic and attack traffic. Most of the previously implemented ML based IDS are based on public datasets. Using a dataset created from the flow of the experimental environment allows training of the model from a real-time dataset. However, the experiment only detects the traffic and does not take any action. However, these promising results can be used for further development of the model

Synthesis, Characterization, and Performance of TiO2-N as Filler in Polyethersulfone Membranes for Laundry Waste Treatment

Sintesis TiO2-N telah dilakukan dengan metode sol gel. Pada hasil karakterisasi XRD doping TiO2 tidak dapat menghancurkan struktur anatase karena difraktogram TiO2-N mewakili puncak yang serupa dengan TiO2. Intensitas puncak spektra FTIR yang kuat berasal dari gugus hidroksil dalam N-TiO2, dibandingkan dengan TiO2 tanpa doping. Dari hasil pengolahan data hasil karakterisasi UV-DRS didapatkan nilai energi bandgap TiO2 3,33 eV, sedangkan pada fotokatalis TiO2-N energi band gap yang dihasilkan yaitu 3,08 eV. Hasil BET menyebutkan TiO2 memiliki luas permukaan yang lebih tinggi dibandingkan dengan TiO2-N. Pada penelitian ini, hasil nilai fluks air murni dan fluks limbah laundry yang paling tinggi terdapat pada membran PES/PEG/TiO2-N yaitu 116,91 L/m2jam pada 2 menit pertama untuk fluks air murni dan 98,636 L/m2jam pada 2 menit pertama untuk fluks limbah dan rejeksi yang dihasilkan mencapai 84,328% untuk nilai penurunan COD dan 82,75% untuk nilai penurunan BOD sehingga penambahan TiO2-N pada membran dapat meningkatkan kinerja membran saat ultrafiltrasi

FITUR-FITUR SEDIMEN ENDAPAN TURBIDIT SEPENJANG SUNGAI CILUTUNG, BANTARUJEG, JAWA BARAT

Penelitian ini dilakukan di sepanjang Sungai Cilutung yang termasuk dalam Formasi Bantarujeg, formasi ini merupakan formasi yang terkenal karena banyak memiliki fitur-fitur sedimen, seperti ditemukannya endapan turbidit dan struktur-struktur sedimen yang bervariasi yang sangat menarik untuk diteliti. Adapun tujuan penelitian ini adalah untuk menggali informasi secara detail mengani fitur-fitur sedimen yang ada dan mengkaitkannya dengan geologi regional untuk menarik kesimpulan mengenai karakterisik arus dan lingkungan pengendapannya. Metode yang dilakukan adalah metode tape & compass traverse dengan menggunakan kompas geologi, pita ukur, dan GPS, selain itu, digunakan juga metode studio untuk membuat gambaran vertikal log litologi dari hasil pengukuran menggunakan softwere SedLog 3.1 dan Corel Draw X6. Dari hasil pengukuran measured section (MS) sepanjang 416,2 m didapat ketebalan 155,69 m, sepanjang daerah pengukuran tersebut satuan batuan dibagi menjadi dua, yaitu satuan batuserpih (lintasan 1-8) dan satuan batupasir (lintasan 9-11). Terdapat tiga unit Bouma sekuen tersingkap jelas di lintasan 10-11, unit Bouma sekuen 1 terletak di bawah yang tersusun dari divisi Ta-b dengan keadaan terbalik (reverse grading), unit Bouma sekuen yang kedua terletak di tengah yang tersusun dari divisi Ta-c, dan unit Bouma sekuen yang terakhir terlatak paling atas yang tersusun dari divisi Ta-b, dari ketiga unit Bouma sekuen ini bisa menjadi indikasi bahwa setidaknya terjadi tiga rangkaian arus yang terjadi dari lintasan 10-11. Selain itu, ditemukan struktur-struktur sedimentasi seperti pararel laminasi, cross laminasi, normal grading, reverse grading, lenticular, dan slump, dengan memperhatikan struktur-struktur tersebut dan adanya sifat karbonatan dari semua lapisan sepanjang lintasan disimpulkan bahwa daerah penelitian diendapkan di daerah laut

Evaluation of machine learning techniques for security in SDN

Abstract Software Defined Networking (SDN) has emerged as the most viable programmable network architecture to solve many challenges in legacy networks. SDN separates the network control plane from the data forwarding plane and logically centralizes the network control plane. The logically centralized control improves network management through global visibility of the network state. However, centralized control opens doors to security challenges. The SDN control platforms became the most attractive venues for Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Due to the success and inevitable benefits of Machine Learning (ML) in fingerprinting security vulnerabilities, this article proposes and evaluates ML techniques to counter DoS and DDoS attacks in SDN. The ML techniques are evaluated in a practical setup where the SDN controller is exposed to DDoS attacks to draw important conclusions for ML-based security of future communication networks