SATESI: Jurnal Sains Teknologi dan Sistem Informasi

Perancangan dan Impelementasi Sistem Informasi Kepegawaian pada PT. Rezeki Bersama Energi

Defriel Lazuardi Mahendra — 2026-04-15

The rapid development of information technology requires organizations to manage human resource data effectively and efficiently in order to support organizational performance. PT Rezeki Bersama Energi currently manages its personnel administration manually using Microsoft Word and Microsoft Excel, which leads to several issues, including delays in administrative processes, a high risk of data entry errors, data duplication, and difficulties in retrieving and presenting personnel information. This study aims to design and implement a web-based Human Resource Information System to support integrated and structured personnel data management. The system development employs an iterative method, while system analysis is conducted using the PIECES framework to identify problems in the existing system. The developed system provides features for employee data management, attendance, leave requests, overtime, and employee rewards, with three levels of access rights: administrator, employee, and management. The results indicate that the implemented Human Resource Information System improves the efficiency and effectiveness of personnel administrative processes, minimizes data recording errors, and assists management in monitoring and making informed decisions related to human resource management in a more accurate and timely manner.

Analisis Sosio-Teknikal Disrupsi AI: Transformasi Arsitektur Pembelajaran dari Digital Assistance Menuju Human-Machine Co-Evolution di Pendidikan Vokasi

Dita Rahmawati — 2026-04-15

This study aims to analyze the impact of artificial intelligence (AI) disruption on the shift in learning paradigms in Indonesian higher education, particularly at Prasetiya Mandiri Polytechnic PSDKU Palembang. The global phenomenon demonstrates that AI has become a disruptive force, serving not only as an administrative tool but also as a collaborative partner in the teaching and learning process. This study employs a descriptive, qualitative approach, drawing on phenomenological methods, to understand the experiences of lecturers and students in their interactions with AI. Data was collected through in-depth interviews, observations of digital activities, and documentation studies from UNESCO, OECD, and Ministry of Education and Culture reports. The data analysis was carried out thematically, focusing on four main themes: digital assistance, changing learning patterns, human–AI collaboration, and ethical challenges in education. The results indicate that AI integration has enhanced learning efficiency, increased student participation, and fostered independent learning. However, negative impacts were also observed, including technology dependence, a decline in critical thinking skills, and the emergence of ethical dilemmas related to plagiarism and algorithmic bias. This research emphasizes that AI should be placed not as a substitute for educators, but as a collaborative partner that enriches the humanistic learning process. It is necessary to strengthen digital literacy, AI ethics, and an adaptive curriculum grounded in human-machine synergy so that educational transformation in the era of technological disruption can occur in a sustainable and equitable manner.

Analisis Faktor-Faktor Penyebab Depresi Mahasiswa di Indonesia Menggunakan Metode Regresi Logistik

Kiki Mustaqim — 2026-04-15

Depression is one of the most common mental health disorders experienced by university students and can have a serious impact on their psychological state, academic performance and social interactions. Academic pressure, financial demands, and changes in living environment are often factors that trigger an increased risk of depression in this age group. Therefore, a comprehensive analysis is needed to identify factors that contribute to the emergence of depression so that prevention efforts can be targeted. This study aims to analyze the factors associated with depression among university students in Indonesia using logistic regression method as a classification approach. The research data was obtained from the Kaggle platform and included several independent variables, namely age, gender, academic pressure, sleep duration, diet, financial stress, study satisfaction, and suicidal thoughts. The results of the analysis showed that the suicidal thoughts variable was the most significant factor affecting the likelihood of students experiencing depression, with a coefficient value of 15.0964. In addition, the logistic regression model built is able to provide good prediction performance with an accuracy rate of 95%. The findings are expected to serve as a basis for educational institutions and policy makers in designing early detection strategies, interventions, and depression prevention programs to create a healthier and more supportive campus environment.