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Jurnal Sains Teknologi dan Sistem Informasi
Published by Yayasan Pendidikan Penelitian Pengabdian Algero
ISSN : -     EISSN : 28078152     DOI : https://doi.org/10.54259/satesi
Core Subject : Science,
Computer Science & IT
SATESI (Jurnal Sains Teknologi dan Sistem Informasi) merupakan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis kritis terhadap isu-isu Ilmu Komputer, Sistem Informasi, dan Teknologi Informasi baik secara nasional maupun internasional. Artikel ilmiah yang dimaksud berupa kajian teoritis dan kajian empiris dari ilmu-ilmu terkait, yang dapat dipertanggungjawabkan dan disebarluaskan secara nasional maupun internasional. Jurnal SATESI menerima artikel ilmiah dengan bidang penelitian di: Sistem Informasi Keamanan Informasi Rekayasa Sistem Data Mining Big Data Sistem Pakar Sistem Penunjang Keputusan Pengolahan Citra Sistem Kecerdasan Buatan/Artificial Intelligent System Business Intelligence and Knowledge Management Database System Internet of Things Enterprise Computing Machine Learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 116 Documents
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Analisis Faktor-Faktor Penyebab Depresi Mahasiswa di Indonesia Menggunakan Metode Regresi Logistik Kiki Mustaqim; Woro Isti Rahayu; Muhammad William Farma; Muhammad Rizky El Sulthani Lintang
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7071

Abstract

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.
Analisis Sosio-Teknikal Disrupsi AI: Transformasi Arsitektur Pembelajaran dari Digital Assistance Menuju Human-Machine Co-Evolution di Pendidikan Vokasi Dita Rahmawati; Sinta Bella Agustina; Indriansyah, Agung; Ninditama, Ilsa Palingga; Purwanto, M Bambang
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7351

Abstract

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.
Segmentasi Investor Cryptocurrency Menggunakan Metode K-Means: Studi terhadap Faktor-Faktor yang Mendorong Keputusan Investasi Arosochi Yosua Daeli; Vicky Darmana; Sirait, Kevin Bastian; Sinaga, Triandes
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 2 (2025): Oktober 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i2.7405

Abstract

The growth of cryptocurrency investors is very rapid with diverse characteristics and various investment driving factors. This study aims to analyze and form investor segmentation in cryptocurrency based on investment driving factors using the K-Means Clustering algorithm. A quantitative approach was applied through online questionnaires to 300 respondents who are cryptocurrency investors, with 289 valid data meeting the research criteria. The variables studied include four driving factors: Fear of Missing Out (FOMO), social media influence, high profit potential, and interest in the investment world. Data were processed through Min-Max normalization, Principal Component Analysis (PCA), and K-Means clustering using Orange Data Mining. The optimal number of clusters was determined using the Silhouette Score, while cluster validation used K-Nearest Neighbors (KNN). ANOVA and Games-Howell tests confirmed significant differences between clusters. The results identified four clusters: Cluster 1 (Emotional Investors, n=37), Cluster 2 (Ambitious Investors, n=156), Cluster 3 (Rational Investors, n=50), and Cluster 4 (Passive Investors, n=46). Cluster 3 is the most optimal in investment decision-making with a profit rate of 90% and zero loss (0%). These findings confirm that optimal investment decisions are driven by rational analysis and logical consideration without excessive emotional influence.
Factors Influencing Customer Purchase Decisions in AI-Driven Online Shopping: Systematic Review Arnold Aribowo; Hery, Hery; Andree Emmanuel Widjaja; Calandra Alencia Haryani
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7476

Abstract

This paper offers a PRISMA-guided systematic literature review to examine how Artificial Intelligence (AI) is applied in e-commerce. The focus is on identifying key factors that influence customer purchasing decisions in AI-driven online transactions. It examines Information Systems (IS) theories relevant to the integration of AI and e-commerce, offering insights into frameworks used to analyze the relationship between AI and consumer behavior. Additionally, the paper identifies gaps in current research and provides recommendations for future studies, particularly in areas requiring further exploration to understand the evolving impact of AI on e-commerce. Through a review of existing literature, the study identifies critical factors such as perceived enjoyment, perceived usefulness, perceived ease of use, interactivity, consumer engagement, AI technology, and information quality, which significantly affect consumer purchase intentions. This review finds that Stimulus-Organism-Response (SOR) and Technology Acceptance Model (TAM) are the most commonly adopted theories, while Media Richness Theory is used less frequently. The findings provide a robust foundation for future research, enabling the formulation of empirically testable hypotheses. Furthermore, this study offers a more integrated perspective by organizing identified constructs into a multi-dimensional framework and suggests directions for future empirical research, such as developing research models and validating them through survey-based approaches and Structural Equation Modeling (SEM-PLS), as well as qualitative methods. The study aims to offer insights to AI developers and e-commerce practitioners, helping them enhance AI-powered systems to better meet consumer needs and expectations, ultimately improving customer satisfaction and increasing purchase rates.
Analisis Kinerja AES dan RC6 pada File Multimedia Sugiyatno, Sugiyatno; Hafidz Maulana Rahman
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7523

Abstract

This study aims to analyze and compare the performance of symmetric cryptographic algorithms, namely Advanced Encryption Standard (AES) and Rivest Cipher 6 (RC6), in securing multimedia files. The research employs a quantitative experimental approach using various file formats, including JPG, PNG, MP3, and MP4, with different file sizes. Performance evaluation is conducted based on three main parameters: encryption time, decryption time, and throughput. The implementation is carried out in a controlled local environment using Python to ensure consistency and accuracy of measurements. The results show that AES consistently outperforms RC6 in terms of faster encryption and decryption processes as well as higher and more stable throughput across all tested formats and file sizes. In contrast, RC6 exhibits higher computational overhead due to its complex arithmetic operations, resulting in slower processing time and less stable performance. Furthermore, the findings indicate that file size and format significantly influence algorithm performance, where larger and more complex multimedia data require higher processing time. This study contributes a comprehensive multi-format evaluation framework that provides practical insights for selecting efficient cryptographic algorithms in real-world multimedia security applications.
Peran Generative Artificial Intelligence dalam Meningkatkan Efisiensi Proses Pembelajaran di Tingkat Tinggi Wincent Wisely; Aurich Thedis; Roy, Roy; Alkaffy Kaffy Ramba; Evander Banjarnahor
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7542

Abstract

The development of Generative Artificial Intelligence (Gen AI) has significantly transformed the learning process, particularly in improving task completion efficiency. This study aims to analyze the effect of Gen AI usage on learning efficiency among high school students and university students. A quantitative approach was employed using a survey of 83 respondents. The variables examined include frequency of use, duration of use, and learning efficiency, which is measured based on task completion time. The results indicate that the level of Gen AI usage is relatively high, with a mean frequency of 3.51 and a mean duration of 3.53, while efficiency shows the highest mean value of 3.94. Regression analysis reveals that the model is statistically significant (p-value < 0.05) with a coefficient of determination of , indicating that 61.18% of the variance in learning efficiency is explained by the model. Partially, frequency of use has a positive and significant effect on efficiency ( ; p-value < 0.05), whereas duration of use is not statistically significant ( ; p-value > 0.05). These findings suggest that usage intensity plays a more critical role than usage duration. Overall, Gen AI is shown to enhance learning efficiency; however, its effectiveness depends on how users actively and appropriately engage with the technology.

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