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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Abdidas Eduproxima : Jurnal Ilmiah Pendidikan IPA Jurnal Minfo Polgan (JMP) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Journal of Technology and Computer (JOTECHCOM) JIPITI: Jurnal Pengabdian kepada Masyarakat Jurnal Pemberdayaan Kepada Masyarakat
Ellanda Purwawijaya
Universitas Battuta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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The Significant Rise In Cybercrime Can Be Attributed To Vulnerabilities In Cybersecurity Ellanda Purwawijaya; Dinur Syahputra; Aripin Rambe; Junerdi Nababan
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13490

Abstract

In today's world, our dependence on computers extends to even the most basic daily tasks. As users, we consistently engage with computers for activities such as communication, data sharing, information retrieval, social interactions, and more, all conducted over networks. It is evident that the network connecting various devices globally, including servers, computers, laptops, mobile phones, etc., serves as the fundamental technology facilitating these tasks. However, this interconnected system poses a significant threat in the form of cybercrime. Despite the implementation of cybersecurity measures throughout the network, there exist flaws and obstacles that compromise security, leading to the occurrence of these crimes. One can envision the relationship between cybercrime and cybersecurity as a ratio, with cybercrime holding the higher value. This variable is steadily increasing at a greater rate than cybersecurity, indicating a growing imbalance between the two.
Kompleksitas Fungsional Perangkat Lunak Menggunakan Serangkaian Kriteria Baru dalam Unified Modeling Language (UML) Ellanda Purwawijaya
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13623

Abstract

Pengembangan teknologi yang cepat mengakibatkan kompleksitas dan ukuran desain perangkat lunak yang semakin meningkat, yang mengarah pada beban kerja administratif dan pengembangan yang tinggi. Studi ini fokus pada pengukuran kompleksitas perangkat lunak menggunakan desain database dan diagram Unified Modeling Language (UML) untuk mengambil data terkait. Model Entity Relationship (ER) yang dikembangkan oleh Peter Chen menyediakan kerangka kerja konseptual untuk mengklasifikasikan data dalam konteks relasional. Skema ORS (Object Relationship Schema) yang disempurnakan juga diperkenalkan untuk memberikan pandangan yang lebih baik mengenai persyaratan basis data. Kompleksitas sistem perangkat lunak kontemporer juga harus dinilai dalam pendidikan tinggi, yang dipengaruhi oleh faktor seperti ketergantungan fungsional, keamanan, kompleksitas komputasi, kasus penggunaan, dan struktur komponen. Penelitian ini berkontribusi pada pemahaman tentang kompleksitas perangkat lunak dan pentingnya penilaian dalam pendidikan tinggi.
Predictive Modeling of Smartphone Addiction: Performance Evaluation of KNN, XGBoost, and Naive Bayes on Behavioral Patterns M. Rhifky Wayahdi; Fahmi Ruziq; Auliana Nasution; Ellanda Purwawijaya
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16178

Abstract

Excessive smartphone use has triggered a global crisis in the form of smartphone addiction, which negatively impacts mental health and productivity. Most current detection methods still rely on subjective questionnaires that are prone to bias. Therefore, this study aims to evaluate and compare the performance of machine learning-based predictive models—namely K-Nearest Neighbors (KNN), Naive Bayes, and Extreme Gradient Boosting (XGBoost)—in objectively classifying addiction levels based on user behavioral patterns. The research methodology adopts a standard machine learning workflow encompassing data preprocessing, model training, and performance evaluation using a dataset of 3,300 user activity log entries. Empirical results demonstrate that XGBoost yields superior classification performance, achieving an accuracy of 97.27% and an F1-Score of 96.70%, significantly outperforming the KNN (94.54%) and Naive Bayes (89.09%) algorithms. Further feature importance analysis confirms that App Usage Time is the most crucial predictor in detecting addiction. This study concludes that the XGBoost architecture is highly robust in handling non-linear behavioral feature interactions, enabling high-precision predictions. The implications of these findings provide a solid technical foundation for the development of automated early detection systems. Future research should consider expanding the dataset to include application categorization and integrating XGBoost modeling into real-time digital wellbeing application prototypes.
Co-Authors Anzani, Nurul Aripin Rambe Azmi, Zulfian Dinur Syahputra Dinur Syahputra Eka Hayana Hasibuan Fithrie Soufitri Fithrie Soufitri Gunawan, Syahrizal Ilham Harahap , Baginda Hasibuan, Rizqy Sultan Pujangga Hendra Pasaribu Hutahaean, Mian Natanael Irawati, Novica Julita, Rina Lina Lina Lubis, Aulia Rahmah M. Rhifky Wayahdi Nababan, Junerdi Nasution , Auliana Nasution, Auliana Nur Wahyuni Pariyasto, Sofyan Rahmad Syuhada Rambe , Aripin Rambe, Aripin Roy Nuary Singarimbun Roy Nuary Singarimbun ruziq, fahmi Singarimbun , Roy Nuary Singarimbun, Roy Nuary Sridewi, Nurmala Surya Darma Syahputra , Dinur Utari Utari