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All Journal POSITIF Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Komputasi dan Teknologi Informasi INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Innovative: Journal Of Social Science Research Majalah Cendekia Mengabdi Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics INSTALL: Information System and Technology Journal
Prastya, Septyan Eka
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Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest Febriani, Wulandari; Mambang, Mambang; Prastya, Septyan Eka; Sabella, Billy; Marleny, Finki Dona
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11415

Abstract

Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
Analysis of the Utilization of TikTok Content as a Coping Strategy to Reduce Stress Among Final-Year Students Using a Classification Method Husna Karima; Zulfadhilah, Muhammad; Prastya, Septyan Eka; Pratiwi, Evi Lestari
INSTALL: Information System and Technology Journal Vol 2 No 3 (2025): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v2i3.991

Abstract

Stress represents a prevalent psychological challenge among final- year university students, particularly during thesis completion. Academic pressure, social demands, and future uncertainty trigger stress that negatively impacts mental health. Social media, especially TikTok, is increasingly utilized as a coping mechanism to reduce stress through entertainment, educational, and motivational content. This study aims to analyze TikTok content utilization as a coping strategy for stress reduction among final-year students using a classification method. This quantitative research employed a survey approach with a population of 342 active TikTok users among final- year students at Sari Mulia University. Data were collected through an online questionnaire covering variables including content type, duration, features used, and psychological indicators such as anxiety, emotions, escapism, and coping effectiveness. Data preprocessing included one-hot encoding, SMOTE, and normalization, followed by classification using Support Vector Machine with RBF kernel optimized through GridSearchCV. Results revealed very high correlations among psychological variables (r ≈ 0.93–1.00), while correlations between content type and stress reduction were relatively low (0.00–0.15). Some pure entertainment content showed negative correlations with psychological improvement. The SVM model achieved high classification accuracy of approximately 94%. This study demonstrates that TikTok can serve as a short-term stress coping tool for final-year students, though its effectiveness depends heavily on the type of content consumed. Educational and motivational content shows greater potential for stress reduction compared to pure entertainment content. This research contributes to understanding digital mental health support mechanisms and provides insights for developing healthier media consumption strategies among university students.
The Effectiveness of early stopping on the efficiency of training CNN models for phishing URL identification Rifani, Muhammad Rifani; Prastya, Septyan Eka; Zulfadhilah, Muhammad; Munsyi
INSTALL: Information System and Technology Journal Vol 3 No 1 (2026): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v3i1.1024

Abstract

Phishing is a significant cybersecurity threat in which malicious URLs deceive users to steal sensitive data. Traditional detection methods, such as blacklists, often fail to keep pace with evolving phishing techniques. Deep learning, particularly Convolutional Neural Networks (CNNs), offers strong potential in phishing URL classification by capturing structural and semantic character-level patterns. However, CNN training demands high computational resources and risks overfitting. This study investigates the effectiveness of early stopping as a regularization technique to improve efficiency and generalization in character-based CNN models. Using a large-scale dataset of 130.080 URLs across four classes (benign, phishing, malware, defacement), the model employed character tokenization, embedding, convolution-pooling layers, and softmax classification. Early stopping monitored validation loss with patience values of 3, 5, and 10 epochs. Results show a 51% training time reduction and accuracy improvement from 96% to 97%, confirming early stopping as an efficient and robust detection approach.
Co-Authors Adla, Musyfia Ahmad Hidayat Ahmad Riki Renaldy Aulia, Hudatul Bayu Nugraha Bima Wicaksono Cipta, Subhan Panji Dodon Turianto Nugrahadi Ekawati, Norliana Evi Lestari Pratiwi - Politeknik Hasnur Kalimantan Selatan, Evi Lestari Pratiwi Fatma Indriani Fatmawati, Samsinah Fauzie Rahman Fawwaz, Nur Aisha Fawwaz, Nur Aisya Febriani, Wulandari Finki Dona Marleny Fitriani, Karlina Elreine Friska Abadi Haniffah Sri Rinjani Hidayaturrahman, Muhammad Hudatul Aulia Husna Karima Indah Wulandari Irwan Budiman Izhariaqi, Mahwa Kartika Kartika Kartika Kartika Mahmudah Mahmudah Mambang Maria Ulfah Maulida, Nur Meilianti Melda Melda Muhammad Itqan Mazdadi Muhammad Khairul Akbar Muhammad Reza Faisal, Muhammad Reza Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Zaini Bakri Muhammad Zulfadhilah Muliadi Munsyi Nur Meilianti Maulida Nurhaeni Nurhaeni Nurhaeni Nurhaeni NURUL HIDAYAH Pebriadi, Muhammad Syahid Prasetya, M. Riko Anshori Pratama, Ramadhani Noor Putri Putri Putri Putri, Putri Putria, Evita Panca Rafi'i, Rafi'i Rahmini Rahmini Rifani, Muhammad Rifani Ripani, Ahmad Risma Maulida Risma Risma Risma Risma, Risma Rismawati Rismawati rizhariaqi, Mahwa Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari, Rudy Sabella, Billy Sandro Nesta Pembriano Subhan Panji Cipta Susanto, Jessika Kindly Tiara, Astia Rahma Yunida, Rahmi Yuslena Sari, Yuslena