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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Computer Science (CO-SCIENCE)
Budi Arif Dermawan
Universitas Pembangunan Nasional Veteran Jakarta
Computer Science & IT Control & Systems Engineering Engineering Other

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Comparative Analysis of Machine Learning Models for Burnout Prediction in Generation-Z Anis Masruriyah; Sanggi Bayu Ardika; Ade Hikma Tiana; Budi Arif Dermawan
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.13081

Abstract

Burnout has become an increasingly prevalent mental health issue among Generation Z due to the interaction of psychological and behavioral factors in a highly digitalized environment. This study aims to predict burnout risk levels using a multi-class machine learning classification approach. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing data understanding, preprocessing, modeling, and evaluation. A synthetic dataset containing 10,000 records and 22 psychological, behavioral, and lifestyle attributes was used to classify burnout risk into three categories: low, medium, and high. To address class imbalance and ensure reliable performance estimation, Stratified K-Fold cross-validation was employed. Logistic Regression was implemented as a baseline linear model, while Random Forest represented a non-linear approach. Experimental results demonstrate that Random Forest achieved the best performance, obtaining a macro F1-score of 0.988 and outperforming Logistic Regression in multi-class burnout prediction. Feature importance analysis further revealed that psychological variables, particularly the wellbeing index and anxiety score, contributed more substantially to burnout prediction than behavioral variables such as screen time. These findings indicate that internal psychological conditions are stronger predictors of burnout risk than external digital behaviors. This study provides a comparative evaluation of linear and non-linear machine learning models in an imbalanced multi-class setting and offers an interpretable framework to support data-driven strategies for early burnout detection and mental health intervention.
DETEKSI SERANGAN SQL INJECTION MENGGUNAKAN ALGORITMA MACHINE LEARNING PADA JARINGAN IOT Rasenda Rasenda; Susanto Susanto; Budi Arif Dermawan
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 10 No. 2 (2025): JUTIM (JURNAL TEKNIK INFORMATIKA MUSIRAWAS) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v10i2.2795

Abstract

SQL Injection merupakan salah satu bentuk serangan siber yang paling berbahaya karena memungkinkan penyerang untuk mengakses, memodifikasi, atau menghapus data secara ilegal melalui manipulasi perintah SQL. Sistem deteksi berbasis aturan memiliki keterbatasan dalam menghadapi pola serangan baru yang bersifat dinamis dan sulit dikenali. Penelitian ini bertujuan mengembangkan model deteksi serangan SQL Injection dengan pendekatan machine learning menggunakan kombinasi Autoencoder dan Algoritma Machine Learning. Autoencoder digunakan untuk mengekstraksi fitur dan mendeteksi pola anomali pada data input, sedangkan Algoritma Machine Learning berperan sebagai model klasifikasi untuk membedakan antara permintaan normal dan serangan. Data yang digunakan terdiri atas payload berlabel yang mencakup input normal dan serangan SQL Injection, yang selanjutnya diproses melalui tahapan normalisasi, ekstraksi fitur, dan pelatihan model. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian diharapkan menghasilkan model deteksi yang adaptif, mampu mengenali pola serangan baru, serta memiliki tingkat kesalahan deteksi yang rendah pada sistem keamanan jaringan IOT.
Co-Authors Ade Hikma Tiana Anis Masruriyah Rasenda Rasenda Sanggi Bayu Ardika Susanto Susanto