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All Journal Indonesian Journal Computer Science (ijcs) Buffer Informatika
faizah, haniyah
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Computer Science & IT

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Classification of Depression Severity Using a Random Forest Algorithm Based on Lifestyle, Demographic, and Psychological Factors faizah, haniyah; Theonady, Oktavio; Salsabillah S, Syalwa; Fathoni; Ibrahim, Ali
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.569

Abstract

Depression among college students is a mental health issue that impacts quality of life and academic performance. However, factors influencing depression levels such as lifestyle, demographics, and psychological factors have not yet been analyzed in an integrated manner. This study aims to develop a depression severity classification model using the Random Forest algorithm based on these factors. The dataset consists of 1,998 records with 16 features selected through the Knowledge Discovery in Database (KDD) process. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results show that the Random Forest model achieved an accuracy of 97.88% and an AUC of 0.998. Feature importance analysis indicates that the variables Symptoms, Nervous Level, and Employment Status are dominant factors in determining depression levels. Based on these results, the model is capable of effectively classifying depression levels and has the potential to serve as the basis for an early detection system in the university setting.
Analisis Asosiasi Antara Produktivitas Pelajar dan Manajemen Waktu Berdasarkan Algoritma FP-Growth Rabbani, Muhammad Randy; Theonady, Oktavio; Faizah, Haniyah; Satria, Eka Bayu; Meiriza, Alsella; Tania, Ken Ditha
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12327

Abstract

Penelitian ini bertujuan menganalisis hubungan antara manajemen waktu dan produktivitas pelajar menggunakan algoritma FP-Growth. Data yang digunakan berasal dari dataset Ultimate Student Productivity yang terdiri dari 5.000 data dan 21 atribut. Analisis dilakukan melalui tahapan Knowledge Discovery in Databases (KDD) yang meliputi seleksi data, pra-pemrosesan, transformasi, serta pembentukan association rule berdasarkan nilai support, confidence, dan lift ratio. Hasil penelitian menunjukkan bahwa kategori sedang (medium) mendominasi sebagian besar variabel yang dianalisis. Aturan asosiasi memiliki nilai confidence tinggi dan lift ratio lebih dari satu, yang menunjukkan hubungan signifikan antar variabel. Produktivitas kategori sedang berkaitan dengan durasi belajar dan tingkat fokus yang seimbang, sedangkan kategori rendah berkorelasi dengan hasil akademik yang rendah. Temuan ini menunjukkan bahwa keseimbangan dalam pengelolaan waktu belajar berperan penting dalam membentuk pola produktivitas pelajar. Selain itu, pendekatan berbasis data mampu memberikan gambaran objektif mengenai perilaku belajar siswa. Temuan ini dapat dimanfaatkan untuk mengoptimalkan manajemen waktu belajar guna meningkatkan produktivitas dan capaian akademik pelajar, serta sebagai acuan bagi institusi pendidikan dalam menyusun strategi pembelajaran berbasis data.
Co-Authors Ali Ibrahim Fathoni Ken Ditha Tania Meiriza, Alsella Rabbani, Muhammad Randy Salsabillah S, Syalwa Satria, Eka Bayu Theonady, Oktavio