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All Journal JURIKOM (Jurnal Riset Komputer)
Sriani
Universitas Islam Negeri Sumatera Utara, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Logika Fuzzy Tsukamoto Sebagai Sistem Pendukung Keputusan Penentuan Mata Kuliah Pilihan Mahasiswa Ilmu Komputer XYZ Muhammad Reza Alhafiz; Sriani
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9453

Abstract

The selection of elective courses poses a challenge for Computer Science students at XYZ University because it influences competency development, while objective decision-making guidance remains limited. This study aims to develop a web-based decision support system to recommend specialization elective courses using the Fuzzy Tsukamoto method. Data were collected through questionnaires from students in semesters five to seven and processed into four input variables: Robotics, Mathematics, Programming, and Analysis. Each variable was modeled into three fuzzy sets (Weak, Moderate, Strong) using trapezoidal membership functions and processed through IF–THEN rule-based inference with a total of 162 rules. Output values were obtained through weighted average defuzzification to generate course recommendations. System testing was conducted by comparing system outputs with manual calculations and evaluated using the Mean Absolute Percentage Error (MAPE). The results showed a MAPE value of approximately ±0.1096%, indicating that the implementation of the Tsukamoto method in the system is consistent with manual calculations. This study contributes to providing a structured and objective decision support system to assist students in determining elective courses based on their competencies.
Prediction of Burnout Syndrome Risk in University Students Using the C5.0 Algorithm Noni Fauzia Rahmadani; Sriani
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9550

Abstract

Burnout among university students is a serious issue that can reduce learning motivation, academic performance, and mental health. Approximately 25–30% of students experience burnout symptoms, which negatively affect concentration and academic productivity. Early detection is still limited due to the lack of accurate data analysis. This study aims to predict the risk level of student burnout using the C5.0 algorithm as a classification method capable of handling both categorical and numerical data. The research data were obtained from 306 students at Universitas Islam Negeri Sumatera Utara through an online questionnaire based on the Maslach Burnout Inventory–Student Survey (MBI-SS). The data were processed through cleaning, encoding, and splitting into training and testing sets using Python. The results show that the model achieves excellent classification performance, with an accuracy of 99.25% on the training set (precision 99.72%, recall 99.45%) and 97% on the testing set (precision 100%, recall 96%). The model also identifies the most influential attributes contributing to burnout, such as stress level and emotional exhaustion. The main contribution of this study is the development of an accurate and interpretable machine learning-based model for predicting student burnout risk. These findings provide practical implications for educational institutions in supporting early detection and designing data-driven preventive interventions, such as counseling services and stress management programs.
Co-Authors Muhammad Reza Alhafiz Noni Fauzia Rahmadani