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All Journal Jurnal Riset Sistem dan Teknologi Informasi
Muhammad Afandi
Universitas Nurul Jadid, Probolinggo
Computer Science & IT Decision Sciences, Operations Research & Management

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Bapak Analisis dan Prediksi Diabetes Menggunakan Artificial Neural Network dengan Dataset CDC Diabetes Health Indicators : Analisis dan Prediksi Diabetes Menggunakan Artificial Neural Network dengan Dataset CDC Diabetes Health Indicators Dodi Dwi Riskianto Dwi; Muhammad Afandi; M. Raihan Ramadhan; Sudriyanto Sudriyanto
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2096

Abstract

Diabetes mellitus is a chronic disease with increasing prevalence and requires effective early detection efforts. This study aims to develop a diabetes risk prediction model using an Artificial Neural Network (ANN) based on non-laboratory health indicators. The dataset used is the CDC Diabetes Health Indicators with a large amount of data and characteristics of classes that are not fully balanced. The research stages include data preprocessing that includes handling missing values, encoding categorical data using one-hot encoding, normalization of numerical features, and analysis of the target class distribution. The ANN model was trained using a Multilayer Perceptron architecture with dropout regularization and L2 penalty and AdamW optimization. The evaluation results show that the model achieved an accuracy of 86.45%, a precision of 85.2%, a recall of 82.7%, and an AUC-ROC value of 0.89. Although the accuracy is in the medium range for a large dataset, the high AUC value indicates excellent model discrimination ability. This performance is affected by the limited number of non-laboratory features used and the imbalanced class distribution. The findings of this study indicate that ANN based on simple health indicators has the potential to be used as a diabetes risk screening tool in primary healthcare. Further research is recommended to apply class balancing techniques, model interpretability analysis, and external validation in the Indonesian population.
Bapak Prediksi Depresi Mahasiswa Menggunakan Algoritma Random Forest Berbasis Data Psikososial Depression Prediction Among University Students Using a Random Forest Algorithm Based on Psychosocial Data : Prediksi Depresi Mahasiswa: Pendekatan Berbasis Data Psikososial Menggunakan Algoritma Random Forest Abiyya Alfahrizi Putra Arifiansyah Abiyya; Muhammad Afandi; Dodi Dwi Riskianto; Sudriyanto Sudriyanto
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2100

Abstract

College students' mental health is a critical issue that is gaining increasing attention, particularly regarding depression, which significantly impacts quality of life and academic achievement. This study aims to develop a predictive model for depression in college students based on psychosocial data using the Random Forest algorithm. The data used is a public secondary dataset from Kaggle with 1,000 samples, covering demographic variables, lifestyle, and psychological indicators. The analysis process included data preprocessing, class balancing, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. Test results showed that the Random Forest model was able to predict depression with 87.0% accuracy, 86.1% precision, 87.4% recall, and 86.7% F1-score, demonstrating good and stable performance. Word cloud visualization identified academic pressure, stress, and anxiety as dominant factors. Compared to previous research using the SVM algorithm, Random Forest demonstrated improved performance, particularly in handling complex and imbalanced data. This study confirms the effectiveness of the Random Forest-based machine learning approach in supporting the early detection of college students' depression and provides a foundation for the development of mental health monitoring systems in higher education settings.
Co-Authors Abiyya Alfahrizi Putra Arifiansyah Abiyya Dodi Dwi Riskianto Dodi Dwi Riskianto Dwi M. Raihan Ramadhan sudriyanto sudriyanto