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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Sains Informatika Terapan (JSIT)
Budi Arham
Universitas Persada Bunda Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Diabetes Risk Prediction Using XGBoost-Based Machine Learning and Explainable Artificial Intelligence Sukasih Sukasih; Budi Arham; Nelti Rizka; Muhammad Badar
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27124

Abstract

Diabetes mellitus is a widespread chronic disease that affects populations worldwide and is often only identified after complications have occurred. Machine learning techniques can be applied to estimate the likelihood of diabetes based on clinical patient data. This research focuses on developing a predictive model using the Extreme Gradient Boosting (XGBoost) algorithm, along with the application of Explainable Artificial Intelligence (XAI) to interpret the model’s outcomes. The dataset used in this study consists of 768 patient records with eight clinical attributes, namely pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index (BMI), diabetes pedigree function, and age. The research process includes data preprocessing, exploratory data analysis, model development, performance evaluation, and interpretation using SHAP. The findings indicate that the XGBoost model achieves high predictive performance and is capable of identifying key factors associated with diabetes risk. Based on SHAP interpretation, glucose level, BMI, age, and insulin are the most influential variables in the prediction process. The integration of machine learning and explainable AI improves model interpretability while maintaining reliable prediction performance.
Stacking Ensemble Model berbasis SVM, Random Forest, dan XGBoost untuk Klasifikasi Emosi dari Sinyal EEG Rafika Sani; Sukasih; Budi Arham
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.930

Abstract

Klasifikasi emosi berbasis sinyal EEG (Electroencephalography) merupakan bidang penting dalam pemrosesan sinyal biomedis karena perannya dalam pengambilan keputusan, interaksi sosial, dan evaluasi kondisi psikologis. Namun, tingginya dimensi data, kompleksitas pola, serta kerentanan terhadap noise menjadi tantangan utama dalam analisis EEG. Penelitian ini menerapkan Stacking Ensemble Model yang mengombinasikan Support Vector Machine (SVM), Random Forest (RF), dan XGBoost (XGB) dengan Logistic Regression sebagai meta-learner untuk meningkatkan akurasi dan stabilitas prediksi emosi. Dataset yang digunakan berasal dari Kaggle dengan 2132 data dan 2549 fitur, mencakup tiga kelas emosi: negatif, netral, dan positif. Tahapan penelitian meliputi pengecekan missing value, normalisasi menggunakan StandardScaler, encoding label, serta pembagian data menjadi data latih dan uji. Hasil eksperimen menunjukkan akurasi model tunggal yang tinggi, yaitu SVM 94,8%, RF 98,6%, dan XGB 99,5%, sedangkan model stacking mencapai akurasi 99,5% dengan nilai precision, recall, dan f1-score mendekati 1,00. Hasil ini menunjukkan bahwa stacking ensemble mampu meningkatkan keandalan klasifikasi emosi berbasis EEG dan berpotensi diterapkan pada HCI, pemantauan kesehatan mental, serta sistem pengenalan emosi real-time.
Prediksi Harga Emas Harian Dan Multi-Horizon Menggunakan ARIMA, LSTM Dan Model Hybrid ARIMA-LSTM Budi Arham; Sukasih; Rafika Sani
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.935

Abstract

Gold price forecasting is a crucial task in financial analysis due to high market volatility and dynamic price movements. This study aims to predict daily gold prices and perform multi-horizon forecasting up to 10 days ahead using Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and a hybrid ARIMA-LSTM model. The dataset consists of daily gold closing price data from January 2022 to December 2024 obtained from Yahoo Finance. Data preprocessing includes handling missing values, normalization using Min–Max scaling, and stationarity testing. ARIMA and LSTM models are developed independently to capture linear and nonlinear patterns, respectively, while the hybrid model combines both approaches by modeling ARIMA residuals using LSTM. Experimental results show that the hybrid ARIMA-LSTM model achieves the lowest prediction error compared to individual models, as indicated by RMSE, MAE, and MAPE values. Furthermore, multi-horizon forecasting results demonstrate that the hybrid model provides more stable and accurate short-term gold price predictions. These findings confirm that the hybrid modeling approach is effective and can support investors and analysts in decision-making processes.
Co-Authors Muhammad Badar Nelti Rizka Rafika Sani Sukasih Sukasih Sukasih