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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Pendidikan Terapan
Nelti Rizka
Institut Pendidikan Dan Teknologi ‘Aisyiyah Riau
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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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.
The Use of Educational Robots and Pedagogical Deep Learning Approach to Enhance Critical Thinking in Early Childhood Education Nini Aryani; Nelti Rizka; Fikri Ramadan; Tommy Tanu Wijaya; Priska Novita
Jurnal Pendidikan Terapan Vol 4, No 4 September (2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jupiter.v4i4.1399

Abstract

Purpose: This study aims to examine the implementation of Educational Robots using a Pedagogical Deep Learning Approach as a learning medium to foster critical thinking skills in early childhood education. Methods: This study employed a descriptive qualitative approach. Data were collected through observations and semi-structured interviews involving children aged 5–6 years. The data were analyzed using coding, category development, and thematic analysis to identify patterns and themes emerging from the learning process. Findings: The findings indicate that Educational Robots were implemented through learning activities emphasizing exploration, problem-solving, and collaborative play, in line with the principles of meaningful, mindful, and joyful learning. The results suggest that the use of Educational Robots facilitated children's critical thinking skills, as reflected in their ability to analyze shapes, make decisions, predict outcomes, identify errors, and revise construction strategies. In addition, increased engagement, collaboration, and curiosity were observed throughout the learning process. Research Implications: This study suggests that Educational Robots integrated with a Pedagogical Deep Learning Approach can facilitate meaningful and accessible learning experiences that support the development of critical thinking skills in early childhood. The findings also offer an innovative, affordable, and sustainable learning medium that can assist early childhood educators in implementing meaningful learning practices. Originality: The originality of this study lies in integrating Educational Robots constructed from recycled materials with a Pedagogical Deep Learning Approach to foster critical thinking skills in early childhood education. This study offers an innovative, affordable, sustainable, and contextually relevant learning medium to support the implementation of meaningful learning in Indonesian early childhood education settings.
Co-Authors Budi Arham Fikri Ramadan Muhammad Badar Nini Aryani Priska Novita Sukasih, Sukasih Tommy Tanu Wijaya