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All Journal WITECH: Jurnal Teknologi Rekayasa Komputer dan Jaringan
Jumriati Jumriati
Universitas Teknologi Akba Makassar, Indonesia

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Application of Machine Learning for Student Data Classification: Penerapan Machine Learning untuk Klasifikasi Data Siswa Jumriati Jumriati; Rahmaniar Rahmaniar
WITECH: Jurnal Teknologi Rekayasa Komputer dan Jaringan Vol. 1 No. 1 (2025): WITECH: Jurnal Teknologi Rekayasa Komputer dan Jaringan
Publisher : Program Studi Teknologi Rekayasa Komputer dan Jaringan, Politeknik Wahdah Islamiyah Makassar

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Abstract

Student data classification plays an important role in academic analysis, helping schools find patterns, organize student information, and guide decisions about how well students are doing and how to improve programs. As more educational data becomes available, machine learning offers better and more reliable ways to understand student traits and predict how they will perform in learning. This study uses machine learning to sort student data based on key academic and personal details. The research process covers cleaning the data, choosing the best features to use, and testing three different algorithms: Naïve Bayes, K-Nearest Neighbors (KNN), and Decision Tree. The effectiveness of these methods is measured using accuracy, precision, recall, and F1-score. The results show that the Decision Tree method is the most accurate at sorting student data, followed by KNN and Naïve Bayes. These results highlight how useful machine learning can be in the field of educational data mining, especially for keeping track of student progress, spotting students who might struggle early on, and helping schools make better decisions. This study offers real-world advice for schools looking to use data more effectively in managing students and planning educational programs.
Intelligent Smart Cooking: Predictive Cooking Time Model Using Machine Learning and IoT: Intelligent Smart Cooking: Model Prediksi Cooking Time Menggunakan Machine Learning dan IoT Jumriati Jumriati
WITECH: Jurnal Teknologi Rekayasa Komputer dan Jaringan Vol. 1 No. 1 (2025): WITECH: Jurnal Teknologi Rekayasa Komputer dan Jaringan
Publisher : Program Studi Teknologi Rekayasa Komputer dan Jaringan, Politeknik Wahdah Islamiyah Makassar

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Abstract

Smart cooking systems have gained significant attention as part of the growing smart home ecosystem, yet most existing solutions rely on static, rule-based thresholds that lack adaptability to variations in food type, weight, and cooking conditions. This study proposes an Intelligent Smart Cooking system that integrates Internet of Things (IoT) sensing with a machine learning–based predictive model to estimate cooking time in real time. Temperature data were collected from 180 cooking sessions using a DS18B20 sensor, while MQ-series gas sensors supported safety monitoring. A dataset containing temperature curves, heating rates, food mass, and water volume was constructed and used to train three regression models: Multiple Linear Regression, Support Vector Regression, and Random Forest Regression. Experimental results show that Random Forest achieved the best performance with an MAE of 18.93 seconds and an R² of 0.954, demonstrating strong capability in capturing nonlinear cooking behavior patterns. The trained model was deployed into the IoT system to enable predictive cooking automation, real-time flame control through a servo motor, and hazard prevention via gas detection. User evaluations also indicated high usability and reliability of the system. The findings highlight the potential of combining IoT and machine learning to improve accuracy, safety, and efficiency in next-generation smart kitchen technologies.
Co-Authors Rahmaniar Rahmaniar