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All Journal International Journal of Electrical and Computer Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science
Adli, Muhammad Zimamul
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Computer Science & IT Electrical & Electronics Engineering

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Multi-Classification of Pakcoy Plants using Machine Learning Methods with Smart Greenhouse Dataset Wibowo, Agung Surya; Mentari, Osphanie; Adli, Muhammad Zimamul; Kusnayadi, Kusnayadi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2212

Abstract

This research aims to design and implement a monitoring and classification system for Pakcoy (Brassica rapa L.) plant conditions based on the Internet of Things (IoT) and machine learning algorithms in the Smart Greenhouse of Universitas Islam Nusantara. This study represents one of the applications of IoT and machine learning technology advancements to improve efficiency and effectiveness in the agricultural sector. The developed system utilizes CO?, SHT30, BH1750, and DHT22 sensors to monitor environmental parameters in real-time, including temperature, humidity, light intensity, panel box temperature, and CO? concentration. The monitoring data are used as input for classifying plant conditions using five machine learning methods: Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron (MLP). The results show that the Random Forest algorithm achieves the best performance, with an accuracy of 84%, precision of 86%, recall of 87%, and F1-score of 86%. The implementation of this system serves as a concrete step toward enhancing the efficiency, sustainability, and modernization of hydroponic agriculture in Indonesia
Tuning feature selection to enhance machine learning predictions of bandgap and efficiency in chalcogenide perovskites Primadianti, Osphanie Mentari; Iman, Ryan Nur; Adli, Muhammad Zimamul; Toha, Agung Muhamad; Wibowo, Agung Surya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1508-1517

Abstract

Solar cell technology has advanced rapidly in efficiency and material innovation. As a renewable energy source, solar cells help mitigate the global energy crisis. Perovskite-based solar cells have recently achieved efficiencies above 25%, surpassing conventional silicon cells. Among emerging materials, chalcogenide perovskites show great promise due to their superior stability compared to halide perovskites. However, they remain in the exploration stage, making accurate predictions of their electrical properties, especially bandgap, essential for assessing potential in solar cell applications. This study predicts bandgap values using computational methods, emphasizing efficiency and cost reduction compared to experimental approaches. Key features derived from collected data include oxidation state, electronegativity, coordination number, ionic radius, and density. Several machine learning (ML) algorithms: AdaBoost Regressor, gradient boosting regressor, support vector regressor, CatBoost Regressor, and k-neighbor regressor, were implemented using Python. The research process involved data collection, preprocessing (feature scaling, fusion, reduction, and selection), model training and testing with 5-fold cross-validation, and hyperparameter optimization to achieve optimal results. Among the tested models, CatBoost Regressor yielded the best performance, achieving a coefficient of determination (R2) of 69.34%, a mean absolute error (MAE) of 23.1%, and root-mean-square error (RMSE) of 29.49%, demonstrating its effectiveness in predicting chalcogenide perovskite bandgaps.
Co-Authors Agung Muhamad Toha Agung Surya Wibowo Iman, Ryan Nur Kusnayadi, Kusnayadi Primadianti, Osphanie Mentari