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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia
Kusnayadi, Kusnayadi
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Humanities Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Social Sciences

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Optimizing Adaptive Agricultural Systems, Making Organic Fertilizer, and Drip Irrigation Technology in Sukamanah Village Maulana, Adrian Rizki; Damayanti, Fujia; Nurlaela, Intan; Kusnayadi, Kusnayadi; Noval, Muhammad; Rifal, Rafi Muhammad; Alam, Rafi Sandina; Sulistiyo, Satria Putra; Jenab, Siti; Septiana, Fajar Indra
AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Vol 4, No 1 (2025): January 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/aurelia.v4i1.4145

Abstract

This research aims to evaluate the effectiveness of several proposed solutions to improve the agricultural system. The main focus of this study is on introducing an adaptive agricultural system tailored to local environmental conditions, producing organic fertilizers using modern agricultural standards, and implementing drip irrigation technology. The results show that the introduction of an adaptive agricultural system significantly increases productivity through the selection of superior seeds and proper planting patterns. Furthermore, the production of organic fertilizer has proven to improve soil quality and crop yields through the layering method and the use of Effective Microorganisms (EM4). The application of drip irrigation technology enhances water use efficiency, reducing the overuse of water resources. Overall, these three solutions positively contribute to increased productivity, sustainability, and resource efficiency in modern agricultural systems.
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
Co-Authors Adli, Muhammad Zimamul Agung Surya Wibowo Alam, Rafi Sandina Damayanti, Fujia Jenab, Siti Maulana, Adrian Rizki Mentari, Osphanie Muhammad Noval Nurlaela, Intan Rifal, Rafi Muhammad Sulistiyo, Satria Putra