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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Adiwisastra, Miftah Farid
Universitas Bina Sarana Informatika
Computer Science & IT Electrical & Electronics Engineering

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Pengembangan Model Random Forest Regressor untuk Prediksi Kelembaban pada Pertanian Perkotaan Berkelanjutan Adiwisastra, Miftah Farid; Bahri, Saeful; Umar, Habib
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3162

Abstract

Agriculture is a sector that supports food security. Currently, agriculture faces serious challenges due to climate change, land limitations, and low technology adoption. This study aims to develop an Internet of Things (IoT)-based smart farming system integrated with artificial intelligence and run through edge computing. The prototype system is designed to collect real-time data on crop growth environments using pH, TDS, temperature, humidity, and water level sensors. The data is then processed locally using the Random Forest Regressor algorithm to determine optimal environmental conditions. Test results show that the model has very high accuracy in predicting humidity (R² = 0.99; RMSE = 0.65) and temperature (R² = 0.99; RMSE = 0.17), although there are still discrepancies in extreme conditions. The integration of IoT, AI, and edge computing has proven to improve energy efficiency, accelerate response times, and provide adaptive and affordable solutions in support of sustainable urban agriculture productivity.Keywords: Artificial Intelligence; Random Forest Regressor; IoT; Edge Computing AbstrakPertanian merupakan sektor yang mendukung ketahanan pangan, saat ini pertanian menghadapi tantangan serius akibat perubahan iklim, keterbatasan lahan, dan rendahnya adopsi teknologi. Penelitian ini bertujuan mengembangkan sistem pertanian cerdas berbasis Internet of Things (IoT) yang terintegrasi dengan kecerdasan buatan dan dijalankan melalui komputasi tepi. Prototipe sistem dirancang untuk mengumpulkan data lingkungan pertumbuhan tanaman secara real-time menggunakan sensor pH, TDS, suhu, kelembaban, dan tinggi permukaan air. Data kemudian diproses secara lokal menggunakan algoritma Random Forest Regressor untuk menentukan kondisi lingkungan optimal. Hasil pengujian menunjukkan model memiliki akurasi sangat tinggi pada prediksi kelembaban (R² = 0,99; RMSE = 0,65) dan suhu (R² = 0,99; RMSE = 0,17), meskipun masih terdapat selisih pada kondisi ekstrem. Integrasi IoT, AI, dan edge computing terbukti mampu meningkatkan efisiensi energi, mempercepat respons, serta memberikan solusi adaptif dan terjangkau dalam mendukung produktivitas pertanian perkotaan berkelanjutan.  
Dataset Development for Quran Memorizers: A Step Towards Data-Driven Personalized Learning Path Adiwisastra, Miftah Farid; Darmawan, Irfan; Nurjanah, Dade
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7343

Abstract

Advances in artificial intelligence and machine learning have created new opportunities for improving Quran memorization methods. A key requirement for such innovation is the availability of structured and representative datasets specifically designed for Quran memorizers. This study presents the design and development of a dataset that captures demographic characteristics, daily learning behavior, and temporal memorization patterns from 350 students across three Islamic boarding schools in Indonesia. Through preprocessing stages, including normalization, discretization, and feature engineering, the dataset was prepared for Hidden Markov Model (HMM)-based analysis. Experimental results show that the model achieved an accuracy of 30.57%, precision of 71.46%, recall of 30.57%, and F1-score of 37.70% in predicting memorization states. These findings indicate that the proposed dataset provides a useful foundation for modeling memorization progress and supporting adaptive learning path recommendations. However, the study is limited by the relatively small dataset and the modest predictive performance of the initial HMM model. Overall, this work provides an important first step toward building an intelligent and personalized data-driven Quran memorization system.
Co-Authors Bahri, Saeful Darmawan, Irfan Nurjanah, Dade Umar, Habib