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Analisis Sistem Kontrol Kelembapan Ruang Budidaya Berbasis ANFIS-IoT Februariyanti, Herny; Khristianto, Teguh; Jananto, Arief; Nurraharjo, Eddy
Journal of Telecommunication Electronics and Control Engineering (JTECE) Vol 7 No 2 (2025): Journal of Telecommunication, Electronics, and Control Engineering (JTECE)
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jtece.v7i2.1833

Abstract

Pengelolaan lingkungan mikro dalam ruang budidaya tertutup memegang peranan penting dalam menjamin pertumbuhan tanaman yang optimal. Kelembapan udara merupakan salah satu parameter krusial yang sangat dipengaruhi oleh kondisi non-stasioner dan dinamis dari sistem tertutup. Oleh karena itu, dibutuhkan sistem kontrol yang adaptif dan cerdas untuk menjaga kelembapan dalam rentang ideal. Penelitian ini mengusulkan sistem kontrol kelembaban berbasis Adaptive Neuro-Fuzzy Inference System (ANFIS) yang diintegrasikan dengan data sensor Internet of Things (IoT). Dataset dikumpulkan dari ruang budidaya aktual, lalu dilakukan preprocessing, pelatihan model ANFIS selama 200 epoch, dan evaluasi menggunakan metrik RMSE dan koefisien determinasi (R²). Model ANFIS menunjukkan performa yang cukup baik pada data pelatihan dengan nilai RMSE = 1.1927 dan R² = 0.5174, namun mengalami penurunan performa pada data pengujian dengan RMSE = 2.5669 dan R² = -1.0104. Hasil ini mengindikasikan kebutuhan akan penyempurnaan model agar lebih tahan terhadap data baru dan anomali. Sistem kontrol kelembaban berbasis ANFIS-IoT menunjukkan potensi dalam mengotomatisasi pengaturan lingkungan ruang budidaya secara cerdas. Meskipun model awal memiliki keterbatasan dalam generalisasi, pendekatan ini membuka peluang pengembangan sistem prediksi dan kontrol berbasis hybrid yang lebih adaptif untuk lingkungan dinamis.
Classification Of Sea Wave Heights On The North Coast Of Central Java Using Random Forest Supriyanto, Aji; Diartonor, Dwi Agus; Hartono, Budi; Jananto, Arief; Afandi, Afandi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5108

Abstract

Global climate change has triggered an increase in the occurrence of significant wave heights (SWH) and sea level rise (SLR) in coastal areas, including the northern coast of Central Java, Indonesia (Pantura). These phenomena directly impact maritime activities, coastal erosion, and tidal flooding. This study aims to classify and predict significant wave height (SWH) and sea level rise (SLR) trends using a machine learning approach based on the Random Forest (RF) algorithm. Daily meteorological and oceanographic observation data from 2019 to 2024, provided by BMKG, serve as the main dataset. The dataset includes wind speed, ocean current velocity, air pressure, and wave direction. SWH is categorized into three classes: Calm, Low, and Moderate. The classification model achieved excellent performance with an accuracy of 98.54%, a macro F1-score of 0.942, and maintained strong accuracy even for the minority class (Moderate) despite data imbalance. The RF Regressor for SWH prediction yielded an R² of 0.864, MAE of 0.067, and RMSE of 0.109 m. Visualizations such as scatter plots, boxplots, and heatmaps supported the conclusion that ocean current speed and wave period are key factors influencing SWH. The study concludes that Random Forest is effective for classifying and predicting sea conditions in tropical regions like Pantura, and it is feasible for implementation in data-driven early warning systems to mitigate coastal risks. This contributes to marine safety and coastal risk mitigation planning.