Prediksi Kadar Air Produk Pertanian Berbasis Machine Learning: Sebuah Tinjauan Sistematis
DOI:
https://doi.org/10.22441/jitkom.v10i2.001Keywords:
Deep Learning, Kadar Air, Machine Learning, Pengeringan, Prediksi, Produk Pertanian, Tinjauan SistematisAbstract
Prediksi kadar kelembaban (moisture content/MC) pada produk pertanian merupakan aspek krusial dalam optimasi proses panen, pengeringan, dan penyimpanan untuk menjaga kualitas dan mengurangi kerugian pascapanen. Metode konvensional sering bersifat destruktif dan lambat, sehingga machine learning (ML) seperti artificial neural network (ANN) dan support vector regression (SVR) semakin banyak digunakan untuk prediksi non-destruktif. Dari 51 kandidat paper yang diidentifikasi melalui pencarian sistematis, Systematic Literature Review (SLR) ini meninjau 32 studi terkini (2017-2025) yang memanfaatkan ML untuk prediksi MC pada berbagai tanaman, termasuk gandum, kopi, apel, dan padi, sementara 19 paper dieksklusi karena tidak memenuhi kriteria. Mengikuti panduan PRISMA, paper-paper dipilih dari database seperti Scopus, IEEE Xplore, dan MDPI berdasarkan kriteria inklusi (studi ML untuk prediksi MC pada produk pertanian) dan eksklusi (non-ML atau non-pertanian). Hasil menunjukkan ANN mendominasi (12 paper), diikuti SVR (7) dan random forest (7), dengan input utama fitur gambar (13 paper) dan data sensor (10 paper). Performa umum mencapai R² >0.90 dan RMSE <1%. Tren menunjukkan peningkatan penggunaan deep learning (LSTM, CNN) dan integrasi IoT untuk prediksi real-time, meskipun gap seperti kurangnya deployment lapangan dan validasi cross-varietas masih ada. SLR ini menyimpulkan bahwa ML efektif untuk prediksi MC, mendukung pertanian pintar dan mengurangi kerugian pascapanen global (14-30% menurut FAO), serta merekomendasikan penelitian lanjutan pada hybrid model dan skalabilitas untuk petani kecil di negara berkembang.References
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