SIMONSA-MBG: SISTEM PEMANTAUAN SISA MAKANAN BERBASIS IOT DENGAN ANALISIS FOOD WASTE RATIO DI SEKOLAH

Wachid Darmawan; Widiyono Widiyono; Muhammad Faizal Kurniawan

doi:10.47775/ictech.v21i2.425

Penulis

Wachid Darmawan IC-Tech https://orcid.org/0009-0009-7678-1011
Widiyono Widiyono
Muhammad Faizal Kurniawan

DOI:

https://doi.org/10.47775/ictech.v21i2.425

Kata Kunci:

Internet of Things, Makan Bergizi Gratis, Food Waste Ratio, Smart Environment, Sistem Pendukung Keputusan

Abstrak

Program Makan Bergizi Gratis (MBG) memerlukan sistem pemantauan sisa makanan berbasis data untuk mendukung evaluasi menu dan pengelolaan sampah di lingkungan sekolah. Penelitian ini bertujuan mengembangkan SIMONSA-MBG, sistem monitoring sampah MBG berbasis Internet of Things (IoT) yang mengintegrasikan pengukuran sensor dengan analisis Food Waste Ratio (FWR) di sekolah. Penelitian menggunakan metode Research and Development (R&D) yang meliputi pengumpulan data, analisis, perancangan, implementasi, dan pengujian. Purwarupa menggunakan ESP32, load cell HX711, sensor ultrasonik HC-SR04, dan DHT22 yang terhubung dengan Firebase Realtime Database dan dashboard web. Pengujian pemantauan kontinu selama 120 menit pada 4 titik pengamatan menunjukkan peningkatan tingkat keterisian dari 4,4% menjadi 72,4% dan akumulasi massa sisa makanan sebesar 17,6 kg. Pengujian menghasilkan = 0,9995, = 1,000, serta MAPE sebesar 0,85% untuk massa > 20 kg dan MAE sebesar 0,125 cm dan MAPE 0,60% untuk jarak 5-10 cm. Penerapan FWR pada sepuluh jenis menu menghasilkan FWR agregat sebesar 11,17%, dengan empat menu berada pada kategori rendah, lima menu pada kategori sedang, dan satu menu pada kategori tinggi. Hasil penelitian menunjukkan bahwa dashboard SIMONSA-MBG dapat menyediakan informasi kuantitatif mengenai kondisi wadah, timbulan sisa makanan, dan FWR per jenis menu sebagai dasar pendukung pengambilan keputusan. Penelitian selanjutnya disarankan untuk memperluas pengamatan pada beberapa sekolah dan mengembangkan prediksi timbulan sisa makanan berbasis machine learning.

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