Pengembangan Bioproses Berbasis Internet of Things dengan Pengendalian Kelembapan Relatif untuk Meningkatkan Kecernaan Ampas Bawang Merah PT XYZ

Abstract

Proses biokonversi ampas bawang merah sangat dipengaruhi oleh kondisi kelembapan relatif (RH), sehingga fluktuasi RH dapat menurunkan aktivitas mikroorganisme dan mengurangi efektivitas degradasi serat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pengembangan bioproses berbasis Internet of Things (IoT) untuk pengendalian RH selama biokonversi ampas bawang merah serta mengevaluasi pengaruhnya terhadap kualitas hasil biokonversi. Sistem dikembangkan menggunakan mikrokontroler ESP32 yang terintegrasi dengan sensor SHT31, pompa misting, blower, modul relay, dan modul MOSFET, serta menerapkan algoritma one-sided hysteresis dengan setpoint RH 85%. Data proses direkam secara otomatis ke Google Sheets melalui jaringan WiFi. Hasil penelitian menunjukkan bahwa sensor SHT31 memiliki nilai Mean Absolute Percentage Error (MAPE) sebesar 0,99%. Sistem mampu mempertahankan RH pada kisaran 85,29–87,71% dengan standar deviasi maksimum 1,12%, recovery time rata-rata 179 detik, dan nilai Root Mean Squared Error (RMSE) yang menurun dari 2,71% menjadi 0,92% selama biokonversi. Penerapan sistem pengendalian berbasis IoT menghasilkan penurunan kadar Acid Detergent Fiber (ADF) dari 35,61% menjadi 24,83% atau sebesar 30,27% dibandingkan bahan tanpa biokonversi. Hasil tersebut menunjukkan bahwa sistem yang dikembangkan mampu menciptakan kondisi biokonversi yang lebih stabil sehingga mendukung peningkatan kecernaan ampas bawang merah sebagai bahan pakan.

The bioconversion of shallot waste is highly influenced by relative humidity (RH), where RH fluctuations may reduce microbial activity and decrease the efficiency of fiber degradation. This study aimed to design and implement an Internet of Things based bioconversion system for RH control during the bioconversion of shallot waste and to evaluate its effect on bioconversion performance. The system was developed using an ESP32 microcontroller integrated with an SHT31 sensor, a misting pump and a blower. A one-sided hysteresis control algorithm with an RH setpoint of 85% was implemented, while process data were automatically recorded in Google Sheets. The results showed that the SHT31 sensor achieved MAPE of 0.99%. The system maintained RH within the range of 85.29–87.71%, with a maximum standard deviation of 1.12%, an average recovery time of 179 seconds, and RMSE that decreased from 2.71% to 0.92% during the bioconversion process. The implementation of the IoT based control system reduced the Acid Detergent Fiber (ADF) content from 35.61% to 24.83%, representing a relative reduction of 30.27% compared with the untreated material. These findings indicate that the developed system was able to maintain more stable bioconversion conditions, thereby supporting improved digestibility of shallot waste as a livestock feed ingredient.