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All Journal Agroindustrial Journal CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Sato, Yuta
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Analysis of MSMEs' Cassava Production Efficiency Using a Comparison of Machine Learning Models in Jember Regency Hadi, Danang Kumara; Sato, Yuta
Agroindustrial Journal Vol 12, No 1 (2025)
Publisher : Department of Agroindustrial Technology, Faculty of Agricultural Technology UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/aij.v12i1.106018

Abstract

Cassava is one of Indonesia's agro-industrial commodities, but many Micro, Small, and Medium Enterprises (MSMEs) in the cassava processing industry face difficulties in achieving optimal production efficiency. This study aims to evaluate the efficiency of cassava processing production systems in MSMEs in Jember by comparing machine learning algorithms (Linear Regression, Random Forest, Support Vector Regression (SVR), and XGBoost) to predict output and key efficiency factors. The data used consists of 250 data points: 80% for model training and 20% for testing to build a machine learning-based prediction model, with input features production processing as the X-axis, and output in the form of production volume as the Y-axis. Data preprocessing, exploratory data analysis, and modeling were conducted using Python, with evaluation based on MAE, RMSE, and R² metrics. Among the tested models, Random Forest demonstrated the best performance with an R² value of 0.990. Sensitivity analysis revealed that production output increases significantly with the addition of labor and machines, with an optimal configuration of 15–20 workers and 2–3 machines per batch. The study concludes that focusing on overall production efficiency rather than merely increasing resources is the most effective strategy.
Volume Measurement System of Used Cooking Oil (UCO) Using Internet of Things and Web-Based System Koto, Zulafrilian; Anandika, Arrya; Sato, Yuta; Pramawahyudi, Pramawahyudi; Syahputra, Ronaldo
CHIPSET Vol. 7 No. 01 (2026): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.7.01.77-84.2026

Abstract

One of the main issues is the discrepancy between the measured and actual oil volumes of used cooking oil (UCO). Using a measurement tank as the measuring medium and an ultrasonic sensor as the tool, a system was developed to automatically measure oil volume with high accuracy. This system allows users to continuously measure oil volume using a servo motor that directs the oil into a storage tank. The system is designed to notify the company and customers via an LCD display and website when the oil measurement is completed. The system successfully measures oil volume using the ultrasonic sensor, achieving an accuracy of 96.74% during implementation.
Co-Authors Anandika, Arrya Koto, Zulafrilian Kumara Hadi, Danang PRAMAWAHYUDI, PRAMAWAHYUDI Syahputra, Ronaldo