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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Dzakwan Daffa Ramdhana
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Rancang Bangun Sistem Klasifikasi Kualitas Minyak Goreng Dengan Parameter Kecerahan Dan Warna Menggunakan Metode Random Forest Dzakwan Daffa Ramdhana; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cooking oil is also a raw material that many Indonesians use to cook various types of processed food. Oil has many functions for the human body, including as a source and solvent for vitamins A, E, K, and D, as well as a more effective source of energy when compared to carbohydrates and protein. In society, the oil that is often used is packaged oil and used cooking oil. The use of cooking oil is increasing, causing people to save money by using used cooking oil. The use of oil repeatedly causes quality damage and is very dangerous for human health, one of the diseases is carcinoma, which is cancer cells or malignant tumors of epithelial cells. This happens because oil that is used repeatedly will make peroxide compounds increase in the oil content. The higher the peroxide number, the more concentrated the liquid. There are various ways of testing to determine the quality of oil. First, physical testing methods, one of which is the water content in oil. Then chemically, one of which is the determination of the peroxide number. In addition, physically it can also be seen through the brightness and color of the oil. The system design in this study uses a TCS3200 sensor and a Light Dependant Resistor (LDR) sensor used to measure the color and brightness of the oil respectively. The classification results in the form of less feasible and feasible classes can be seen on the LCD and serial monitor. There are 8 test data from 25 available oil datasets. From the 8 test data, the accuracy of random forest classification is 87.5% and the average computation time is 26.9ms.
Co-Authors Edita Rosana Widasari Utaminingrum, Fitri