SISTEM KENDALI SUHU OVEN LITRIK MENGGUNAKAN ALGORITMA ARTIFICIAL NEURAL NETWORK (ANN) UNTUK PEMANGGANGAN UBI CILEMBU

• Main Author: Naftalia Trivenia Simbolon, - (Author)
• Format: Book
• Published: 2023-01-27.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata
• Notes: http://repository.upi.edu/89641/8/S_D5151_1801686_Title.pdf; http://repository.upi.edu/89641/2/S_D5151_1801686_Chapter%201.pdf; http://repository.upi.edu/89641/3/S_D5151_1801686_Chapter%202.pdf; http://repository.upi.edu/89641/4/S_D5151_1801686_Chapter%203.pdf; http://repository.upi.edu/89641/5/S_D5151_1801686_Chapter%204.pdf; http://repository.upi.edu/89641/6/S_D5151_1801686_Chapter%205.pdf; http://repository.upi.edu/89641/7/S_D5151_1801686_Appendix.pdf
• Keywords: L Education (General)

Sistem kendali suhu oven listrik merupakan hal penting dalam proses pemanggangan untuk mencegah kegagalan memanggang kurang matang atau terlalu matang (overcooked). Perlu dilakukannya pengendalian suhu terhadap oven listrik agar suhu yang digunakan tetap stabil dan mampu memanggang makanan hingga matang. Variabel yang mempengaruhi tingkat kematangan objek saat memanggang adalah suhu, waktu, dan massa objek yang digunakan. Prediksi tingkat kematangan dibutuhkan ketika memanggang untuk mencegah kegagalan saat memanggang seperti halnya kurang matang atau bahkan gosong. Artificial Neural Network (ANN) metode sederhana yang paling banyak digunakan untuk memprediksi. Oleh karena itu, penelitian ini pengendalian suhu pada oven listrik. Dengan set point yang digunakan berdasarkan prediksi kematangan ubi cilembu menggunakan ANN. Hasil penerapan sistem kendali pemanasan oven dengan set point 120˚C, dengan suhu awal 26,5˚C mendapatkan persentase error steady state sebesar 15,3%. Dari 75 dataset pemanggangan ubi cilembu didaptkan tingkat kakurasian sebesar 86%. ; The electric oven temperature control system is important in the roasting process to prevent undercooking or overcooking. It is necessary to control the temperature of the electric oven so that the temperature used remains stable and is able to bake food until cooked. The variables that affect the object's maturity level when baking are temperature, time, and the mass of the object used. Prediction of the level of doneness is needed when baking to prevent failure when baking, such as undercooking or even burning. Artificial Neural Network (ANN) is the most widely used simple method for predicting. Therefore, this research is controlling the temperature in an electric oven. With the set point used based on predicting Cilembu sweet potato maturity using ANN. The results of applying the oven heating control system with a set point of 120˚C, with an initial temperature of 26.5˚C get a steady state error percentage of 15.3%. From 75 datasets of roasting Cilembu sweet potatoes, an accuracy rate of 86% was obtained.