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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Deep Learning, Computer Vision and Digital Image Processing
Azzahra, Aura
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Computer Science & IT Electrical & Electronics Engineering

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Pengendalian Untuk Mengoptimalkan Produksi Mie Pada Warung Mie Pedas Dengan Menggunakan Logika Fuzzy Berbasis Metode Tsukamoto Safitri, Ayu; Azzahra, Aura; Kurnia, Shahnaz Tasha
Journal of Deep Learning, Computer Vision, and Digital Image Processing Vol 2 No 1 (2024): Vol. 2 No. 1 (March 2024)
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v2i1.316

Abstract

ABSTRACT The rapid development of technology has led to a change in consumer consumption patterns, including food consumption, especially the production of noodles. Noodles are a source of energy and nutrients needed by all living organisms. One of the noodle dishes often used as a substitute for rice is instant noodles. Instant noodles are easy to serve and practical, and their production is usually carried out based on consumer demand. However, there is still a lack of research on determining the optimal production quantity of instant noodles to meet consumer demand and align with the availability of raw materials. Therefore, it is suggested to use Fuzzy Logic-based Method Tsukamoto to optimize noodle production at noodle shops. The Fuzzy Logic Method Tsukamoto helps regulate noodle production in accordance with consumer preferences and avoid waste of raw materials. The results of the study show that the method can adjust the production quantity of instant noodles based on consumer demand and availability. This approach ensures that noodle production meets consumer needs and prevents overuse of raw materials Keywords: Noodles, Tsukamoto, demand, supply, production
Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression Azzahra, Aura; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4155

Abstract

PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.
Co-Authors Ayu Safitri Kurnia, Shahnaz Tasha M. Afdal Mustakim Mustakim Rice Novita