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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik: Jurnal Trinistik
Basirun, Arif Reza
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Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

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Smart Monitoring System Pada Pembudidayaan Jamur Tiram Menggunakan Metode Fuzzy Logic Basirun, Arif Reza; Anggraini, Violita; Al Kautsar, Muhammad Nurudduja
Jurnal TRINISTIK: Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik Vol 3 No 1 (2024): Maret 2024
Publisher : nter of Execellence (COE) ICT Infrastructure, Smart Manufacture and Digital Supply Chain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/trinistik.v3i1.1333

Abstract

Mushrooms, which are known as food commodities, when cultivated require temperatures between 22-28°C and humidity of 60-80%. Controlling these two factors is very crucial, especially when weather fluctuations occur throughout the day. As a solution, an artificial intelligence-based system was introduced to regulate temperature and humidity. The "fuzzy control" technique in artificial intelligence is an option because of its flexibility and convenience without involving complex mathematical calculations. The Fuzzy Mamdani method is one approach to "fuzzy control" which allows automatic control of temperature and humidity in mushroom cultivation areas. To apply this method, sensors are needed that can provide data to the fuzzy system. This fuzzy system works in four steps: fuzzification, rule determination, inference, and defuzzification. Based on the results of comparative tests with MATLAB software, there is a correctness level of around 26% in the measurement results.
Prediction of Anime Rating with Hybrid Artificial Neural Networks and Convolutional Neural Networks Al Kautsar, M. Nurudduja; Anggraini, Violita; Basirun, Arif Reza; Rifai, Achmad Pratama
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 1 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i1.28390

Abstract

This study proposes an innovative approach to predict anime scores by leveraging a combination of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Tabular data such as source, number of episodes, type, and genre are incorporated alongside the image representation of anime into a holistic model. Evaluation results on the test set show satisfactory performance, with an average loss value of 0.673, Mean Absolute Error (MAE) of 0.654, and Mean Absolute Percentage Error (MAPE) of 9.44%. Training and validation graphs reflect the model's convergence without significant signs of overfitting or underfitting. The integration of information from both data sources yields a model capable of providing accurate predictions of anime scores, contributing to an understanding of trends and preferences in the anime industry, and opening opportunities for the development of similar models in the field of score prediction or other quality evaluations.
Co-Authors Achmad Pratama Rifai Al Kautsar, M. Nurudduja Al Kautsar, Muhammad Nurudduja Violita Anggraini