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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Tekinfo | Scientific Journal of Industrial and Information Engineering Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik: Jurnal Trinistik
Violita Anggraini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

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Implementasi Algoritma Decision Tree (J.48) untuk Memprediksi Resiko Kredit pada BMT Atik Febriani; Violita Anggraini
Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi Vol 9 No 2 (2021)
Publisher : Program Studi Teknik Industri Universitas Setia Budi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31001/tekinfo.v9i2.904

Abstract

Credit is crucial in financial institutions that affects the growth and development of these institutions. Weak supervision and management in the process of extending credit to customers can lead to high non-performing loans. This problem occured in one of the financial institutions that provides credit to customers, namely BMT X. Data for 2019 showed that there were 600 applications for multipurpose loans. Of these, only about 76% showed good collectability. The condition of credit collectability that is not optimal causes BMT X to spend more to collect installments that must be paid by the debtor directly. This bad credit causes losses to the financial institution. Thus, in providing credit, BMT X must be smart in assessing customer’ feasibility. The purpose of this research is to design credit policies in order to minimize the prediction errors of customers with bad credit category. The technique used in this research is classification data mining with the J.48 algorithm. To measure the effectiveness of an attribute in classifying a data sample set, it is necessary to select the attribute that has the greatest information gain which will be placed at the root node. This research produces six rules with an accuracy level of 80,2% so as it can be used by BMT X to search customer’s feasibility to gain credit. Keywords: Algorithm J.48, data mining, decision tree, credit risk
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 Basirun, Arif Reza Febriani, Atik