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All Journal International Journal of Engineering, Science and Information Technology Brilliance: Research of Artificial Intelligence
Muthalib, Muchlis Abd
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Clustering of Futsal Interest Level Among Students K-Means Method Bagaswara, Faris; Muthalib, Muchlis Abd; Meiyanti, Rini
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.879

Abstract

Futsal is a small field sport with a time of 20 minutes per round. Malikussaleh University is one of the universities that initiated Futsal as a health sport for its students. To determine students' interest in Futsal, clustering was carried out using the K-Means method on 100 students of the Faculty of Engineering involved in this study. This research proposal uses five variables: time variables, field facilities, motivation, environment, and plans. This study aims to help students at Malikussaleh University of Engineering find out what level of interest students have in Futsal. Grouping is based on data mining to determine the pattern of each sequence. Data mining includes tracking patterns, classification, association, outlier detection, clustering, regression, and forecasting. This study also led to an innovative grouping system using the Python programming language and MySQL as a database. The K-Means Clustering algorithm used in this grouping system states that out of 100 Malikussaleh University students, 20 people are students who have a professional player futsal interest level (C1), 28 students have a regular player futsal interest level (C2), five students have a Beginner player futsal interest level (C3), 47 students have an amateur player futsal interest level (C4). The study results showed that 20% were professional, 28% were regular, 5% were beginner, and 47% were amateur players. These results indicate that the interest in Futsal for Malikussaleh University students is still minimal, so encouragement is needed for students to participate in futsal activities.
Prediction Of Unemployment Rate Using The Fuzzy Time Series Chen Model Method Karima, Annisa; Abdullah, Dahlan; Muthalib, Muchlis ABD; Nurdin, Nurdin; Daud, Muhammad
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7310

Abstract

Unemployment is a significant socio-economic problem in Lhokseumawe City that requires serious attention from policymakers. The unemployment rate fluctuates from year to year, making accurate forecasting an important aspect in formulating effective strategies and policies to reduce unemployment. One method that can be used to analyze and forecast time series data with uncertainty is the Fuzzy Time Series (FTS) method, which applies fuzzy logic concepts to handle vague and imprecise data patterns. In this study, the Fuzzy Time Series method is applied to predict the number of unemployed people in Lhokseumawe City. The data used are historical unemployment data over a period of 10 years, from 2013 to 2022. The research process begins with defining the universe of discourse (U), determining the number and length of interval classes, defining fuzzy sets on U, and fuzzifying the unemployment data. Furthermore, Fuzzy Logical Relationships (FLR) are identified and grouped into Fuzzy Logical Relationship Groups (FLRG). The defuzzification process is then carried out to obtain crisp values, followed by forecasting calculations.The analysis was conducted using the RStudio application. The forecasting results show that the predicted number of unemployed people in 2023 is 10,514.125, which is rounded to 10,514 people. The accuracy of the forecasting model is evaluated using Mean Absolute Percentage Error (MAPE) and Average Forecasting Error Rate (AFER), both of which yield values of 6.70%. Since the MAPE and AFER values are less than 10%, the forecasting results can be categorized as very good and reliable for decision-making purposes.
Co-Authors ANNISA KARIMA Bagaswara, Faris Dahlan Abdullah Muhammad Daud Nurdin Nurdin Rini Meiyanti