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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Euis Widanengsih
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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Analisis Pengelompokan Karakter Pemain Usia Dini di Sekolah Futsal Golden Eagles Berdasarkan Faktor Perilaku Menggunakan Algoritma K-means Abdul Khoir; Muhamad Arifin Ilhan; Kevin Ananda Maas; Risca Lusiana Pratiwi; Euis Widanengsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1709

Abstract

This study aims to analyze and categorize the character of young futsal players based on the results of coaches' assessments using the K-Means Clustering algorithm. Data were obtained from 70 Golden Eagles Futsal School students aged 5–13 years who were directly observed by coaches during routine training sessions. Assessment aspects included concentration, cooperation, discipline, enthusiasm, emotional control, and response to instructions. The analysis process was carried out using Python with the Scikit-learn library as the main tool for data processing and visualization of results (bar charts and heatmaps). The clustering results formed three main clusters: (1) independent and highly focused children, (2) developing children, and (3) children who require more attention. The average behavioral scores showed clear differences between clusters, with the first group having the highest levels of concentration and discipline. These findings indicate that a data-driven approach can provide a deep understanding of the character of young children in the context of futsal, as well as serve as a reference for coaches in designing more personalized and effective coaching strategies.
Spam Message Classification Using the Naïve Bayes Algorithm Based on RapidMiner Muhamad Yusup; Mochamad Isham Fadillah; Rifky Adinanta Fauzanie; Risca Lusiana Pratiwi; Rani Irma Handayani; Euis Widanengsih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1811

Abstract

This study implements the Naïve Bayes algorithm for classifying spam and non-spam (ham) messages using the RapidMiner Studio platform. The dataset used was obtained from the SMS Spam Collection Dataset on the Kaggle platform, which consists of 5,759 messages with a distribution of 4,075 ham messages and 1,291 spam messages. The research stages included text pre-processing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the Naïve Bayes model achieved an accuracy of 89.64% with a precision of 56.93%, a recall of 100%, and an F1-score of 72.56%. The research findings indicate that the Naïve Bayes algorithm is effective in detecting spam messages with adequate accuracy, and prove that RapidMiner is an efficient tool for implementing machine learning methods in text classification.
Co-Authors Abdul Khoir Kevin Ananda Maas Mochamad Isham Fadillah Muhamad Arifin Ilhan Muhamad Yusup Rani Irma Handayani Rifky Adinanta Fauzanie Risca Lusiana Pratiwi