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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Mechatronics and Artificial Intelligence
Windu Gata
Nusa Mandiri University
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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PREDICTING JOBS OR COLLEGES WITH CLASSIFICATION ALGORITHMS USING Ilham Wahyudi Siadi; Windu Gata; Cicih Sri Rahayu
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 7, No 3 (2024): August 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i3.2138

Abstract

Education is the main foundation in shaping the future of students. In this era of technological development and global competition, it is important for schools to provide quality education, and also help students prepare for their next steps after graduating from school. The question of whether students will continue to higher education or immediately enter the world of work is a very important one. The research objects are Lagger Score, Family Status, KIP Data and Number of Siblings. Then classification identification was carried out from the data using the Random Forest, SVM, Naïve Bayes, Decision Tree, Neural Network algorithms in the Orange application. Based on the results of 433 research data that have been tested, precision, recall and accuracy calculation results are obtained for each model. the highest accuracy of Naïve Bayes and Random Forest is 95% (0,956). The results of this research show that the performance of Naïve Bayes and Random Forest is superior to SVM, Decision Tree and Neural Network. Decision Tree: (0.887), and SVM: (0.949) and Neural Network: (0.942).
PREDICTING JOBS OR COLLEGES WITH CLASSIFICATION ALGORITHMS USING Ilham Wahyudi Siadi; Windu Gata; Cicih Sri Rahayu
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 7 No. 3 (2024): August 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i3.2138

Abstract

Education is the main foundation in shaping the future of students. In this era of technological development and global competition, it is important for schools to provide quality education, and also help students prepare for their next steps after graduating from school. The question of whether students will continue to higher education or immediately enter the world of work is a very important one. The research objects are Lagger Score, Family Status, KIP Data and Number of Siblings. Then classification identification was carried out from the data using the Random Forest, SVM, Naïve Bayes, Decision Tree, Neural Network algorithms in the Orange application. Based on the results of 433 research data that have been tested, precision, recall and accuracy calculation results are obtained for each model. the highest accuracy of Naïve Bayes and Random Forest is 95% (0,956). The results of this research show that the performance of Naïve Bayes and Random Forest is superior to SVM, Decision Tree and Neural Network. Decision Tree: (0.887), and SVM: (0.949) and Neural Network: (0.942).
Social Media Dynamics: Twitter Users’ Responses to the Presence of Naturalized Players in Indonesia's National Football Team Sony Harianto; Windu Gata
Journal of Mechatronics and Artificial Intelligence Vol. 2 No. 1 (2025): JMAI: June 2025
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v2i1.80033

Abstract

Public discourse on the naturalization of players in Indonesia’s national football team continues to grow, with social media platforms such as Twitter becoming primary outlets for expressing opinions. This study employs advanced sentiment analysis techniques, utilizing IndoBERT—a deep learning model specifically designed for the Indonesian language—to analyze the sentiments of Twitter users. The analytical process includes data preprocessing, sentiment distribution visualization, and model performance evaluation. Findings reveal that IndoBERT captures public sentiments more comprehensively and accurately than conventional sentiment analysis models. Sentiment polarity is significantly influenced by factors such as player performance, public expectations, and media narratives. This study offers practical insights for policymakers in Indonesian football to support data-driven strategic decision-making. Furthermore, it underscores the value of natural language processing (NLP) and sentiment analysis in understanding complex socio-cultural dynamics in the digital era.
Co-Authors Cicih Sri Rahayu Ilham Wahyudi Siadi Sony Harianto