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Data Mining Classification in The New Student Admission Process Using The K-Nearest Neighbors Method : Case Study: Yapmi Boarding School Veri Arinal; Tri Wahyudi; Mesra Betty Yel; Nurul Khoiriyah
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i4.111

Abstract

The new student admission process is an important activity in educational institutions to ensure that prospective students meet the established admission criteria. However, the selection process is often conducted manually, making it less efficient and prone to subjective assessments. This study aims to implement a data mining classification approach using the K-Nearest Neighbors (K-NN) method to support decision-making in the new student admission process at Yapmi Boarding School. The research utilizes historical admission data consisting of academic scores, interview results, and other admission criteria as classification attributes. The K-NN algorithm was applied to classify prospective students into accepted and rejected categories based on the similarity of their characteristics to previously evaluated applicants. The research methodology includes data collection, preprocessing, classification modeling, and performance evaluation using accuracy metrics. The results demonstrate that the K-NN method is capable of classifying prospective students effectively and can assist admission committees in making more objective and accurate decisions. The implementation of this model contributes to improving the efficiency, consistency, and reliability of the student admission process at Yapmi Boarding School. Therefore, the K-NN algorithm can be considered a viable alternative for supporting educational admission decision systems.
Sentiment Analysis of the Trending Topic #Indonesiagelap on X Using a Naive Bayes Algorithm Based on Particle Swarm Optimization Untung Surapati; Veri Arinal; Tri Wahyudi; Ahmad Fauzan
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.127

Abstract

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.