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All Journal JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Salisu, Imam Auwal
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering

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Clustering Of Informatics Study Program Based On Understanding The Material Using The K-Means Algorithm Prasetyo, Naufal Ibra; Bria, Dionisia Kasilda; Paratu, Jeki Bani; Wafa, Fachrian Muhammad Ahzami; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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The level of student understanding in coursework is a crucial determinant of academic success, reflecting both teaching quality and the effectiveness of applied learning methods. In the context of Informatics, challenges often stem from the complexity of subjects such as algorithms, programming, and data analysis, which require analytical and in-depth comprehension. However, differences in learning abilities, backgrounds, and styles often result in varying levels of understanding among students. This study investigates the application of k-means clustering as an innovative method to analyze academic data and classify students based on their understanding of course materials. By utilizing data such as exam scores, quiz results, and classroom engagement, k-means clustering identifies patterns in students’ comprehension levels, offering educators insights to tailor teaching strategies effectively. The findings of this study are expected to aid educators in designing targeted interventions, enhance learning processes, and support an inclusive and effective academic environment.
Trend Detection and Popular Topics on Social-Media Using a clustering algorithm to find patterns and topics that are going viral on the Instagram platform Jamaq, Evan; Hasan, Abdul Aziz; Kurniawan, M Rizal; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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This study aims to cluster Instagram posts based on hashtags and the number of likes using the K-Means Clustering algorithm. The data used is data that represents various popular topics on social media, such as travel, culinary, fashion, and local coffee. The analysis process involves data preprocessing, clustering algorithm implementation, and result evaluation to identify patterns and trends among users. The results successfully grouped posts into three main clusters, namely clusters with low engagement, clusters related to local food and coffee, and clusters with high engagement on travel and fashion topics. This clustering provides useful insights for marketers, content creators, and researchers in understanding social media user behavior and designing more effective marketing strategies. This research confirms the importance of data analysis as a tool to uncover hidden patterns and support data-driven decision-making.
Sentiment Analysis of Comments on Higher Education Social Media Using Naïve Bayes Algorithm Salisu, Imam Auwal; Ramadhan, Irzal Raisya; Matdoan, Sakina; Arifin, Zainal; Praseptiawan, Mugi
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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The rapid development of information technology has driven the widespread use of social media across various aspects of life, including the academic environment. Social media platforms, such as Instagram, have become popular channels for disseminating information and fostering interactions between individuals and groups. With the growing number of users, sentiment analysis on social media is essential to understand public perceptions and responses to specific issues. Higher education institutions play a strategic role in creating a positive image through social media. Social media provides opportunities for universities to convey achievements, academic activities, and other information effectively to a broader audience, enhancing their reputation in the public eye. Moreover, Instagram serves not only as a communication tool but also as an educational medium capable of increasing student engagement through relevant and informative content. Technically, the Naïve Bayes algorithm is well-known for its speed and efficiency in sentiment analysis. This probability-based method leverages historical data to predict positive, negative, or neutral sentiments, offering competitive accuracy even when handling large datasets. This study aims to apply the Naïve Bayes algorithm for sentiment analysis of comments on the Instagram account of Widyagama University (@uwg.malang) as a case study. The research is expected to provide valuable insights for developing effective communication strategies and serve as a reference for other higher education institutions or organizations in utilizing analytical technologies for strategic purposes.
Utilizing Datamining to Predict Sales Trends Based on Historical Data Junda, Alby Afifuddin; Trisna, Maria Rosalina; Genohon, Yustino Prami; Akhdan, Farrel Muhammad Raihan; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in predicting sales trends based on historical data. The results of the study show that SVM is more effective than Naïve Bayes with an accuracy of 34.74% compared to 15.49%. This study helps companies in making strategic decisions and improving operational efficiency. Data Mining is an important tool in predicting sales trends and improving prediction accuracy.
Co-Authors Akhdan, Farrel Muhammad Raihan Bria, Dionisia Kasilda Genohon, Yustino Prami Hasan, Abdul Aziz Jamaq, Evan Junda, Alby Afifuddin Kurniawan, M Rizal Matdoan, Sakina Paratu, Jeki Bani Praseptiawan, Mugi Prasetyo, Naufal Ibra Ramadhan, Irzal Raisya Trisna, Maria Rosalina Wafa, Fachrian Muhammad Ahzami Zainal Arifin