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Journal : International Journal of Information Technology and Computer Science Applications (IJITCSA)

Multinomial Naive Bayes Algorithm for Indonesian language Sentiment Classification Related to Jakarta International Stadium (JIS) Rizki Surya Pratama, Daffa; Munandar, Tb Ai; Fadhilla Ramdhania, Khairunnisa
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 1 (2024): January - April 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i1.118

Abstract

The research focuses on analysing public evaluations, particularly those on Google Maps, about the Jakarta International Stadium (JIS). The study aims to employ the multinomial Naive Bayes algorithm to ascertain the sentiment expressed in these reviews. The objective of this study was to employ the multinomial Naive Bayes method to analyse the reviews on Google Maps pertaining to the Jakarta International Stadium (JIS). The utilised data consists of 2971 public reviews on Google Maps specifically pertaining to Jakarta International Stadium (JIS). These reviews were acquired through web scraping using a data miner. The acquired data is next processed in the text preparation phase to generate a prepared dataset suitable for analysis. This preprocessing stage includes operations such as casefolding, stopword removal, tokenizing, and stemming. The study yielded an accuracy of 0.83, or 83%, when tested on 733 data points. Out of these, 292 positive data points were correctly anticipated, while 59 positive data points were incorrectly forecast. Additionally, 317 negative data points were correctly predicted, while 65 negative data points were incorrectly predicted. The conducted modelling is subsequently categorised using a novel dataset of 161 review data points, with the objective of discerning the sentiment expressed within the dataset. The analysis of the new dataset yielded 101 reviews with positive sentiment and 50 reviews with negative sentiment.
Comparative Analysis of K-Means and Hierarchical Clustering for Regional Welfare Disparity Identification in West Java Province Muhamad Dani Yusuf; Tb Ai Munandar; Khairunnisa Fadhilla Ramdhania
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.213

Abstract

This study aims to cluster regencies/cities in West Java Province based on public welfare indicators using the K-Means Clustering and Hierarchical Clustering methods. The data used includes health, economic, population density, and average length of schooling indicators in 2023. Cluster quality evaluation was performed using the silhouette score. The results show that K-Means Clustering with five clusters yields the highest silhouette score of 0.219. For comparison, Hierarchical Clustering with the Ward Linkage method and eight clusters was chosen, having a silhouette score of 0.202, which is the largest among other Hierarchical Clustering methods. The identification of each cluster's characteristics in K-Means reveals areas with multidimensional challenges (Cluster 1), industrial areas with unemployment issues (Cluster 2), areas with high stunting prevalence despite good access to basic facilities (Cluster 3), densely populated urban areas with good welfare but high unemployment (Cluster 4), and areas with very high health complaints and low welfare (Cluster 5). K-Means clusters (except Cluster 4) tend to have a low average length of schooling, below 12 years. Consistency in cluster patterns was found between K-Means and Ward Linkage, especially in advanced urban areas and areas with multidimensional welfare challenges in southern West Java. These findings are expected to serve as a reference for the government and policymakers in formulating more targeted and effective development strategies.