Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Siber International Journal of Education Technology Greenation International Journal of Engineering Science
Hendarman Lubis
Universitas Bhayangkara Jakarta Raya, Jakarta, Indonesia
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Mechanical Engineering Social Sciences Other

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Text Mining for Sentiment Analysis of Social Media Regarding the Revision of the TNI Law Using Python by Utilizing the Lexicon Approach Ridwan Ridwan; Hapzi Ali; Hendarman Lubis
Siber International Journal of Education Technology (SIJET) Vol. 2 No. 2 (2024): Siber International Journal of Education Technology (October 2024)
Publisher : Siber Nusantara Review & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/sijet.v2i2.156

Abstract

This study aims to analyze public sentiment regarding the proposed revision of the Indonesian National Military (TNI) Law through social media, specifically Twitter. With the growing role of social media as a public sphere, it serves as a critical platform for expressing public opinions on national issues, including legal reforms. Using sentiment analysis, this research classifies Twitter posts into positive, neutral, or negative categories based on content related to the revision of the TNI Law. Data collection was carried out using the Twitter API (Tweepy), gathering 300 tweets using keywords related to the TNI Law revision. The analysis employed two methods: a lexicon-based approach using TextBlob and machine learning classification using Naive Bayes. The results reveal that the majority of the tweets express neutral sentiment (44.3%), followed by negative sentiment (28.3%) and positive sentiment (27.3%) based on TextBlob analysis. The Naive Bayes model demonstrated better sensitivity to negative sentiment, with 38.3% of tweets classified as negative. The findings suggest that public concern centers around the potential militarization of civil space, human rights violations, and threats to democracy. These concerns highlight the importance of transparent communication from the government regarding policy changes. Additionally, the research underscores the potential of natural language processing (NLP) and machine learning in monitoring public opinion in real time, which can serve as a valuable tool for policymakers in shaping responsive, data-driven legislation.
Analysis of the Influence of E-commerce on Consumer Behavior in Purchasing Beauty Products on the Shopee Marketplace Moderated by Consumer Trust and Price Perception Ridwan; Hendarman Lubis
Siber International Journal of Education Technology (SIJET) Vol. 3 No. 1 (2025): Siber International Journal of Education Technology (July - September 2025)
Publisher : Siber Nusantara Review & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/sijet.v3i1.287

Abstract

This study examines the influence of e-commerce on consumer behavior in purchasing beauty products on Shopee, a leading online marketplace in Southeast Asia, with a focus on the moderating effects of consumer trust and price perception. A survey conducted among 500 Shopee users in Indonesia reveals that e-commerce usage has a significant positive effect on purchasing behavior (? = 0.45, p < 0.01). Furthermore, consumer trust (? = 0.29, p < 0.01) and price perception (? = 0.33, p < 0.01) were found to significantly moderate the relationship between e-commerce usage and purchasing behavior. Structural Equation Modeling (SEM) was employed to analyze the data, which showed that as consumer trust and price perception increase, the likelihood of purchasing beauty products on Shopee also increases. The study highlights that building consumer trust through secure payment systems, transparent product information, and reliable reviews, along with offering competitive pricing strategies, can enhance purchasing behavior. These findings provide practical insights for e-commerce platforms like Shopee to optimize strategies, increase customer loyalty, and boost sales in the growing online beauty market.
Implementation of the Apriori Algorithm for Product Arrangement in a Minimarket Ridwan Ridwan; Hendarman Lubis
Greenation International Journal of Engineering Science Vol. 2 No. 4 (2025): (GIJES) Greenation International Journal of Engineering Science (December 2024
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v2i4.352

Abstract

This research aims to implement the Apriori algorithm to analyze customer purchase patterns and provide recommendations for product placement in Indomaret Perjuangan Bekasi. Data mining techniques, specifically the Apriori algorithm, are applied to transaction data collected from the store between March 1 and April 1, 2025. The primary objective is to identify frequent itemsets and generate association rules that reveal which products are frequently purchased together, providing insights into customer purchasing behavior. The research begins by preprocessing the data into a format suitable for analysis using Weka, a data mining tool. The Apriori algorithm is then applied to uncover product combinations that occur frequently in transactions. Based on the association rules generated, the study recommends optimal product placement strategies to enhance customer convenience and increase cross-selling opportunities. The results show that certain product combinations, such as bread, jam, and milk, and instant noodles, eggs, and sauce, exhibit strong association, with a support of 0.25 and confidence of 0.75 for bread, jam, and milk, and support of 0.20 and confidence of 0.80 for instant noodles, eggs, and sauce. These findings suggest that placing these products in close proximity on the shelves can improve customer shopping experiences and increase sales. Additionally, combinations like coffee, sugar, and milk show a support of 0.15 and confidence of 0.85, indicating strong purchasing patterns. However, the study also acknowledges limitations, such as the small scope of the data and the focus on a single location. The study emphasizes that further research with larger datasets and multiple locations could provide more robust insights. This research demonstrates the practical application of data mining techniques in the retail sector, showing how the Apriori algorithm can optimize store operations and product placement. It provides valuable insights that can help retailers enhance customer satisfaction, streamline inventory management, and boost sales through data-driven decision-making.
Co-Authors Hapzi Ali Ridwan Ridwan Ridwan