Ahmad Abdul Chamid
Muria Kudus University

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Application of Naïve Bayes for Sentiment Analysis of Shopee App User Comments Muhammad Dwiky Candra Fardani; Esti Wijayanti; Ahmad Abdul Chamid
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2854

Abstract

The growth of e-commerce has transformed consumer behavior, with Shopee emerging as one of the leading platforms in Southeast Asia and particularly dominant in Indonesia. Millions of user reviews on the Google Play Store capture diverse experiences, yet their unstructured nature hinders efficient extraction of actionable insights. This study addresses the challenge by developing an automated sentiment analysis system for Shopee user reviews, focusing on the effective use of the Naïve Bayes algorithm for Indonesian-language data. While Naïve Bayes is widely applied in text classification, this research distinguishes itself by integrating rigorous preprocessing tailored to colloquial and context-specific Indonesian app reviews, coupled with TF-IDF weighting, to enhance classification performance. A dataset of 4,000 reviews was collected via web scraping, labeled automatically based on user ratings, and split into 80% training and 20% testing subsets. Preprocessing included cleaning, case folding, tokenization, and stemming to standardize textual input. The proposed model achieved an accuracy of 83%, precision of 81%, recall of 90%, and F1-score of 85%, indicating strong performance despite class imbalance and the prevalence of ambiguous or sarcastic expressions. The results demonstrate that a lightweight probabilistic classifier, when combined with domain-specific preprocessing, can yield competitive accuracy while maintaining computational efficiency. This study contributes to sentiment analysis research in underrepresented linguistic contexts and offers a practical framework for e-commerce platforms to systematically interpret large-scale user feedback, prioritize feature improvements, and enhance customer satisfaction strategies.
Web-Based Customer Loyalty Point System Using QR Code with Whatsapp Notification and Reward Management at Bismole Elektrik Store Qatrhunnada Abiyu Akhdan; Aditya Akbar Riadi; Ahmad Abdul Chamid
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apv6sm19

Abstract

The development of information technology encourages retail business operators to implement digital systems to improve service quality and operational efficiency. At Bismole Elektrik Store, the processes of recording customers, purchase transactions, and calculating loyalty points were previously done manually, leading to data-recording errors and slowing down the service process. Based on the observation of 120 customer transaction data and interviews with 2 store owners and cashiers, several issues were found, such as difficulties in searching for customer data and discrepancies in point calculations. This research aims to develop a web-based customer loyalty point system using QR codes as a digital customer identity integrated with reward management, sales reports, and real-time WhatsApp notifications. The system development uses the Waterfall method, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. The system is developed using the programming languages PHP, HTML, CSS, JavaScript, and the MySQL database. The system evaluation was conducted using the Black Box Testing method with 9 testing scenarios and User Acceptance Testing involving 5 users consisting of the store owner, cashier, and customers. The results of the Black Box Testing showed that all system features operated with a success rate of 100%, while the User Acceptance Testing results indicated a user satisfaction level of 92%, demonstrating that the system is easy to use and capable of supporting store operational activities. The research results show that the implementation of QR codes can accelerate the customer identification process, automate point calculations, manage the reward redemption process, and provide transaction information through WhatsApp notifications. Thus, the developed system can enhance the efficiency and accuracy of managing the customer loyalty program at Toko Bismole Elektrik.