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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Minfo Polgan (JMP) Journal of Technology and Computer (JOTECHCOM) LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics International Journal of Educational Insights and Innovations (IJEDINS)
Manza, Yuke
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Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : Journal of Technology and Computer (JOTECHCOM)

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AI for MSMEs: Smart Solutions to Optimize Operations and Marketing Manza, Yuke
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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This research investigates the transformative potential of Artificial Intelligence (AI) for Micro, Small, and Medium Enterprises (MSMEs) in optimizing operations and marketing. Employing a mixed-methods approach—combining quantitative surveys (n=100+) and in-depth qualitative interviews (n=15-20)—the study reveals a significant positive correlation between AI adoption and enhanced operational efficiency, evidenced by average reductions of 25% in data processing time and 15% in inventory management. Furthermore, AI substantially boosts marketing effectiveness, leading to a 30% increase in audience reach and an 18% rise in sales conversion rates. Despite these clear benefits, MSMEs face considerable barriers to AI adoption, primarily financial constraints (65% of respondents) and limited digital literacy (58%). To address these challenges, the research proposes an affordable and easy-to-implement AI framework emphasizing cloud-based solutions (SaaS) and comprehensive training programs. The findings underscore AI as a crucial driver for MSME competitiveness and recommend concerted efforts from government and industry stakeholders to foster a supportive ecosystem. This study bridges the digital divide, offering evidence-based recommendations for resilient, efficient, and sustainable MSMEs in the digital era.
Text Classification Using TF-IDF and Naïve Bayes: Case Study of MyXL App User Review Data Nurhayati, Nurhayati; Hartimar, Lima; Manza, Yuke; Siregar, Kiki Putriani
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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The MyXL application, developed by leading Indonesian operator XL Axiata, allows customers to independently manage their telecommunication services. However, a significant volume of negative user reviews necessitates a deeper analysis of user sentiment. This research classifies MyXL app reviews using the TF-IDF (Term Frequency-Inverse Document Frequency) method for feature extraction and the Naïve Bayes algorithm for sentiment classification, implemented via a Python-based GUI. The study's objective is to categorize reviews into positive, negative, and neutral sentiments. A dataset of 1000 user reviews from Kaggle underwent comprehensive preprocessing—including text cleaning, normalization, tokenization, stopword removal, and stemming—before conversion into a numerical representation using TF-IDF. The classification model, built with the Naïve Bayes algorithm, was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an accuracy of 61.5%. This finding demonstrates that combining TF-IDF and Naïve Bayes is effective for classifying sentiment in Indonesian text reviews, particularly within the mobile app domain. Furthermore, the methodology shows clear potential for development into a large-scale and automated user opinion analysis system.
Co-Authors Ashari, Annisa Hartimar, Lima M. Rhifky Wayahdi Ndruru, Agus F.S. Negoro, Wahyu Saptha Nurhayati Nurhayati Rambe, Lima Hartimar Rika Rosnelly, Rika Setiawan, Adil Siregar, Kiki Putri Ani Siregar, Kiki Putriani Suhada WD, Muhammad Yusof, Khairunnisa M.