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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Amanda, Khansa Putri
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Computer Science & IT

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Multi-Source Sentiment Analysis of Shopee Tokopedia Using Hybrid Machine Learning for Customer Relationship Management Optimization Kurniasari, R. Nyi Pipih; Ramadhani, Muthia; Amanda, Khansa Putri; Fathoni, Fathoni; Ibrahim, Ali
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2672

Abstract

Sentiment analysis on marketplace customer reviews is important for understanding user perceptions and supporting Customer Relationship Management (CRM) strategies. This study proposes a multi-source sentiment analysis approach based on big data from Shopee and Tokopedia platforms using Hybrid Machine Learning. The research process includes data collection, preprocessing, TF-IDF feature extraction, and classification using Support Vector Machine (SVM) and Random Forest techniques. The preprocessing stage consists of case folding, tokenization, stopword removal, and stemming to improve the quality of textual data. The TF-IDF method is used to transform text data into numerical features before classification. The evaluation results show that the SVM model achieved an accuracy of 97.49%, while the Random Forest model achieved 97.47%. The sentiment distribution indicates a strong positive bias, reflecting high customer satisfaction with marketplace services. However, negative sentiment persisted, mainly due to delivery delays, application errors, and customer service issues. The proposed hybrid approach can provide data-driven insights to improve service quality and support decision-making in CRM strategies.
SECI and K-Means Integration for Public Sector Logistics Budget Efficiency Surya, Leiden Fauzi Yoka; Kurniasari, R. Nyi Pipih; Ramadhani, Muthia; Amanda, Khansa Putri; Tania, Ken Ditha; Kurniawan, Dedy; Rifai, Ahmad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2695

Abstract

Suboptimal management of office supplies causes significant budget waste in the public sector. This study addresses this issue by integrating the K-Means algorithm and the Socialization, Externalization, Combination, dan Internalization (SECI) knowledge management model to optimize logistics budget efficiency at the Palembang DPRD Secretariat. K-Means was utilized to partition the 2025 supply expenditure data into three priority clusters based on budget absorption and demand frequency. To ensure analytical outputs influence managerial decisions, K-Means was positioned as the primary explicit-to-explicit transformation engine within the SECI combination phase. The integration successfully transformed raw transaction data into a data-driven Standard Operating Procedure (SOP). Quantitative analysis reveals that a small subset of items in Cluster C3 accounts for a disproportionately high share of total budget absorption. Consequently, supervision can now strictly target these high-budget anomalies such as the Rp52.2 million spent on specific folio paper significantly reducing potential leakage and improving allocation efficiency. The main scientific contribution of this study is a novel framework that bridges mathematical data extraction and managerial policy formulation. This integrated approach is proven to measurably enhance regional budget efficiency.
Co-Authors Ahmad Rifai Ali Ibrahim Dedy Kurniawan Fathoni Fathoni Ken Ditha Tania Kurniasari, R. Nyi Pipih Ramadhani, Muthia Surya, Leiden Fauzi Yoka