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All Journal Jurnal Sosioteknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Mulyawan, Rifqi
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Computer Science & IT Engineering Social Sciences

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Comparison of Text Vectorization Methods for IMDB Movie Review Sentiment Analysis Using SVM Mulyawan, Rifqi; Naparin, Husni; Fatihia, Wifda Muna
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10372

Abstract

Sentiment Analysis is a scientific study in the field of Machine Learning that focuses on classifying opinions expressed in text. IMDb is a platform widely used to provide information and share viewpoints among moviegoers worldwide, where audience reactions often serve as a benchmark for a movie’s success. This research aims to classify positive and negative sentiments by applying and evaluating the effectiveness of Support Vector Machine (SVM) with four different feature representation methods: (a) Bag of Words (BoW), (b) TF-IDF, (c) Word2Vec, and (d) Doc2Vec. After preprocessing the textual data, each method was employed to extract features for model training. The experimental results demonstrate that the combination of SVM with Word2Vec achieved the best overall performance with an F1-Score of 0.8607 and an Accuracy of 0.8607, while also being the fastest in training time (75.0s). In comparison, BoW reached an F1-Score of 0.8219, TF-IDF achieved 0.8520, and Doc2Vec obtained 0.8440. These findings highlight that Word2Vec provides the most effective feature representation for sentiment classification using SVM in this study.
Etika dan Desain Kerja Berpusat pada Manusia dalam Integrasi AI pada Ekosistem Platform Industry 5.0 Miftahurroziqin, Miftahurroziqin; Wardhani, Dyah Febria; Mulyawan, Rifqi
Jurnal Sosioteknologi Vol. 24 No. 3 (2025): NOVEMBER 2025
Publisher : Fakultas Seni Rupa dan Desain ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/sostek.itbj.2025.24.3.9

Abstract

This study proposes a conceptual-analytical framework for human-centric AI integration in Industry 5.0 by employing a Systematic Literature Review (SLR) and Applied Framework Analysis (AFA). A structured PRISMA-based screening process was applied to ensure transparency in identifying and selecting 48 scholarly sources that inform the framework. The review reveals three foundational dimensions enabling responsible AI adoption: ethical governance grounded in transparency and accountability, inclusive innovation addressing bias and digital inequality, and futureready human capital oriented toward capability enhancement. These dimensions are synthesized into a sociotechnical framework that bridges normative humancentric principles with organizational practice. To demonstrate its analytical utility, the framework is applied to Gojek Indonesia as an illustrative case using publicly available secondary information, without claiming empirical verification or access to proprietary algorithmic processes. The analysis indicates partial alignment between Gojek’s documented initiatives and human-centric principles, particularly in interface transparency, communication design, and worker-support mechanisms, while also exposing persistent tensions related to power asymmetry and limited algorithmic visibility. The study concludes that although secondary evidence suggests opportunities for human-centric implementation, comprehensive evaluation requires multi-method empirical research capable of capturing lived experiences and internal decision-making structures.
Co-Authors Fatihia, Wifda Muna Miftahurroziqin, Miftahurroziqin Naparin, Husni Wardhani, Dyah Febria