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Rosmawan, Hendri
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Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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IMPLEMENTASI SVM-PSO DALAM ANALISIS SENTIMEN PENGGUNA GOOGLE PLACE REVIEW DI MARKAS CAFE Rosmawan, Hendri; Setyanto, Setyanto; Wibowo, Ferry Wahyu
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 2 No 4 (2024): OKTOBER
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v2i4.230

Abstract

Sentiment analysis plays an important role in understanding customer perceptions of businesses, allowing companies to respond more effectively to customer needs and satisfaction. This study aims to evaluate the performance of a Support Vector Machine (SVM) model optimized with Particle Swarm Optimization (PSO) in classifying the sentiment of user reviews on Markas Cafe. The dataset consists of 1,533 user reviews categorized into three sentiment classes: positive, neutral, and negative. The optimization process using PSO is used to find the optimal SVM parameters. The results showed that the SVM-PSO model achieved an accuracy of 87.7% and an Area Under Curve (AUC) of 0.85, with the best performance on positive sentiment (94.7% precision and 92.8% recall). Although the model showed good ability in detecting positive sentiments, the results for neutral and negative sentiments indicated the need for further improvement. This study confirms the effectiveness of SVM-PSO in sentiment analysis and suggests this approach can be utilized by businesses to improve marketing and customer service strategies based on user feedback.
PEMANFAATAN CLOUD COMPUTING UNTUK KOLABORASI RISET DAN PENGEMBANGAN SOLUSI PENDIDIKAN DI INSTITUT MAHARDIKA CIREBON Ramadhan, Syaiful; Sutriyono, Ade; Rosmawan, Hendri
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 4 No 1 (2026): JANUARI
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v4i1.338

Abstract

The shift towards digital transformation in higher education demands effective collaborative tools for research and learning. This study aims to analyze the utilization of cloud computing for research collaboration and the development of educational solutions among lecturers and students at Institut Mahardika, Cirebon. This research uses a quantitative descriptive method with a survey approach, involving 85 respondents. Data were collected through questionnaires and analyzed using descriptive statistics. The results show that the adoption of cloud computing significantly increases research productivity and cross-disciplinary collaboration efficiency. The data indicates an 82% increase in the speed of data sharing and co-authoring processes. Furthermore, cloud platforms enable the rapid development of educational solutions, such as centralized e-learning repositories. In conclusion, cloud computing is not just a storage medium but a vital catalyst for academic innovation. This study confirms that proper integration of cloud infrastructure, supported by adequate digital literacy, can solve spatial and temporal barriers in academic environments
ANALISIS PERBANDINGAN KINERJA INDOBERT DAN MULTILINGUAL BERT (MBERT) PADA KLASIFIKASI SENTIMEN ULASAN FITUR SHOPEE LIVE DI GOOGLE PLAY STORE Rosmawan, Hendri; Ardiyansyah, Ardiyansyah; Azis, Muhamad Fuad; Rafsyahid, Jaisyi Bagir
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 4 No 2 (2026): APRIL
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v4i2.376

Abstract

The Shopee Live streaming e-commerce feature generates a large volume of user reviews on the Google Play Store, yet the text is dominated by informal Indonesian, slang, and code-mixing, making it difficult to analyze using conventional sentiment analysis approaches. This study analyzes and compares the performance of IndoBERT and Multilingual BERT (mBERT) in classifying sentiment of Shopee Live feature reviews into three classes: Positive, Negative, and Neutral. The study follows the CRISP-DM framework, encompassing web scraping of reviews from January-June 2025, filtering, text preprocessing, manual labeling, dataset splitting (80:10:10), fine-tuning both models using HuggingFace Transformers on Google Colaboratory, and evaluation using confusion matrices. The cleaned dataset comprised 5,240 reviews with a class distribution of 44.96% Positive, 35.79% Negative, and 19.25% Neutral. Evaluation results on the test set show that IndoBERT achieved an Accuracy of 89.7%, Precision of 89.5%, Recall of 89.7%, and F1-Score of 89.6%, outperforming mBERT, which achieved an Accuracy of 84.3%, Precision of 84.5%, Recall of 84.3%, and F1-Score of 84.4%. Both models exhibited lower performance on the Neutral class compared to the Positive and Negative classes. These findings confirm that IndoBERT is a more optimal model for sentiment classification of informal Indonesian-language e-commerce reviews, and can be recommended as an AI-based analytical instrument for product development teams to automatically and continuously monitor user sentiment.
Co-Authors Ardiyansyah Ardiyansyah Azis, Muhamad Fuad Ferry Wahyu Wibowo Rafsyahid, Jaisyi Bagir RAMADHAN, SYAIFUL Setyanto Setyanto Sutriyono, Ade