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All Journal Indonesian Journal on Computing (Indo-JC) Zero : Jurnal Sains, Matematika, dan Terapan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Bunyamin
Telkom University
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Education Mathematics Physics

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Sentiment Analysis of Indonesian TikTok Review Using LSTM and IndoBERTweet Algorithm Jerry Cahyo Setiawan; Kemas M. Lhaksmana; Bunyamin Bunyamin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3911

Abstract

TikTok is currently the most popular app in the world and thus gets many reviews on the Google Play Store and other app marketplace platforms. These reviews are valuable user opinions that can be analyzed further for many purposes. Harnessing valuable analyses from these reviews can be obtained manually, which will be time-consuming and costly, or automatically with machine learning methods. This paper implements the latter with LSTM and IndoBERTweet, a derivative of BERT, using Indonesian vocabulary from Twitter post data. This research aims to determine the appropriate method to create a model that can automatically classify TikTok reviews into negative, neutral, and positive sentiments. The result demonstrates that IndoBERTweet outperforms the other, with an accuracy of 80%, whereas the LSTM accuracy is at 78%.
The Generating Indonesian Paraphrased Sentences with Verbal Predicate Replacement Bunyamin; Arie Ardiyanti Suryani
Indonesia Journal on Computing (Indo-JC) Vol. 8 No. 3 (2023): December 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.3.709

Abstract

Sentence paraphrasing is restating sentences using different diction without changing the meaning of the language. Paraphrasing sentences can be done in several ways, including synonym substitution techniques, changing sentence forms, or replacing the predicate part of sentence. This research aims to produce a paraphrased sentence generator with semantic similarities to the original sentence. The paraphrasing used in this research is to identify the verb type predicate in simple sentences using PoS Tagging. Then look for words similar to the predicate using the similarity of the word2vec model. A list of opposites antonyms is used to improve the lexical substitution results. Evaluation is done by using human judgment between the results and the original sentence. The experimental results show that of the 600 sentence datasets, 48.37% of the sentences have semantic similarities, 20.93% have semantic reductions, and 30.70% have no semantic similarities.
Sentiment Analysis of Indonesia's Free Nutritious Meal Program on Platform X (Formerly Twitter) Using IndoBERT Adiba Zahriyah Muhabbab; Bunyamin Bunyamin; Hasmawati Hasmawati
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.27629

Abstract

Public sentiment toward government programs is increasingly expressed through social media, necessitating robust quantitative evaluation methods. This study examines public sentiment toward Indonesia's Free Nutritious Meal (Makan Bergizi Gratis/MBG) program using 7,958 manually annotated Indonesian-language posts from platform X (January-August 2025), consisting of 3,752 positive, 848 negative, and 3,358 neutral tweets. Sentiment classification was conducted using IndoBERT-base-P2 and compared with a Support Vector Machine (SVM) baseline with TF-IDF features, employing class-weighted learning to address data imbalance. Model performance was evaluated using accuracy and macro F1-score, followed by paired-sample statistical testing. IndoBERT-base-P2 achieved 92% accuracy and a macro F1-score of 0.90, outperforming SVM (86% accuracy, macro F1 = 0.83). Paired t-test results indicate that this improvement is statistically significant (p < 0.05), confirming the robustness of transformer-based modeling. This study contributes methodologically by integrating contextual language modeling, imbalance-aware optimization, and inferential statistical validation within a unified sentiment analysis framework, demonstrating the quantitative advantage of transformer-based approaches for Indonesian social media policy analysis.
Co-Authors Adiba Zahriyah Muhabbab Arie Ardiyanti Suryani Hasmawati Hasmawati Jerry Cahyo Setiawan Kemas M. Lhaksmana