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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Jurnal INFOTEL
Sri Suryani Prasetiyowati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
IDENTIFYING POSSIBLE RUMOR SPREADERS ON TWITTER USING THE SVM AND FEATURE LEVEL EXTRACTION Claudia Mei Serin Sitio; Yuliant Sibaroni; Sri Suryani Prasetiyowati
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.868

Abstract

In everyday life, many events occur and give rise to various kinds of information, which are also rumors. Rumors can cause fear and influence public opinion about the event in question. Identifying possible rumor spreaders is extremely helpful in preventing the spread of rumors. Feature extraction can be done to expand the feature set, which consists of conversational features in the form of social networks formed from user replies, user features such as following, tweet count, verified, etc., and tweet features with text analysis such as punctuation and sentiment values. These features become instances used for classification. This study aims to identify possible spreaders of rumors on Twitter with the SVM classification model. This instance-based classification algorithm is good for linear and non-linear classification. In the non-linear classification, additional kernels are used, such as linear, RBF, and sigmoid. The research focuses on getting the best model with high performance values from all the models and kernel functions that have been defined. It was found that the SVM classification model with the RBF kernel has a high overall performance value for each data combination with a ratio of the amount of data is 1:1 or the difference is very large. This model gives accurate results with an average of 97.02%. With a wide distribution of data, the SVM classification model with the RBF kernel is able to map the data properly.
WORD EMBEDDING OPTIMIZATION IN SENTIMENT ANALYSIS OF REVIEWS ON MYTELKOMSEL APP USING LONG SHORT-TERM MEMORY AND SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE Haziq, Muhammad Raffif; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2498

Abstract

Telkomsel is one of the internet service provider companies that has a mobile-based application called MyTelkomsel which functions to facilitate users in conducting online services independently. Users of the application certainly have their own responses about the application, so that users can provide responses to the application. Therefore, sentiment analysis can be one of the solutions to find out public sentiment towards the application. In this research, the author builds a system for sentiment analysis using word embedding Word2vec, GloVe, FastText to get word representation in vector form with classification using Long Short-Term Memory (LSTM) combined with Synthetic Minority Over-sampling Technique (SMOTE) which can handle data imbalance. The data used comes from user reviews of the MyTelkomsel application found on the Google Play Store. This study compares the performance of several word embedding in LSTM and LSTM-SMOTE classifiers. The results showed the results show that the performance of three-word embedding on the LSTM model is superior compared to the LSTM-SMOTE model. Overall, it was found that the combination of FastText and LSTM gave the best performance compared to the other five combinations with an accuracy value of 89.11%.
LEARNING RATE AND EPOCH OPTIMIZATION IN THE FINE-TUNING PROCESS FOR INDOBERT’S PERFORMANCE ON SENTIMENT ANALYSIS OF MYTELKOMSEL APP REVIEWS Zaidan, Muhammad Naufal; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2396

Abstract

With the advancement of the digital era, the growth of mobile applications in Indonesia is rapidly increasing, particularly with the MyTelkomsel app, one of the leading applications with over 100 million downloads. Given the large number of downloads, user reviews become crucial for improving the quality of services and products. This study proposes a sentiment analysis approach utilizing the Indonesian language model, IndoBERT. The main focus is on optimizing the learning rate and epochs during the fine-tuning process to enhance the performance of sentiment analysis on MyTelkomsel app reviews. The IndoBERT model, trained with the Indo4B dataset, is the ideal choice due to its proven capabilities in Indonesian text classification tasks. The BERT architecture provides contextual and extensive word vector representations, opening opportunities for more accurate sentiment analysis. This study emphasizes the implementation of fine-tuning with the goal of improving the model's accuracy and efficiency. The test results show that the model achieves a high accuracy of 96% with hyperparameters of batch size 16, learning rate 1e-6, and 3 epochs. The optimization of the learning rate and epoch values is key to refining the model. These results provide in-depth insights into user sentiment towards the MyTelkomsel app and practical guidance on using the IndoBERT model for sentiment analysis on Indonesian language reviews.
Geo-Sentiment Analysis of Public Opinion of X Users towards the Documentary Film Dirty Vote using the Bidirectional Long Short-Term Memory Method Salsabila, Syifa; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4195

Abstract

Presidential elections held every five years, often generates significant public discourse. The 2024 presidential election saw the release of the documentary Dirty Vote, which raised allegations of electoral fraud and sparked polarized opinions on social media, especially on X. This study aims to analyze public sentiment toward Dirty Vote using geo-sentiment analysis and the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Data were collected from geotagged tweets, with sentiment classified as positive, negative, or neutral. The research explored various data processing techniques, including TF-IDF for feature extraction, FastText for feature expansion, and balancing methods like SMOTE and class weighting to address data imbalance. Results showed that the baseline Bi-LSTM model achieved an accuracy of 71.57% and an F1-Score of 74.05%. When enhanced with TF-IDF and FastText, accuracy increased to 77.07%, though the F1-Score dropped slightly to 72.95%. Applying SMOTE resulted in a decrease in accuracy to 76.45%, but significantly improved the F1-Score to 74.93%. Exploratory data analysis revealed that negative sentiment was most concentrated in Java Island, particularly Jakarta, and peaked during February 2024, coinciding with the documentary's release and the election period. This study significantly contributes to understanding how geographic locations influence public opinion on sensitive political issues. A lack of understanding of geographically-based sentiment patterns can hinder identifying regional needs, leading to poorly targeted policies. By integrating data analysis methods with geographical approaches, this research provides deep insights for designing more effective, data-driven public intervention strategies and supports policymaking that is more responsive to the dynamics of public opinion.
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Diyas Puspandari Ekaputra, Muhammad Novario Elqi Ashok Erna Sri Sugesti Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Jauzy, Muhammad Abdurrahman Al Juniardi Nur Fadila Lesmana, Aditya Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Mitha Putrianty Fairuz Muh. Kiki Adi Panggayuh Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Fajar Riani Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahmanda, Rayhan Fadhil Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Sinaga, Astria M P Siti Uswah Hasanah Sri Harini Sri Harini Suhendar, Annisya Hayati Winico Fazry Wira Abner Sigalingging Yahya, Arnasli Yuliant Sibaroni Zaidan, Muhammad Naufal