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All Journal Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ICT : Information Communication & Technology Vygotsky: Jurnal Pendidikan Matematika dan Matematika Infotekmesin Journal of Information Systems and Informatics Buletin Poltanesa Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Padma: Jurnal Pengabdian Kepada Masyarakat Journal of Multimedia Trend and Technology Jurnal Komunikasi Global Jurnal Informatika: Jurnal Pengembangan IT Jurnal Medika: Medika Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Edu Komputika Journal Jurnal Informatika
Primandani Arsi
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Primandani Arsi; Reza Arief Firmanda; Iphang Prayoga; Pungkas Subarkah
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification Aulia Dian Agustina; Primandani Arsi; Pungkas Subarkah; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 5 No. 1 (2026): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v5i1.82

Abstract

This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.
Transformasi Pengelolaan Pariwisata Desa Tambaknegara melalui Aplikasi WISME dan Penerapan Prinsip Saptapesona Anugerah Bagus Wijaya; Zanuar Rifai; Rujianto Eko Saputro; Fiby Nur Afiana; Ranggi Praharaningtyas Aji; Primandani Arsi; Bunga Asriandhini; Rida Purnama Sari
PADMA Vol 5 No 2 (2025): JURNAL PENGABDIAN KEPADA MASYARAKAT (PADMA)
Publisher : LPPM Politeknik Piksi Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/padma.v5i2.2155

Abstract

This community service program aims to improve the management capacity of Tambaknegara Tourism Village in Banyumas through the implementation of the WISME (Wisata Manajemen Elektronik) application and the strengthening of the Saptapesona principle. The main challenges include limited digital skills, insufficient online promotion, and inconsistent service quality based on Saptapesona values. The program was carried out using a participatory approach, involving training, mentoring, implementation, and evaluation stages. The results show improved digital literacy among tourism managers and the successful integration of Saptapesona values into tourism services. In addition, a Village Digital Creative Team was formed to promote tourism through social media and the WISME platform. This program demonstrates that the integration of digital technology and Saptapesona values can strengthen sustainable community-based tourism management
Development of Escape the Virus Android-based Endless Runner Game with Local High Score and Progressive Level Features Nadia Rahmawati Asnan; Primandani Arsi; Anugerah Bagus Wijaya
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3791

Abstract

The rapid growth of Android-based games has increased the demand for applications that not only provide engaging gameplay mechanics but also incorporate features that support user experience. However, previous studies on endless runner games have primarily focused on core gameplay mechanics and system functionality, with limited attention to supporting features related to player motivation and engagement. Therefore, this study aims to design and develop Escape the Virus, a 2D Android-based endless runner game, and evaluate user responses to the implementation of a local high score system and a progressive level mechanism. This study employed a Research and Development (R&D) approach using the Multimedia Development Life Cycle (MDLC) method, which consists of the concept, design, material collecting, assembly, testing, and distribution stages. The game was developed using Unity with an obstacle avoidance concept and a scoring system based on the player's ability to avoid obstacles. System testing was conducted using Black Box Testing, while user evaluation involved 100 respondents through a Likert-scale questionnaire. The testing results indicated that all game features functioned according to the designed specifications. User evaluation showed positive responses, with average scores of 4.22 for active participation, 4.26 for playing motivation, and 4.21 for level progression. In addition, the local high score system and progressive level mechanism received positive responses from users and were perceived as features that support motivation and engagement during gameplay. These findings indicate that the integration of supporting features in an Android-based endless runner game was positively received by users during the evaluation process.
Sentiment Analysis of Google Maps Reviews on Temple Tourism in Central Java Using IndoBERT Embeddings and BiLSTM Ranggi Praharaningtyas Aji; Primandani Arsi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1589

Abstract

The rapid growth of user-generated content provides valuable insights into tourists’ perceptions of destinations. This study analyzes sentiment in Google Maps reviews of temple tourism destinations in Central Java using IndoBERT embeddings and a Bidirectional Long Short-Term Memory (BiLSTM) model. A total of 10,714 Indonesian-language reviews were collected through web scraping and processed through preprocessing, pseudo-labeling, embedding generation, and model training. To prevent data leakage, the dataset was divided into stratified training and testing sets, while Random OverSampling (ROS) was applied only to the training data. Since manually annotated labels were unavailable, sentiment categories were generated automatically using a pre-trained IndoBERT classifier. The BiLSTM model achieved 80.25% accuracy on the imbalanced dataset and approximately 95% accuracy against IndoBERT-generated pseudo-labels under balanced training conditions. Improvements in Macro F1-score and balanced accuracy indicate better recognition of minority classes. However, the results should be interpreted cautiously because pseudo-labeling and oversampling may affect performance. Overall, this exploratory study demonstrates the potential of IndoBERT and BiLSTM for Indonesian tourism sentiment analysis while highlighting the need for human-annotated data and stronger validation in future research.
Pendampingan e-Smart Early Warning untuk Peringatan Dini Banjir di Wisata Desa Karangsalam Lor Nandang Hermanto; Pungkas Subarkah; Dini Riandini; Refida Septiana Putri; Salma Ngarifatul Khofiyah; Bagus Adhi Kusuma; Primandani Arsi
Jurnal Medika: Medika Vol. 4 No. 4 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/0yyt8272

Abstract

The Juneng Mijil Community Self-Help Group (KSM) in Karangsalam Lor Village, Baturraden District, Banyumas Regency is a village tourism manager, one of which is Juneng Waterfall. The problem with the partners is that there is no technology used for early flood warning at the Juneng Waterfall and Twin Waterfall tourist sites, as well as low community literacy regarding early flood management. This activity aims to optimize the use of Android-based information technology and the Internet of Things (IoT) applied at Juneng Waterfall and Kembar Waterfall, through KSM Juneng Mijil in Karangsalam Lor Village. The implementation methods in this community service include the Pre-Implementation Stage, Implementation Stage, and Evaluation Stage. The results of the activity showed high enthusiasm among participants, as well as an increase in understanding and knowledge regarding the benefits, usage, and maintenance of the Internet of Things (IoT) and Android. This activity is important in the utilization of technology, particularly in optimal and safe flood warning systems for the community.
Public Sentiment Classification of Danantara in Social Media X Using Support Vector Machine and Random Forest Primandani Arsi; Pungkas Subarkah; Ranggi Praharaningtyas Aji
Edu Komputika Journal Vol. 12 No. 2 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i2.38254

Abstract

The increasing use of social media as a platform for public discourse provides valuable data for understanding societal responses to national strategic policies. One prominent example is the establishment of Danantara (Daya Anagata Nusantara), a sovereign wealth fund launched by the Indonesian government in February 2025. This study aims to analyze public sentiment toward Danantara using Indonesian-language posts collected from social media platform X and to comparatively evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A dataset of 1,434 public tweets was collected through web scraping and processed using text preprocessing techniques, including cleaning, tokenization, stopword removal, stemming, and TF-IDF feature extraction. Sentiment labels were generated using an Indonesian RoBERTa model and validated by a linguistic expert. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE). Model performance was evaluated using 5-fold stratified cross-validation with accuracy, precision, recall, and F1-score metrics. Experimental results show that Random Forest achieved slightly superior performance, reaching an average accuracy of 91.47%, compared to 91.06% obtained by SVM. Confusion matrix analysis indicates that RF better distinguishes neutral sentiment, while SVM performs competitively in identifying strong sentiment polarity. This study contributes by providing the first empirical comparison of classical machine learning approaches for analyzing public sentiment toward Indonesia’s sovereign wealth fund discourse, offering methodological insights and practical implications for data-driven policy evaluation using social media analytics.
Co-Authors Akhmad Mustolih Aldi Setia Pambudi Ali Nur Ikhsan Andi Dwi Riyanto Anugerah Bagus Wijaya Arbangi Puput Sabaniyah Aulia Dian Agustina Azizan Nurhakim Azizan Nurhakim Bagus Adhi Kusuma Berlilana Berlilana Bunga Asriandhini Desta Nur Efika Ardini Desty Rakhmawati Dewi Fortuna Dini Riandini Dwi Krisbiantoro Faridatun Nida Fiby Nur Afiana Iphang Prayoga Iphang Prayoga Irfan Santiko Irma Darmayanti Jali Suhaman Joko Prayogi Khoirul Abbi Rokhman Laili Nur Hidayati Mohammad imron Muhammad Hasyim Asyari Nadia Rahmawati Asnan Nandang Hermanto Nurhopipah, Ade Oman Somantri Prasetyo, Agung Pungkas Subarkah Raditya Danar Dana Ranggi Praharaningtyas Aji Refida Septiana Putri Retno Waluyo Reza Arief Firmanda Rida Purnama Sari Rizki Wahyudi Rosana Fadilla Sari Rujianto Eko Saputro Saeful Karim Salma Ngarifatul Khofiyah Septi Fajarwati Septi Oktaviani Nur Hidayah Sulis waningsih Tri Astuti Trian Damai Wawa Maisa Yuni Novita Yusyida Munsa Idah Zanuar Rifai