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All Journal MEDIA ELEKTRIKA Dinamik Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Seminar Nasional Informatika (SEMNASIF) Sistemasi: Jurnal Sistem Informasi Justek : Jurnal Sains Dan Teknologi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of New Media Technology Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Santika : Jurnal Ilmiah Sains dan Teknologi Indonesian Journal of Data and Science Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Cakrawala Repositori Imwi Community Empowerment Jurnal Computer Science and Information Technology (CoSciTech) IICS J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Pengabdian Kepada Masyarakat Abdi Putra Jurnal AKAL: Abdimas dan Kearifan Lokal
Adhitia Erfina
Nusa Putra University
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation Other

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Zero-Shot Sentiment Analysis Of DeepSeek AI App Reviews Using DeepSeek-R1 Pamungkas, Restu sri; Erfina, Adhitia; Warman, Cecep
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.303

Abstract

This study aims to evaluate the effectiveness of the Zero-Shot Learning (ZSL) approach using the DeepSeek-R1-Distill-Qwen-1.5B model in performing sentiment classification on Indonesian-language reviews of the DeepSeek AI application from the Google Play Store. A total of 2,000 unlabeled user reviews were collected and processed through instructional prompts to guide the model in classifying sentiments into three categories: positive, negative, and neutral. The model operates without fine-tuning and relies entirely on Zero-Shot Learning using Indonesian-language prompts. Out of 2,000 reviews, 1,348 were successfully classified with valid sentiment labels. Of these, 1,131 reviews (83.9%) were labeled as positive, 211 reviews (15.7%) as negative, and only 6 reviews (0.4%) as neutral. Evaluation results indicated an overall accuracy of 77.67%. The F1-Score for the positive class reached 86.66%, while the negative and neutral classes scored 33.56% and 16.66%, respectively, highlighting the performance disparity between dominant and underrepresented sentiment categories. These findings demonstrate that the DeepSeek-R1 model has strong potential in detecting positive sentiment in Indonesian without requiring additional training. However, its performance on negative and neutral sentiments remains limited, revealing the challenge of handling low-resource and imbalanced data in Zero-Shot settings. Future research should explore improved prompt engineering or multilingual adaptation to address the current limitations and enhance classification consistency across all sentiment categories
Sarcasm and Irony Detection in Lazada App Reviews Using IndoBERT Putri, Nabila; Erfina, Adhitia; Warman, Cecep
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.307

Abstract

Digital technology has reshaped consumer behavior, particularly in e-commerce, where Google Play Store reviews provide rich feedback but often include sarcasm and irony that conventional sentiment models misread. This study proposes an Indonesian sarcasm–irony detection model using IndoBERT, a transformer pre-trained on Indonesian corpora. A dataset of 1,998 Lazada app reviews was collected via web scraping and preprocessed through text cleaning, tokenization, and stopword removal with the Sastrawi library. IndoBERT was fine-tuned to classify reviews into three classes: sarcasm, irony, and literal. Performance was assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved 96.40% accuracy, with F1-scores of 0.9725 (sarcasm), 0.9675 (irony), and 0.9267 (literal). Word cloud visualizations revealed distinct lexical patterns across classes, supporting IndoBERT’s ability to capture contextual cues behind implicit sentiment. The findings indicate IndoBERT is effective for advanced opinion mining in Indonesian e-commerce, with potential applications in customer feedback monitoring, surfacing hidden complaints, and improving recommendation systems beyond surface polarity. Limitations include reliance on a single platform (Google Play) and text-only input, without modeling non-textual signals such as emojis or punctuation intensity. Future work should test cross-platform generalization, incorporate non-textual cues, and apply data augmentation to reduce class imbalance, particularly for the less frequent literal class, to improve robustness for real-world deployment
Sentiment Analysis of Public Opinion on Pi Network on Reddit Using FinBERT Wiguna, Sindy Indira; Erfina, Adhitia; Warman, Cecep
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.342

Abstract

The rapid growth of blockchain technology has led to the emergence of new cryptocurrencies, including Pi Network, which emphasizes accessibility through mobile-based mining. This study aims to answer the research question of whether FinBERT, a financial domain-specific transformer model, can effectively classify public sentiment in informal Reddit discussions related to Pi Network. FinBERT was first evaluated on a labeled financial sentiment dataset to assess its performance in a structured financial context before being applied to Reddit data. Model performance was measured using accuracy, precision, recall, and F1-score. After validation, the model was used to analyze one thousand twenty Reddit comments discussing Pi Network. Text preprocessing included cleaning, case folding, tokenization, stopword removal, stemming, and sequence standardization. The evaluation results show that FinBERT achieved an accuracy of eighty-five point ninety-eight percent on the financial validation dataset, with strong precision and recall across sentiment classes. When applied to Reddit comments, neutral sentiment was the most dominant, followed by positive and negative sentiments. Pi Network was selected as the case study because, unlike more established cryptocurrencies, it is still in an early stage of development and relies heavily on community participation, making public opinion particularly important for understanding its adoption and credibility
Perbandingan Hasil Aspect-Based Sentiment Analysis pada Ulasan Google Review Restoran Sunda di Bogor Menggunakan Fine-Tuning IndoBERT Latifah, Siti; Erfina, Adhitia; Warman, Cecep
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10417

Abstract

Penelitian ini dilakukan untuk menganalisis dan membandingkan sentimen pelanggan terhadap lima restoran Sunda di Kota Bogor menggunakan metode Aspect-Based Sentiment Analysis (ABSA) berbasis Fine-Tuning IndoBERT. Ulasan pelanggan di platform digital seperti Google Review berpengaruh besar terhadap citra dan keputusan konsumen, sementara jumlah ulasan yang besar sulit dijelaskan secara manual. Data penelitian diperoleh dari 3.232 ulasan Google Review dan diproses menjadi 3.010 data yang dikelompokkan berdasarkan lima aspek utama, yaitu makanan, pelayanan, harga, suasana, dan fasilitas. Metode Fine-Tuning IndoBERT digunakan untuk mengklasifikasikan sentimen positif, netral, dan negatif, dengan evaluasi melalui metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa model memiliki performa sangat baik dengan akurasi tertinggi sebesar 97,51% pada aspek pelayanan dan terendah 92,52% pada aspek makanan, serta nilai F1-score makro di atas 0,91. Analisis menunjukkan bahwa Bumi Aki unggul pada aspek makanan dan fasilitas, Saung Abah pada pelayanan, Saung Kuring pada harga, dan Gumati pada suasana. Hasil penelitian ini menunjukkan bahwa Fine-Tuning IndoBERT efektif dalam memahami opini pelanggan berbahasa Indonesia dan dapat menjadi acuan bagi pelaku usaha kuliner dalam meningkatkan kualitas layanan.
Co-Authors Acep Saepulrohman adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Andini Wangsa Putri Apriliani, Sinta Carti Irawan Cecep Warman Dede Sukmawan Dhea Lestari Didit Supriyadi Dinda Tasya Mahardika Dudih Gustian Egi Salehudin Basryah Elok Setianingtyas falentino sembiring Gina Syabani Hidayat, Ainul Haq Nurridha Warahmat IVANA LUCIA KHARISMA Jatmiko, Wisuda Juandi Hadi Juhdi Rosadi Kamdan Karim, Khobir Abdul Lestari, Rita Ajeng M. Havidz Alkautsar Melawati Melawati Melawati Moh. Abd. Aziz Hidayat Mohammad Daniel Gumilar Muchtar Ali Setyo Yudono Muhamad Anton Permana Muhamad Fajar Muhamad Fani Al-shufi Muhammad Ikhsan Thohir Muhammad Iqbal Jauharuzzaman Muhammad Ramdani Mupaat Mupaat, M Murweni, Ira Muslih, Muhamad Nabila, Nazwa Neng Mira Indri Anggraeni Neng Resti Wardani Neng Sri Intan Septiani Nunik Destria Arianti Pamungkas, Restu sri Pratama Haditua Reyner Siregar Putri, Nabila Retna Ayu Puspatarini Retna Ayu Puspatarini Risma Meidawati Rusdiana Rita Ajeng Lestari Saepudin, Sudin Sastradipraja, C K sihabudin sihab Siti Latifah Solihin, Hanhan Hanfiah Sri Lestari Tarikh Agusti Ijudin Tarikh Agustia Ijudin Tarikh Agustia Ijudin Theresia Septiani Nainggolan Tofik Hidayat Utami, Dian Siti Wafa Nur’aeni wiguna, sindy indira Wisuda Jatmiko Yoga Vikriansyah Yoga Vikriansyah Wijaya