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All Journal Dinamik Jurnal Pilar Nusa Mandiri Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Progresif: Jurnal Ilmiah Komputer JIIP (Jurnal Ilmiah Ilmu Pendidikan) Indonesian Journal of Data and Science J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Engineering and Applied Technology (IJEAT) Jurnal Saintifik International Journal of Health, Engineering and Technology West Science Business and Management Jurnal Riset Manajemen Jurnal Pengabdian Bersama Masyarakat Indonesia Journal of International Multidisciplinary Research Jurnal Ilmiah Manajemen dan Akuntansi
Cecep Warman
Universitas Nusa Putra
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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 Adhitia Erfina Anti Aprianti Apriliani, Sinta Debataraja, Lorhenson Desty Endrawati Subroto Dudih Gustian Egit Pudarwati Fachri, Saeful Fathurrohman Fathurrohman Febrianata, Eka Fidziah Fikri Faisal Hibatullah Giri Giri Guli Guli Habi Baturohmah Kahpi, Heri Sapari Kusumawati, Retno Ryani Moh Ival Alpian S Muhamad Renaldi Aripin Mutaqin, Reihan Nabila, Nazwa Nurjaman, Moh. Pamungkas, Restu sri Putri, Nabila Rizki Maulana, Rizki Saepudin, Sudin Sihabudin Sihabudin Siti Latifah Sucihati, Sucihati Suharno Suharno Wardani, Neng Resti wiguna, sindy indira Yayatillah Rubiati Yoga Vikriansyah Wijaya Yuliawati