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Sarcasm and Irony Detection in Lazada App Reviews Using IndoBERT Nabila Putri; Adhitia Erfina; Cecep Warman
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 Sindy Indira Wiguna; Adhitia Erfina; Cecep Warman
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
PERANCANGAN SISTEM INFORMASI PERUMAHAN MENGGUNAKAN FRAMEWORK ZACHMAN (Studi Kasus : Perumahan Tiara Regency) Maulana, Rizki; Baturohmah, Habi; Warman, Cecep
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6271

Abstract

Perumahan tiara regency memiliki kekurangan keefektivitasan pembookingan unit salah satu nya adalah pelanggan harus datang langsung mensurvei unit hal ini membuat costomer costomer yang tidak memiliki banyak waktu dan menginginkan pembookingan yang cepat menjadi terhalang oleh karena itu penelitian ini bertujuan untuk merancang sistem informasi manajemen yang mendukung pelayanan menggunakan. Solusi yang di lakukan adalah merancang sistem infromasi pembookingan yang nanti akan di gunakan untuk pembookingan unit secara online. Metode Zachman digunakan untuk melakukan perancangan, dengan fokus pada studi kasus pengembang Perumahan Tiara Regency Sukabumi. Metode Zachman digunakan untuk menganalisis dan merancang struktur informasi yang dibutuhkan oleh organisasi.