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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) 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 Locus Penelitian dan Pengabdian Jurnal AKAL: Abdimas dan Kearifan Lokal
Adhitia Erfina
Nusa Putra University
Aerospace Engineering 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 Restu sri Pamungkas; 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.v6i3.303

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 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
Analisis Sentimen Publik terhadap Kebijakan Pengenaan Komponen Dalam Negeri (TKDN) di Indonesia menggunakan Model Indobert Shalman Alfarisy; Adhitia Erfina; Cecep Warman
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10427

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap kebijakan Tingkat Komponen Dalam Negeri (TKDN) di Indonesia dengan menggunakan model IndoBERT. Dataset terdiri dari 7.497 komentar yang dikumpulkan dari tiga platform media sosial utama, yaitu Instagram, TikTok, dan YouTube. Sentimen pada setiap komentar diklasifikasikan ke dalam tiga kategori utama, yaitu negatif, netral, dan positif. Hasil penelitian menunjukkan bahwa distribusi sentimen bervariasi antar platform, mencerminkan perbedaan karakteristik pengguna dan pola interaksi di masing-masing media sosial. Hasil penelitian menunjukkan bahwa distribusi sentimen bervariasi antarplatform. Instagram didominasi oleh sentimen positif (48%) dan netral (44%) , TikTok oleh sentimen netral (56%) dan negatif (28%) , sementara YouTube menunjukkan dominasi sentimen positif (56%). Model IndoBERT mencapai akurasi tertinggi pada platform YouTube (87,10%), diikuti oleh Instagram (85,93%) dan TikTok (82,63%). Recall tertinggi dicapai pada sentimen negatif dan positif (1,00), namun sangat rendah pada sentimen netral, terutama pada YouTube (0,38). Faktor utama yang mempengaruhi pembentukan sentimen meliputi kekhawatiran tentang pembatasan produk asing, pandangan terkait ketidakadilan ekonomi, serta dukungan terhadap semangat nasionalisme ekonomi yang semakin berkembang di kalangan masyarakat. Penelitian ini membuktikan bahwa model IndoBERT terbukti efektif dalam menganalisis dan memahami pandangan publik di media sosial secara mendalam, sekaligus mengidentifikasi berbagai tantangan yang muncul dalam proses klasifikasi sentimen netral yang sering kali memiliki konteks ambigu dan kompleks.
QR Code Generator: Studi Kasus Orisinalitas Produk Kasepuhan Sinar Resmi Tarikh Agustia Ijudin; Adhitia Erfina
JUSIFO : Jurnal Sistem Informasi Vol 8 No 1 (2022): June
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v8i1.11866

Abstract

Kasepuhan Sinar Resmi in Cisolok District is an inseparable part of the Ciletuh Geopark Area. The village has several original products made directly by the local community, and marketed online. However, it is often used as a crime by some people who fake products made by the Kasepuhan Sinar Resmi community for personal gain. This crime is certainly very detrimental to the people of Kasepuhan Sinar Official, they do not get the slightest profit from the goods sold and the safety of the product is doubted. The purpose of this study is to create a system that guarantees the authenticity of the sold's products by the Kasepuhan Sinar Resmi community, so that sellers and buyers can get the benefit. In this study, the system development method used is an Object Oriented Programming (OOP) approach with tools such as Use Case Diagrams, Activity Diagram. From the existing problems, this research focuses on the design and construction of a QR Code Generator to detect the originality of the Kasepuhan Sinar Resmi’s product, this is conducted so that people get the original Kasepuhan Sinar Resmi’s product.
Analisis Sentimen Masyarakat Indonesia terhadap Pemindahan Ibu Kota Negara Indonesia pada Twitter Sri Lestari; Mupaat Mupaat; Adhitia Erfina
JUSIFO : Jurnal Sistem Informasi Vol 8 No 1 (2022): June
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v8i1.12116

Abstract

The relocation state capital of Indonesia raises various responses, especially from the Indonesian people. The discussion related to these issues is very interesting to study, how are the positive and negative sentiments of the Indonesian towards the government's decision. This study aims to analyze the sentiments of the Indonesian people regarding the relocation state capital of Indonesia, including the chosen name of Nusantara on Twitter. In this study, a comparison of 3 algorithms is used, namely the Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbor (KNN) algorithms. From this study, the results obtained are 1,141 positive comments, while negative sentiments are 591 comments. This shows that the Indonesian people have a positive opinion towards the new capital city of Indonesia. In the classification and model testing phase, 10-fold cross validation is used. From these tests, the SVM algorithm obtained an accuracy value of 85.71%, the Naïve Bayes algorithm obtained an accuracy value of 76.70%, the KNN algorithm obtained an accuracy value of 52.74%. This study shows that the SVM algorithm can work better than the Naïve Bayes algorithm and KNN. The accuracy value for the KNN algorithm obtains a low value, this is because the KNN algorithm is sensitive to features that are less relevant.
Co-Authors Acep Saepulrohman adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Andini Wangsa Putri Apriliani, Sinta Carti Irawan Cecep Warman Cecep Warman Dede Sukmawan Dhea Lestari Didit Supriyadi Dinda Tasya Mahardika Dudih Gustian Egi Salehudin Basryah Elok Setianingtyas falentino sembiring Gina Syabani Haikal, M. 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 Fajar Muhamad Fani Al-shufi Muhammad Anton Permana Muhammad Ikhsan Thohir Muhammad Iqbal Jauharuzzaman Muhammad Ramdani Mupaat Mupaat, M Murweni, Ira Muslih, Muhamad Nabila Putri Nabila, Nazwa Neng Mira Indri Anggraeni Neng Resti Wardani Neng Sri Intan Septiani Nunik Destria Arianti Pratama Haditua Reyner Siregar Restu sri Pamungkas Retna Ayu Puspatarini Retna Ayu Puspatarini Risma Meidawati Rusdiana Rita Ajeng Lestari Saepudin, Sudin Sastradipraja, C K Shalman Alfarisy sihabudin sihab Sindy Indira Wiguna 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