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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Jurnal Informatika Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah EXPLORE Jurnal Mantik JTIM : Jurnal Teknologi Informasi dan Multimedia International Journal of Informatics and Computation INFOKUM Infotek : Jurnal Informatika dan Teknologi Jurnal Kompilasi Hukum Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi EXPLORE Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology Explore Jurnal Karya untuk Masyarakat (JKuM)
Imran, Bahtiar
Universitas Teknologi Mataram
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary

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FAKE REVIEW DETECTION ON DIGITAL PLATFORMS USING THE ROBERTA MODEL: A DEEP LEARNING AND NLP APPROACH Hadi, Zulpan; Nurkholis, Lalu Moh.; Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.355

Abstract

Fake reviews have emerged as a serious threat to the integrity of digital platforms, particularly in e-commerce and online review sites. This study explores the application of RoBERTa (Robustly Optimized BERT Approach), a transformer-based architecture optimized for natural language processing (NLP), in automatically detecting fake reviews. The methodology includes data collection from online platforms, contextual feature extraction using RoBERTa embeddings, model training through supervised learning, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The training results indicate a significant convergence trend in the training loss, while the validation loss remains relatively unstable, reflecting challenges in model generalization. Nevertheless, experimental results demonstrate that RoBERTa outperforms other approaches such as Logistic Regression PU, K-NN with EM, and LDA-BPTextCNN, achieving an accuracy of 86.25%. These findings highlight RoBERTa's strong potential in detecting manipulative content and underscore its value as an essential tool in building a transparent and trustworthy digital ecosystem.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi; Zulpahmi, M.; Zaeniah, Zaeniah; Wahyudi, Erfan
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/inf.v12i2.25340

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
A Hybrid Framework Based on YOLOv8 and Vision Transformer for Multi-Class Detection and Classification of Coffee Fruit Maturity Levels Subki, Ahmad; M. Zulpahmi; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10590

Abstract

Detection and classification of coffee cherries based on maturity levels present a significant challenge in agricultural product processing systems, primarily due to the high visual similarity among classes within a single bunch. This study aims to develop a multi-class detection and classification system for coffee cherries by integrating YOLOv8 and Vision Transformer (ViT) as a classification enhancer. The initial detection process is conducted using YOLOv8 to identify and automatically crop coffee cherry objects from bunch images. These cropped images are then re-classified using the Vision Transformer to improve prediction accuracy. The training process was carried out with a learning rate of 0.0001, a batch size of 16, and epoch variations of 50, 100, and 150. Evaluation results demonstrate that the integration of YOLOv8 and ViT significantly improves classification accuracy compared to using YOLOv8 alone. At 100 epochs, the YOLOv8+ViT model achieved an accuracy of 89.52%, a precision of 90.43%, and a recall of 89.52%, outperforming the standalone YOLOv8 model, which only reached an accuracy of 75.44%. These results indicate that the Vision Transformer effectively enhances classification performance, particularly for visually similar coffee cherry classes. The integration of these two methods offers a promising alternative solution for improving image-based multi-class classification in agriculture and other domains involving complex visual objects.
Implementation of Conditional WGAN-GP, ResNet50V2, and HDBSCAN for Generating and Recommending Traditional Lombok Songket Motifs Akbar, Ardiyallah; Karim, Muh Nasirudin; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10894

Abstract

Songket is a traditional Indonesian woven textile with profound cultural and aesthetic value, particularly in Lombok, where artisans continue to preserve its distinctive motifs. However, the creation of new designs is still carried out manually, requiring considerable time and relying heavily on the artisans’ creativity. This study proposes an integrated system that combines Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP), ResNet50V2, and HDBSCAN to automatically generate and recommend Lombok’s traditional songket motifs. The dataset consists of primary data collected directly from local artisans and secondary data from the BatikNitik public repository, thereby providing authentic yet diverse motif samples for training. CWGAN-GP is employed to synthesize motifs with stable and realistic structures across multiple epochs. Subsequently, ResNet50V2 is utilized for deep visual feature extraction, HDBSCAN for density-based clustering, and UMAP for two-dimensional visualization of motif distribution. The system successfully groups motifs into meaningful clusters, with the largest cluster containing consistent patterns of high aesthetic value. A recommendation mechanism is also developed to suggest up to five similar motifs from the original dataset within the same cluster, ensuring cultural relevance while fostering design innovation. Despite these promising outcomes, several limitations remain, such as the relatively small number of songket motif samples, variations in motif quality, and challenges during data collection including inconsistent lighting and non-uniform patterns. These factors affect both dataset consistency and generative performance. Nevertheless, this approach demonstrates the potential of artificial intelligence to support the preservation and innovation of cultural heritage by assisting artisans in creating and exploring new motifs more efficiently without losing their traditional identity.
Co-Authors AA Sudharmawan, AA Abba Suganda Girsang, Abba Suganda Ahmad Yani ahmad yani Akbar, Ardiyallah Akhmad Muzakka Alfian Hidayat Amirudin Kalbuadi Anak Agung Istri Sri Wiadnyani Andre Satriawan Atika Zahra Nirmala Baihaki, Makmun Baiq Nonik Ria Riska Baiq Nonik Ria Riska Diki Hananta Firdaus Efendi, Muhamad Masjun Erfan Wahyudi Erniwati, Surni Fachrul Kurniawan Febri, Elin Febriani Giardi, Muh Hamzah Andung Hambali Hambali Hambali Hambali Hambali, H Hamim, Lutfi Hanis Purnamasidi Hasan Basri Hendri Ramdan Hidayatullah, Beni Ari Karim, Muh Nasirudin Karina Nurwijayanti Karya Gunawan Karya Gunawan Lalu Darmawan Bakti Lalu Darmawan Bakti, Lalu Darmawan Lalu Delsi Samsumar, M.Eng. M Zulpahmi M. Zulpahmi M. Zulpahmi Mahayadi, Mahayadi Makmun Baihaki Marroh, Zahrotul Isti’anah Maspaeni Maspaeni Moch Arief Soeleman Moh. Arief Soeleman Muahidin, Zumratul Muh. Akshar Muhammad Rijal Alfian Muhammad Zohri Mutaqin, Zaenul Muttaqin, Athaur Muzakka, Akhmad Ndang, Rijalul Mujahidin Nining Putri Ningsih Nunung Rahmania Nurkholis, Lalu Moh. Pratama, Rifqy Hamdani Purwanto Purwanto Ricardus Anggi Pramunendar Riska, Baiq Nonik Ria Rosida, Sri Rudi Muslim Rudi Muslim Salman Salman Salman Salman Saputra, Dede Haris Selamet Riadi Selamet Riadi Sriasih, Sriasih Subektiningsih Subektiningsih Subki, Ahmad Suharjito Suharjito, Suharjito Suhartono Surni Erniwati Surni Erniwati Suryadi, Emi Tahrir, Muhammad Zaeniah Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zahroni, Teguh Rizali Zenuddin, Z Zulpahmi, M Zulpahmi, M. Zulpan Hadi