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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 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 Media Elektrik Green Engineering: Journal of Engineering and Applied Science Jurnal Informatika 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 Environmental Science 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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Implementasi Machine Learning untuk Mendeteksi Penyakit Katarak menggunakan Kombinasi Ekstraksi Fitur dan Neural Network Berdasarkan Citra Maspaeni Maspaeni; Bahtiar Imran; Alfian Hidayat; Surni Erniwati
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 2 (2025): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i2.621

Abstract

According to data from the World Health Organization (WHO), more than 1.3 billion people worldwide experience visual impairments, with Cataracts being one of the main causes. Cataracts are an eye condition characterized by clouding of the lens, which can lead to blindness if left untreated. This study aims to accurately detect Cataracts using a combination of feature extraction and neural networks, utilizing digital fundus images. The Dataset used consists of 600 fundus images divided into 80% for training and 20% for testing. The feature extraction process is performed to identify distinctive characteristics of the images relevant to Cataract diagnosis. These features are then analyzed by a neural network to recognize patterns indicative of Cataracts. To optimize performance, this study implements a hypertuning process. Before tuning, the initial model achieved an accuracy of 0.83, with precision, recall, F1-score of 0.83, and an AUC of 0.92. After four stages of hypertuning, the model’s performance improved progressively. The first tuning achieved an accuracy of 0.85, with precision, recall, and F1-score of 0.85, and an AUC of 0.93. In the second tuning, accuracy increased to 0.88, with precision of 0.87, recall of 0.88, F1-score of 0.87, and an AUC of 0.93. The third tuning maintained an accuracy of 0.88, with precision improving to 0.90, recall at 0.87, F1-score of 0.88, and an AUC of 0.94. The fourth tuning delivered the best results, with an accuracy of 0.90, precision of 0.92, recall of 0.89, F1-score of 0.90, and an AUC of 0.94. These results demonstrate that the hypertuning process plays a significant role in improving model performance.
SemetonBug: Next-Generation Machine Learning-Powered Code Analyzer for Precision Bug Detection and Dynamic Error Localization Erniwati, Surni; Imran, Bahtiar; Muahidin, Zumratul; Zaeniah, Zaeniah; Juhartini, Juhartini
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11837

Abstract

Bug detection in Python programming is a crucial challenge in software development. This research proposes SemetonBug, a machine learning-based system for automatically detecting bugs in Python code. The system utilizes a Random Forest Classifier as the main model, with features extracted from the syntactic structure of the code using an Abstract Syntax Tree (AST). The dataset consists of 200 Python files, divided into 100 files with bugs and 100 files without bugs. The model is optimized using Grid Search Cross Validation, with the best combination of n_estimators = 300, max_depth = 20, min_samples_split = 5, and min_samples_leaf = 2. Evaluation results show that the model achieves 85% accuracy, 0.84 precision, 0.87 recall, and 0.86 F1-score. The detected bugs are stored in an Excel file for further analysis. By leveraging machine learning, SemetonBug enhances efficiency and accuracy in bug identification compared to traditional rule-based methods. These findings highlight the potential of machine learning models in improving software quality and reducing coding errors automatically.
Anomaly-Based DDoS Detection Using Improved Deep Support Vector Data Description (Deep SVDD) and Multi-Model Ensemble Approach Imran, Bahtiar; Samsumar , Lalu Delsi; Subki, Ahmad; Wahyuni, Wenti Ayu; Muahidin, Zumratul; Karim, Muh Nasirudin; Yani, Ahmad; M. Zulpahmi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11863

Abstract

Distributed Denial-of-Service (DDoS) attacks remain a critical threat to network infrastructure, demanding robust and efficient detection mechanisms. This study proposes an enhanced Deep Support Vector Data Description (Deep SVDD) model for unsupervised DDoS detection using the UNSW-NB15 dataset. The approach leverages a deep encoder architecture with batch normalization and dropout to learn compact latent representations of normal traffic, minimizing the hypersphere volume enclosing benign flows. Only normal samples are used during training, adhering to the unsupervised anomaly detection paradigm. The model is evaluated against five established baselines—Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, Autoencoder, and a simple ensemble—using AUC, F1-score, and recall as primary metrics. Experimental results demonstrate that Deep SVDD significantly outperforms all baselines, achieving superior class separation, high detection sensitivity, and computational efficiency (0.0004 GFLOPs). Notably, while LOF exhibited a deceptively high F1-score, its AUC near 0.5 revealed poor discriminative capability, highlighting the risk of relying on single metrics. The ensemble approach failed to improve performance, underscoring the limitation of naive score averaging when weak detectors are included. Visualization of score distributions and ROC curves further confirms Deep SVDD’s ability to effectively distinguish DDoS from benign traffic. These findings affirm that representation learning in latent space offers a more reliable foundation for anomaly detection than traditional distance-, density-, or reconstruction-based methods. The proposed model presents a promising solution for real-time, low-overhead intrusion detection systems in modern network environments. Future work will explore adaptive ensembles, self-supervised pretraining, and deployment on edge devices.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Bahtiar Imran; Selamet Riadi; Emi Suryadi; M. Zulpahmi; Zaeniah Zaeniah; Erfan Wahyudi
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/

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.
Pelatihan Pembuatan Website Bagi Staf Desa di Desa Teratak Kecamatan Batukliang Utara Kabupaten Lombok Tengah Zaenudin; Lalu Delsi Samsumar; Amirudin Kalbuadi; Bahtiar Imran
Jurnal Karya untuk Masyarakat (JKuM) Vol 3 No 2: JULI 2022
Publisher : Universitas Tarakanita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36914/tk2qj095

Abstract

Desa Teratak merupakan desa dari beberapa desa yang ada dikecamatan Batukliang Utara Kabupaten Lombok Tengah. Desa Teratak Terdiri dari 73 Rukun Tangga Dan 12 Rukun Warga, dan 3429 KK. Desa Teratak berbatasan dengan Desa Aik Berik disebelah utara, Desa Selebung di sebelah selatan, Desa Selebung di sebelah barat, serta Desa Aiq Bukak di sebelah timur. Desa Teratak merupakan desa yang memiliki potensi kerajinan, industri di bidang perikanan, pertanian, dan pariwisata. Potensi Kerajinan yang terkenal di Desa Teratak yaitu kerajinan bambu yaitu bakul. Industri perikanan yang berkembang di Desa Teratak antara lain Nila, Koi, dan Gurami. Potensi pertanian yaitu padi. Sedangkan Potensi Wisata yaitu Geopark Rinjani, Tereng Kuning, Danau Biru, Air Terjun Elong Tune, Air terjun Serawah, dan kuliner. Selama ini desa teratak belum memiliki website desa sebagai sarana informasi kepada masyarakat, oleh karena itu dibuatlah kegiatan pelatihan ini bertujuan membuat dan menerapkan website desa teratak, pada pelatihan ini menghasilkan sebuah website yang di hosting dengan alamat alamat https://desateratak.com pemeranan website ini di harapkan dapat meningkatkan informasi kepada masyarakt dengan tepat tentang kegiatan pemerintah khususnya desa, pelanyanan kepada masyarakat dan dapat menjadi media promosi bagi desa teratak.
Comparative Analysis of the EKI-SM and BSTC Models in Detecting Fake Reviews Lalu Darmawan Bakti; Zulpan Hadi; Bahtiar Imran
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.11886

Abstract

Detecting fake reviews is essential for maintaining the credibility of e-commerce platforms and protecting consumers from misleading information. However, this study specifically contributes by providing a comparative analysis of the EKI-SM and BSTC models, highlighting the efficiency and learning stability advantages of the EKI-SM in fake review detection tasks. Using a dataset of 40,000 Amazon product reviews, we conducted experiments to evaluate both models based on accuracy, precision, recall, F1-score, and AUC score. The results show that EKI-SM achieves an accuracy of 94.98%, recall of 91.82%, and F1-score of 94.85%, slightly outperforming the BSTC, which achieves an accuracy of 94.60 %. Although the BSTC showed marginally higher precision, the difference was not significant. Beyond performance metrics, this study emphasizes the conceptual contribution of EKI-SM, which provides a better balance between precision and recall, as well as faster convergence and lower training loss. Both models achieved high AUC scores (99.41% for BSTC and 99.42% for EKI-SM), confirming their strong capability to distinguish between genuine and fake reviews. These findings indicate that EKI-SM is not only competitive in classification performance but also more efficient and stable during training, making it a more reliable approach for fake review detection
Semantic segmentation of pendet dance images using multires U-Net architecture Ramdan, Hendri; Soeleman, Moh. Arief; Purwanto, Purwanto; Imran, Bahtiar; Pramunendar, Ricardus Anggi
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1316.329-338

Abstract

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN) Karim, Muh Nasirudin; Pramunendar, Ricardus Anggi; Soeleman, Moch Arief; Purwanto, Purwanto; Imran, Bahtiar
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1317.209-217

Abstract

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Classification of stroke patients using data mining with adaboost, decision tree and random forest models Imran, Bahtiar; Wahyudi, Erfan; Subki, Ahmad; Salman, Salman; Yani, Ahmad
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1328.218-228

Abstract

A stroke is a fatal disease that usually occurs to the people over the age of 65. The treatment progress of the medical field is growing rapidly, especially with the technological advance, with the emergence of various medical record data sets that can be used in medical records to identify trends in these data sets using data mining. The purpose of this study was to propose a model to classify stroke survivors using data mining, by utilizing data from the kaggle sharing dataset. The models proposed in this study were AdaBoost, Decision Tree and Random Forest, evaluation results using Confusion Matrix and ROC Analysis. The results obtained were that the decision tree model was able to provide the best accuracy results compared to  the other models, which was 0.953 for Number of Folds 5 and 10. From the results of this study, the decision tree model was able to provide good classification results for stroke sufferers.
PEARLVISION AI: AN AUTOMATED PEARL QUALITY GRADING SYSTEM BASED ON MORPHOLOGICAL FEATURES AND ENSEMBLE LEARNING Karim, Muh. Nasirudin; Muhammad Masjun Efendi; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.472

Abstract

Conventional pearl quality assessment remains heavily reliant on manual visual inspection, which is subjective and inconsistent. This study develops PearlVision AI, an automated system for grading Lombok pearls using morphological feature extraction and ensemble learning. The dataset comprises 361 South Sea pearl images (Pinctada maxima) labeled into three commercial grades: A (n=120), AA (n=120), and AAA (n=120). The proposed pipeline integrates hybrid segmentation (Hough Circle Transform + Convex Hull) for robust object isolation, extraction of four geometric descriptors (circularity, eccentricity, area, perimeter), and comparative evaluation of four classification algorithms: Random Forest, Gradient Boosting, K-Nearest Neighbor, and SVM (RBF). Results demonstrate that Random Forest achieved optimal performance with a test accuracy of 97.22% and a 5-fold cross-validation score of 91.68%, consistently maintaining precision, recall, and F1-score >0.95 across all grade classes. Feature importance analysis revealed that size-related features (area and perimeter) contributed more significantly to class discrimination than shape-based metrics (circularity), reflecting the natural correlation between pearl diameter and commercial value in this dataset. With an inference time of <0.5 seconds per image, PearlVision AI offers an objective, efficient, and reproducible solution for reducing manual grading bias and enhancing quality control consistency in the pearl industry
Co-Authors AA Sudharmawan, AA Abba Suganda Girsang, Abba Suganda Ahmad Yani ahmad yani Akbar, Ardiyallah Akhmad Muzakka Alfian Hidayat Amirudin Kalbuadi Atika Zahra Nirmala Baihaki, Makmun Baiq Nonik Ria Riska Baiq Nonik Ria Riska Darmawan Bakti, Lalu Diki Hananta Firdaus Efendi, Muhamad Masjun Erfan Wahyudi erniwati, surni Fachrul Kurniawan Febri, Elin Febriani Giardi, Muh Hamzah Andung Hambali Hambali Hambali Hambali Hamim, Lutfi Hasan Basri Hidayatullah, Beni Ari Karim, Muh Nasirudin 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, Moch Arief Muahidin, Zumratul Muh. Akshar Muhammad Masjun Efendi Muhammad Rijal Alfian Muhammad Zohri Mutaqin, Zaenul Muttaqin, Athaur Muzakka, Akhmad Nasirudin Karim, Muh Ndang, Rijalul Mujahidin Nining Putri Ningsih Nunung Rahmania Nurkholis, Lalu Moh. Pratama, Rifqy Hamdani Purnamasidi, Hanis Purwanto Purwanto Ramdan, Hendri Ricardus Anggi Pramunendar Riska, Baiq Nonik Ria Rosida, Sri Rudi Muslim Rudi Muslim Salman Salman Salman Salman Salman San Sudirman Saputra, Dede Haris Satriawan, Andre Selamet Riadi Selamet Riadi Soeleman, Moh. Arief Sriasih, Sriasih Subektiningsih Subektiningsih Subki, Ahmad Suharjito Suharjito, Suharjito Suhartono Supardianto Supardianto Surni Erniwati Suryadi, Emi Tahrir, Muhammad wahyuni, wenti ayu Zaeniah Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zahroni, Teguh Rizali Zenuddin, Z Zulpahmi, M Zulpahmi, M. Zulpan Hadi Zulpan Hadi