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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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COLONOSCOPIC POLYP SEGMENTATION USING SEGFORMER-B0 WITH A DICE-BCE HYBRID LOSS Ahmad Yani; San Sudirman; M. Zulpahmi; Emi Suryadi; Bahtiar Imran
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.476

Abstract

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, with most cases originating from early lesions such as colon polyps. Early detection through colonoscopy is essential to reduce mortality rates; however, accurate polyp identification remains challenging due to variations in shape, size, texture, and illumination conditions. This study aims to implement and evaluate the SegFormer-B0 architecture combined with a Dice-BCE hybrid loss function for polyp segmentation in colonoscopy images. The study utilized the public Kvasir-SEG dataset consisting of 1,000 colonoscopy images with pixel-level annotations. The dataset was divided into 80% training data and 20% validation data. Image preprocessing included resizing to 256×256 pixels and normalization using ImageNet statistics. The model was trained for 25 epochs using the AdamW optimizer with a learning rate of 1×10⁻⁴. Performance evaluation was conducted using Dice Coefficient, Intersection over Union (IoU), Sensitivity, and Specificity metrics. The experimental results demonstrated that the proposed model achieved a Dice Coefficient of 89.92%, Mean IoU of 81.90%, Sensitivity of 89.12%, and Specificity of 98.51%. The training process also showed stable convergence, supported by a training loss of 7.53% and validation loss of 23.30%. The findings indicate that the integration of SegFormer-B0 with the Dice-BCE hybrid loss effectively improves segmentation accuracy and stability while addressing class imbalance issues in colonoscopy images. Therefore, the proposed approach has strong potential to support computer-aided diagnosis systems for colorectal cancer screening.
A Leakage-Aware Ensemble Framework for Imbalanced Tabular Data: Mitigating SMOTE Contamination in Hotel Cancellation Prediction Gibran Maulana Syamroni; Bahtiar Imran; Surni Erniwati; Zaeniah; Wenti Ayu Wahyuni
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.514

Abstract

Predictive modeling on large-scale, imbalanced tabular data is frequently compromised by target leakage and improper resampling, leading to inflated performance metrics. While tree-based ensemble methods like Random Forest (RF) and XGBoost are widely deployed, their architectural divergence in handling complex behavioral anomalies under strict class imbalance remains underexplored. This study proposes a leakage-aware ensemble framework to mitigate SMOTE contamination and target leakage in hotel cancellation prediction. Using a rigorous CRISP-DM pipeline on 119,390 records, we applied SMOTE exclusively to the training set and engineered six behavioral features to capture non-linear contradictions, such as the counter-intuitive 99.36% cancellation rate in non-refund deposits. We systematically benchmarked RF (bagging) against XGBoost (boosting) using stratified 5-fold cross-validation, hyperparameter optimization, and loss curve monitoring. Results demonstrate that XGBoost structurally outperforms RF in minority-class detection, achieving superior Recall (0.6659), F1-Score (0.6607), and AUC-ROC (0.8657), with significantly lower variance (0.0035). Conversely, RF exhibited higher Precision (0.6588) and better cross-validation stability during hyperparameter search. Crucially, feature importance analysis revealed a structural divergence: RF prioritized temporal variables (lead_time), while XGBoost emphasized behavioral commitment signals (parking, special requests). These findings confirm that gradient boosting’s sequential residual-correction mechanism is inherently more robust than variance-reduction bagging for imbalanced tabular data containing complex, non-linear anomalies. The proposed leakage-free framework not only resolves methodological flaws in prior studies but also provides a reliable, proactive risk-scoring foundation for integrating real-time decision support systems in production environments.
PEARLVISION AI: AN AUTOMATED PEARL QUALITY GRADING SYSTEM BASED ON MORPHOLOGICAL FEATURES AND ENSEMBLE LEARNING Muh. Nasirudin Karim; Muhammad Masjun Efendi; Bahtiar Imran
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
Interpreting Text-Enriched Dual-Head Multitask Learning for Indonesian Hateful Meme Detection Using Explainable AI Selamet Riadi; Emi Suryadi; Muhamad Masjun Efendi; Bahtiar Imran; Muhammad Zamroni Uska
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.576

Abstract

Internet memes in Indonesia are frequently weaponized to disseminate implicithate speech through sarcasm and cultural nuances. Automatically detectingsuch content is computationally challenging, and existing deep learningframeworks predominantly operate as opaque black boxes, lacking decisiontransparency. This study implements and optimizes a text-enriched dual-headmultitask learning architecture utilizing IndoBERTweet to concurrently classifyhatefulness and appropriateness within the INDOMEME dataset. Ratherthan processing raw image pixels, we employ a text-enrichment strategy wherevisual semantics are transcribed into textual descriptors via Optical CharacterRecognition and vision-language captioning. To bridge the interpretability gap,we deploy Local Interpretable Model-agnostic Explanations (LIME) to decodethe internal feature attributions of the architecture. Furthermore, advancedtraining optimizations, encompassing cosine annealing, gradient accumulation,class-weighted loss, and dynamic threshold calibration, were engineered toenhance model generalization. Experimental evaluations demonstrate thatthe optimized model achieves a Macro-F1 score of 0.812 for hatefulness and0.820 for appropriateness, surpassing the established baseline. Crucially, theLIME analysis unveils a pivotal finding: despite sharing an identical textualbackbone, the hate-specific head predominantly focuses on lexicons carryingsocial agitation, whereas the appropriateness head prioritizes general normviolations. These empirical findings substantiate that multitask learning enrichessemantic representation quality, offering a transparent framework fortrustworthy content moderation.
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 Gibran Maulana Syamroni Hambali Hambali Hambali Hambali Hamim, Lutfi Hasan Basri 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, Moch Arief Muahidin, Zumratul Muh. Akshar Muh. Nasirudin Karim 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 Uska, Muhammad Zamroni wahyuni, wenti ayu Wenti Ayu Wahyuni Zaeniah 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