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All Journal Indonesian Journal of Electronics and Instrumentation Systems ELKHA : Jurnal Teknik Elektro JUITA : Jurnal Informatika Annual Research Seminar Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknologi Terpadu Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yohannes, Yohannes
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Evaluation of MobileNet-Based Deep Features for Yogyakarta Traditional Batik Motif Classification Muhdhor, Umar; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15668

Abstract

Batik is an Indonesian intangible cultural heritage that embodies profound philosophical, aesthetic, and cultural values. Yogyakarta batik motifs, such as Parang, Kawung, and Truntum, reflect Javanese wisdom and identity through distinctive geometric and floral patterns. In the digital era, artificial intelligence based image processing provides a promising approach to support the preservation and automatic recognition of traditional batik motifs. The objective of this study is to evaluate the effectiveness of MobileNet-based feature extraction combined with Support Vector Machine (SVM) classification for Yogyakarta batik motif recognition. The proposed method employs MobileNet as a convolutional feature extractor and SVM as a decision model to separate motif classes in the feature space. Experiments were conducted on 685 batik images consisting of three motif classes, with class imbalance handled using Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using weighted accuracy, precision, recall, and F1-score under five-fold cross validation. The results show that MobileNetV3Large achieved the best performance with a weighted accuracy of 98.36%, followed by MobileNetV3Small and MobileNetV4Small. Statistical significance tests using the Friedman test and Wilcoxon signed-rank analysis confirm that the performance differences among the evaluated models are statistically significant. These findings indicate that MobileNetV3 architectures provide robust and discriminative feature representations for batik motif classification on limited yet structured datasets. This study contributes a validated MobileNet–SVM framework for batik recognition and supports ongoing efforts in the digital preservation of Indonesia’s cultural heritage. Future work will explore larger motif sets and cross-dataset evaluation to further improve generalization performance.
Feature-Level Fusion of DenseNet121 and EfficientNetV2 with XGBoost for Multi-Class Retinal Classification Laksana, Jovansa Putra; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15670

Abstract

Accurate and efficient classification of retinal fundus images plays a critical role in supporting the early diagnosis of ocular diseases. However, models relying on a single deep learning backbone often struggle to capture the multi-scale and heterogeneous characteristics of retinal lesions, leading to unstable performance across visually similar disease classes. To address this limitation, this study proposes a novelty feature-level fusion framework that integrates complementary representations from DenseNet121 and EfficientNetV2-s, followed by classification using XGBoost. The fusion pipeline extracts 1024-dimensional features from DenseNet121 and 1280-dimensional features from EfficientNetV2-s, which are concatenated into a unified 2304-dimensional feature vector. Experiments were conducted on a dataset of 10,247 retinal fundus images spanning six categories: Central Serous Chorioretinopathy, Diabetic Retinopathy, Macular Scar, Retinitis Pigmentosa, Retinal Detachment, and Healthy. The proposed fusion model achieved an accuracy of 91.60%, outperforming DenseNet121 XGBoost (91.31%) and EfficientNetV2-s XGBoost (89.70%). Moreover, the fusion strategy demonstrated improved class-level stability, particularly for visually similar retinal disorders where single-backbone models exhibited higher misclassification rates. This study contributes a lightweight yet effective multi-backbone feature-level fusion approach that enhances discriminative representation and classification stability without increasing model complexity. In addition, the use of XGBoost introduces a tree-based decision mechanism that is inherently more interpretable than conventional fully connected layers, offering potential advantages for clinical analysis. Overall, the results highlight the effectiveness of multi-backbone feature fusion as a reliable strategy for automated retinal disease classification.
Performance Analysis of YOLOv11 Integrated with Lightweight Backbones (MobileNetV2, GhostNet, ShuffleNet V2) for Cigarette Detection Andreas, Kevin; Yohannes; Meiriyama
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0gjq1j10

Abstract

Cigarette object detection in indoor environments plays a vital role for enforcing smoke-free zone regulations and protecting public health from secondhand smoke exposure. This study investigates the performance of YOLOv11n architecture integrated with three lightweight backbone modifications (MobileNetV2, GhostNet, and ShuffleNet V2) for real-time cigarette detection with the aim of achieving efficiency suitable for potential deployment on resource-constrained edge devices. Comprehensive experiments were conducted using the Cigar Detection Dataset comprising 5,333 images, augmented to 8,890 samples through horizontal flipping and brightness adjustment techniques. All models were trained for 100 epochs using the SGD optimizer on an NVIDIA Tesla T4 GPU. The evaluation metrics included detection accuracy (mAP@0.5, mAP@0.5:0.95, precision, recall, and F1-score) and computational efficiency (parameters, model size, GFLOPs, and FPS). Experimental results demonstrate that the pretrained YOLOv11n baseline achieves the highest detection accuracy with mAP@0.5 of 0.8072 and precision of 0.8688. Among lightweight backbone variants, ShuffleNet V2 (0.5x) provides the most compact solution with only 2.28M parameters and a 4.73 MB model size, while ShuffleNet V2 (0.75x) offers an optimal balance between accuracy (mAP@0.5: 0.7430) and efficiency with only 0.95% accuracy degradation compared to the 1.0x variant. These findings provide practical guidance for selecting appropriate model configurations based on deployment constraints in smoke-free area monitoring systems.
Klasifikasi Motif Kain Batik Nitik Menggunakan Support Vector Machine dengan Ekstraksi Fitur EfficientNet-B0 Saputra, Dika; Yohannes, Yohannes
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3507

Abstract

Nitik Batik is an Indonesian cultural heritage with complex geometric dot patterns, yet its digitalization and preservation efforts remain limited. This study aims to develop an automatic classification system for 60 Nitik Batik motifs using a combination of EfficientNet-B0 as a feature extractor and Support Vector Machine (SVM) as a classifier. The Batik Nitik 960 Dataset was expanded from 960 to 1,920 images with rotation augmentation. Experiments were conducted with 10-fold cross-validation and evaluation on separate test data. Results show that the model without augmentation achieved 55.71% accuracy, 90.06% macro precision, 55.71% recall, and 65.29% F1-score. Blur augmentation with 30% probability reduced accuracy to 49.29% although it decreased overfitting by 6.63%. SVM parameters were set to C=0.3 and gamma=0.01 to improve regularization. This study concludes that the combination of EfficientNet-B0 and SVM is effective for multi-class batik classification, but blur augmentation is unsuitable for detail-rich textile data. Future research recommendations include exploring geometric augmentation and more advanced feature extractor architectures.Kata kunci: Nitik Batik; Image classification; EfficientNet-B0; Support Vector Machine; augmentation. AbstrakBatik Nitik merupakan warisan budaya Indonesia dengan motif geometris berbentuk titik yang kompleks, namun upaya digitalisasi dan pelestariannya masih terbatas. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk 60 motif Batik Nitik menggunakan kombinasi EfficientNet-B0 sebagai ekstraktor fitur dan Support Vector Machine (SVM) sebagai klasifikator. Dataset Batik Nitik 960 Dataset diperluas dari 960 menjadi 1.920 citra dengan augmentasi rotasi. Eksperimen dilakukan dengan skema 10-fold cross-validation dan evaluasi pada data uji terpisah. Hasil menunjukkan bahwa model tanpa augmentasi mencapai akurasi 55,71%, precision macro 90,06%, recall 55,71%, dan F1-score 65,29%. Augmentasi blur 30% justru menurunkan akurasi menjadi 49,29% meskipun mengurangi overfitting sebesar 6,63%. Parameter SVM diatur C=0,3 dan gamma=0,01 untuk meningkatkan regularisasi. Penelitian ini menyimpulkan bahwa kombinasi EfficientNet-B0 dan SVM efektif untuk klasifikasi batik multikelas, namun augmentasi blur tidak sesuai untuk data tekstur kaya detail. Rekomendasi penelitian selanjutnya adalah eksplorasi augmentasi geometris dan arsitektur feature extractor yang lebih advance.Kata kunci: Batik Nitik; Klasifikasi citra; EfficientNet-B0; Support Vector Machine; Augmentasi.
Co-Authors Ade Hendri Pandrean Adhytio Mahendra Andreas, Kevin Azarya, Philips Denny Beni Anthony Bobby Jaya Saputra Cahyati, Imelia Dwinora Calvin Oliver Saputra Celvine Adi Putra Cendy Prakarsah Dafid Dafid Dandy, Dandy Daniel Udjulawa Daniel Udjulawa Devella, Siska Dody, Muhammad Famerdi, Farhan Agung Farhan Agung Famerdi Farisi, Ahmad Febbiola Febbiola Feristyani, Indah Firda Novia Rahmawati Gerry Jeven Timoti Glen, Billy Hafiz Irsyad Hartati, Ery Indah Feristyani Jericho Jericho Jerry Setiawan Jimmy Aprilyanto Johannes Petrus Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto Laksana, Jovansa Putra Leo Chandra Leonardo Leonardo M Ezar Al Rivan Marcella, Dewi Meiriyama Meiriyama, Meiriyama Meiriyama, Meiriyama Molavi Arman Muhammad Farid Athar Muhammad Rizky Pribadi Muhammad Yudha Setiawan Muhdhor, Umar Novan Wijaya Nur Rachmat Pandi Pandi Pandi Pandi, Pandi Pandrean, Ade Hendri Philips Denny Azarya Prabowo, Adrianus Prasthio, Rial Putra, Lipi Amanda Ricky Wijaya RR. Ella Evrita Hestiandari Sahpira, Mulia Saputra, Dika Sari, Yulya Puspita Selvie, Selvie Setiawan, Jerry Sonia Sonia, Sonia Tanuwijaya, William Timoteus Ivan Sariyo Wijang Widhiarso William Hadisaputra Yoannita Yoannita Yoannita Yoannita, Yoannita Yulya Puspita Sari