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Nanik Suciati

Departemen Informatika Institut Teknologi Sepuluh Nopember

Author-ID : 387460

Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Optic Nerve Head Segmentation Using Hough Transform and Active Contours Handayani Tjandrasa; Ari Wijayanti; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.833

Optic nerve head is part of the retina where ganglion cell axons exit the eye to form the optic nerve. Glaucomatous changes related to loss of the nerve fibers decrease the neuroretinal rim and expand the area and volume of the cup. Therefore optic nerve head evaluation is important for early diagnosis of glaucoma. This study implements the detection of the optic nerve head in retinal fundus images based on the Hough Transform and Active Contour Models. The process starts with the image enhancement using homomorphic filtering for illumination correction, then proceeds with the removal of blood vessels on the image to facilitate the subsequent segmentation process. The result of the Hough Transform fitting circle becomes the initial level set for the active contour model. The experimental results show that the implemented segmentation algorithms are capable of segmenting optic nerve head with the average accuracy of 75.56% using 30 retinal images from the DRIVE database.Optic nerve head segmentation using hough transform and active contours

Automated Facial Wrinkle Segmentation for Dermatological Assessment Using VGG-Based U-Net with Hybrid Augmentation Setiawan, Wahyu Fajar; Suciati, Nanik
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5561

Manual and automated facial wrinkle segmentation remains challenging due to the fine-grained nature of wrinkles, uneven distribution across facial regions, severe class imbalance (~2% wrinkle pixels), and sensitivity to lighting variations—limiting the reliability of existing dermatological assessment tools. This study aims to evaluate VGG transfer learning with hybrid augmentation strategies for U-Net-based automated facial wrinkle segmentation. Using the FFHQ-Wrinkle dataset comprising 1,000 manually annotated high-resolution images (1024×1024 pixels), this study systematically evaluates three U-Net variants (Baseline, VGG16-based, VGG19-based) across four augmentation strategies: no augmentation, hierarchical image enhancement (CLAHE, gamma correction, bilateral filtering), geometric transformation (rotation, translation, shear, zoom, flip), and hybrid combination. A multi-component loss function integrating Focal Loss, Dice Loss, IoU Loss, and Boundary Loss addresses class imbalance while optimizing both region overlap and edge localization. The proposed VGG19-based U-Net with hybrid augmentation achieves state-of-the-art performance: Dice coefficient of 0.6585, IoU of 0.4970, precision of 0.6186, recall of 0.7344, and Boundary F1 of 0.9185. Key findings demonstrate that VGG19 transfer learning provides +21.54% Dice improvement over Baseline U-Net with 12.7-fold reduction in overfitting, while hybrid augmentation yields +4.87% Dice improvement with +2.24% synergistic gain beyond individual strategies. This research advances automated dermatological tools for precise skin health assessment, reducing subjectivity in clinical evaluations and providing actionable guidelines for practitioners developing automated wrinkle analysis systems.

Improving Vegetation Encroachment Detection in Powerline Areas Using EfficientNet-Based U-Net Semantic Segmentation Jannah, Alissa Velia Royhatul; Suciati, Nanik
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5863

Vegetation growing beyond safe limits has the potential to pose a threat to safety and the reliability of overhead powerlines, as well as cause financial losses for infrastructure providers. Identifying potential obstructions to overhead powerlines is crucial for addressing these issues. This study proposes the EFF-UNET semantic segmentation technique on the VEPL dataset to identify areas of overlap between vegetation and overhead powerlines by overlaying the two models. Visually, overhead powerlines have a thin pixel structure and are difficult to distinguish from the background or vegetation, whereas the feature extraction process in the U-Net encoder can degrade small objects due to progressive resolution loss. Modifications to the encoder in the baseline U-Net architecture utilize the EfficientNet family by comparing variants B0 through B7 to produce the best model. EfficientNet specifically employs compound scaling to optimize the network’s resolution, depth, and width during feature extraction, thereby preserving information integrity during downsampling. Experimental results demonstrate the superiority of EfficientNetB7 through a measured trade-off compared to other models, where for vegetation segmentation, this model achieves an IoU of 0.9824, Accuracy of 0.9905, Dice of 0.9911, and Loss of 0.0089. Meanwhile, for powerline segmentation, the results show an IoU of 0.9153, Accuracy of 0.9978, Dice of 0.9558, and Loss of 0.0442. Based on these findings, EFF-UNET model successfully addresses the shortcomings of conventional models in preserving feature representation. This model is capable of improving the performance of vegetation and overhead powerlines segmentation to produce precise encroachment areas, thereby enabling accurate on-site infrastructure inspections.

Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript Muhammad 'Arif Faizin; Nanik Suciati; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1256

The imbalance of Javanese script data in ancient manuscript recognition poses a challenge due to the limited availability of data. A potential approach to addressing this issue is the use of Generative Adversarial Networks (GAN). This study evaluates the effectiveness of synthetic data generated using Enhanced Balancing GAN (EBGAN) in mitigating data imbalance. Various evaluation scenarios are conducted, including: (i) assessing the impact of syn-thetic data as augmentation, (ii) evaluating the sufficiency of synthetic data for recognition models, (iii) analyzing minority class oversampling with different selection strategies, and (iv) evaluating model generalization through cross-validation. Quantitative analysis of the generated synthetic data, based on Fréchet Inception Distance (FID) and Structural Similarity Index (SSIM), as well as visual assessment, indicates that the quality of synthetic data closely resembles real data. Additionally, experimental results demonstrate that combining real and synthetic data improves accuracy, precision, recall, and F1-score. The oversampling strategy for synthetic data has proven effective in meeting the data sufficiency requirements for training recognition models. Meanwhile, selecting minority classes and determining threshold values based on percentage, distribution, and model performance in oversampling can serve as guidelines for enhancing script recognition performance. Compared to previous methods, the use of EBGAN has been shown to produce more diverse synthetic data with better visual quality. However, further research is still needed to optimize GAN performance in supporting script recognition.

Handling Imbalance in Javanese Manuscript Character Dataset using Skeleton-based Balancing Generative Adversarial Networks Muhammad 'Arif Faizin; Nanik Suciati; Chastine Fatichah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6572

Javanese script is an important part of Indonesia’s cultural heritage, representing cultural values from the past. However, recognizing and classifying Javanese characters within manuscripts is challenging due to the limited availability of data and uneven distribution of character classes. The decline in formal use of Javanese script has drastically reduced the pool of manuscript samples, causing certain characters to appear rarely and skewing class frequencies. Existing methods that utilize Generative Adversarial Networks (GANs) attempt to address this problem. However, they often struggle to generate characters that are both consistent and visually accurate in terms of structural details. To address these issues, this study introduces a skeleton-based balancing GAN (SkelBAGAN), which improves the structural details of the previous method for generating characters. The proposed method introduces three main enhancements: (i) a layer for extracting the character skeleton structure, (ii) an optimized pretrained network using an autoencoder for learning the skeleton distribution, and (iii) refinement of the evaluation function, preserving both the distribution and structural fidelity in the adversarial process. The performance of the proposed model is evaluated against previous methods using the Fréchet Inception Distance (FID) to assess distribution quality and the Structural Similarity Index Measure (SSIM) to evaluate structural fidelity. The results indicate that the proposed methods outperform previous methods in balancing the FID and SSIM metrics. The integration of all enhancements in SkelBAGAN achieves the lowest FID, indicating improved generative quality while maintaining competitive SSIM values. The qualitative study indicates that SkelBAGAN outperforms previous methods in character generation. These results highlight how the skeleton-based improvement of the quality of generated characters enhances the recognition performance for underrepresented Javanese characters in imbalanced datasets. Ultimately, this work contributes to the broader effort to preserve the Javanese script as a vital element of Indonesia’s cultural identity.

Attention-enhanced U-Net with VGG backbone for robust facial wrinkle segmentation under variable illumination and pose conditions Wahyu Fajar Setiawan; Nanik Suciati
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2394

Facial wrinkle segmentation is critical for automated dermatological assessment, yet existing deep learning methods exhibit significant performance degradation under real-world illumination and pose variations, restricting practical clinical deployment where imaging conditions cannot be controlled. This study proposes a novel robustness-oriented segmentation framework that integrates three synergistic components: (1) attention-enhanced U-Net architectures with strategically frozen VGG16/VGG19 backbones enabling hierarchical feature transfer, (2) a dual augmentation strategy coupling geometric transformations for pose invariance with a four-level photometric enhancement pipeline for illumination robustness, and (3) a weighted mask fusion mechanism combining expert annotations with weak supervision labels. Three architectures (baseline Attention U-Net, VGG16, and VGG19 variants) are trained on 1,000 FFHQ-Wrinkle images and systematically evaluated across four augmentation strategies under nine challenging deployment conditions, including low light, high contrast, noise, head tilts, and perspective shifts. The proposed VGG19 Attention U-Net with combined augmentation achieves a Dice coefficient of 0.6533 and IoU of 0.4931, outperforming the best existing method (Striped WriNet) by +4.26% in Dice and +5.89% in IoU under identical re-implemented training conditions. The model retains 97.82% of its original performance across all nine perturbation conditions (robustness score: 0.6391), representing a 10.4% robustness improvement over the non-augmented baseline. These results demonstrate that the synergistic combination of attention mechanisms, transfer learning, and dual augmentation produces clinically viable robustness for facial wrinkle segmentation.

Co-Authors Adni Navastara, Dini Agus Eko Minarno Agus Priyono Agus Zainal Arifin Agus Zainal Arifin Ahmad Saikhu Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Akwila Feliciano Pradiptatmaka Alam Ar Raad Stone Amelia Devi Putri Ariyanto Amirullah Andi Bramantya Anggun Dwi Rizkika Anny Yuniarti Antonius Kevin Wiguna Ardian Yusuf Wicaksono Ari Wijayanti Aris Fanani Arrie Kurniawardhani Arsy Bilahi Tama Ary Mazharuddin Shiddiqi Arya Yudhi Wijaya Atika Faradina Randa Atikah, Luthfi Avin Maulana Awangditama, Bangun Rizki Ayu Kardina Sukmawati Ayu Septya Maulani Baso, Budiman Bryan Nandriawan Bui, Ngoc Dung Chastine Fatichah Chilyatun Nisa' Daffa Muhamad Azhar Damayanti, Putri Daniel Sugianto Darlis Herumurti Davin Masasih Diana Purwitasari Dimas Rahman Oetomo Dini Adni Navastara, Dini Adni Dion Devara Aryasatya Eko Prasetyo Eva Yulia Puspaningrum Evelyn Sierra Faishal Azka Jellyanto Fajar Astuti Hermawati Fajar Setiawan Fandy Kuncoro Adianto Fandy Kuncoro Adianto Febri Liantoni, Febri Feiticeira Zulkarnaen Fiqey Indriati Eka Sari Fitri Bimantoro Ginardi, R.V. Hari Gou Koutaki Gurat Adillion, Ilham Hafidz, Abdan Handayani Tjandrasa Hani Ramadhan Haq, Arinal Hidayat, Ahmad Nur Hilya Tsaniya Imagine Clara Arabella Imam Kuswardayan Irawan Rahardja, Agustinus Aldi Isye Arieshanti Isye Arieshanti Jannah, Alissa Velia Royhatul Januar Adi Putra Januar Adi Putra Kautsar, Faiz Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata M. Bahrul Subkhi Maulidan Bagus A.R Maulidiya, Erika Mawaddah, Saniyatul MIFTAHOL ARIFIN, MIFTAHOL Muchamad Kurniawan Muchamad Kurniawan Muchamad Kurniawan, Muchamad Muhamad Nasir Muhammad 'Arif Faizin Muhammad Farih Muhammad Fikri Sunandar Mutmainnah Muchtar Nafa Zulfa Neisa Hibatillah Alif Ni Luh Made ITS Novrindah Alvi Hasanah R Dimas Adityo R. Dimas Adityo Rachman, Rudy Rahma Fida Fadhilah Rangga Kusuma Dinata Rangga Kusuma Dinata Rizal A Saputra Rizal A Saputra, Rizal A Rohman Dijaya Romario Wijaya Safhira Maharani Safhira Maharani Salim Bin Usman Salim Bin Usman Salsabiil Hasanah Sarimuddin, Sarimuddin Septiana, Nuning Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shintami Chusnul Hidayati Shofiya Syidada Sjahrunnisa, Anita Suastika Yulia Riska Sugianela, Yuna Syavira Tiara Zulkarnain Tanzilal Mustaqim Tiara Anggita Tiara Anggita Vriza Wahyu Saputra Wahyu Fajar Setiawan Wibowo, Della Aulia Wicaksono, Farhan Wijayanti Nurul Khotimah Yulia Niza Yulia Niza Yuna Sugianela Yuna Sugianela Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas

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