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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Habitat JPSriwijaya Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Applied Informatics Journal of Applied Engineering and Technological Science (JAETS)
Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi
Fakultas Ilmu Komputer Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Neuroscience Public Health Other

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Vision Transformer for Active Compound Function Classification Based on 2D Molecular Structures Dian Eka Ratnawati; Diva Kurnianingtyas; Agus Wahyu Widodo; Rekyan Regasari Mardi Putri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9418

Abstract

Accurate classification of active compounds based on molecular structure is crucial for accelerating drug discovery while reducing laboratory costs and time. However, existing structure-based classification methods, particularly convolutional neural networks and graph-based models, often struggle to capture long-range dependencies or require large-scale datasets and extensive feature engineering. This study investigates the use of the Vision Transformer (ViT) model to classify 2D molecular structure images of compounds into cancer and cardiovascular therapy categories. A dataset containing 500 images, consisting of 250 per class, was obtained from the PubChem database, processed for consistency, and divided into 72% training, 20% testing, and 8% validation. To address the limited dataset size, careful preprocessing, regularization through weight decay, and systematic hyperparameter tuning were applied to reduce overfitting risks. The ViT model was trained with the Adam optimizer and a linear learning rate scheduler. Hyperparameters were systematically tuned to identify the optimal configuration. Results show that the best settings, with batch size 60, weight decay 0.1, learning rate 3.0×10⁻⁶, and 15 epochs, achieve an accuracy, F1 score, and loss of 80.0%, 79.9%, and 0.597, sequentially. These findings highlight the potential of ViT for small-scale cheminformatics tasks, offering an alternative to conventional methods while maintaining competitive performance.
Deep Learning Architecture Model for Iris Image Segmentation in Biometrics Soebroto, Arief Andy; Mahmudy, Wayan Firdaus; Hidayat, Nurul; Putri, Rekyan Regasari Mardi; Nugroho, Anto Satriyo
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.100566

Abstract

Abstrak : Teknologi biometrik memanfaatkan karakteristik fisik atau perilaku manusia untuk identifikasi dan verifikasi identitas, dengan salah satu implementasi paling signifikan adalah biometrik iris. Teknologi ini menggunakan pola unik pada iris mata untuk tujuan identifikasi yang aman dan andal, namun masih menghadapi tantangan dalam memastikan segmentasi citra yang konsisten. Penelitian ini berfokus pada pengembangan segmentasi citra iris menggunakan deep learning sebagai langkah krusial dalam proses identifikasi biometrik iris. Segmentasi citra bertujuan untuk memisahkan wilayah iris dari bagian mata lainnya, seperti pupil, sklera, dan kelopak mata, namun proses ini memerlukan pendekatan yang lebih canggih untuk mengatasi variasi citra. Penelitian ini mengimplementasikan arsitektur deep learning populer, yaitu DeepLabV3 dan U-Net, untuk segmentasi citra iris. Evaluasi performa dilakukan menggunakan metrik IoU Score, Accuracy, Precision, Recall, dan F1-Score. Hasil pengujian menunjukkan bahwa DeepLabV3 memberikan kinerja terbaik dengan IoU Score sebesar 0,918, Accuracy sebesar 0,993, Precision sebesar 0,962, Recall sebesar 0,952, dan F1-Score sebesar 0,957. Keunggulan DeepLabV3 terletak pada kemampuannya dalam melakukan ekstraksi fitur yang kompleks dan menangkap konteks informasi pada berbagai skala secara efektif. Temuan ini menggarisbawahi potensi besar penerapan deep learning dalam segmentasi citra iris untuk sistem biometrik. Dengan performa optimal yang dicapai oleh DeepLabV3, teknologi ini dapat diandalkan untuk meningkatkan akurasi dan efisiensi proses identifikasi biometrik, membuka peluang luas untuk pengembangan lebih lanjut dalam aplikasi keamanan berbasis iris.===================================================Abstract :Biometric technology is an innovation that uses human physical or behavioral characteristics for identity determination and verification with an aspect of its most significant implementations identified to be iris biometrics. The technology uses unique patterns in iris for secure and reliable identification purposes but certain challenges are encountered in ensuring consistent image segmentation. Therefore, this research focuses on developing iris image segmentation using deep learning as an important step in biometric identification process. Image segmentation aims to separate iris region from other parts of the eye, such as the pupil, sclera, and eyelids. However, the process requires a more sophisticated method to overcome image variations. This research implements popular deep learning architectures, DeepLabV3 and U-Net, for the segmentation. Subsequently, the performance of the models was evaluated based on the IoU Score, accuracy, precision, recall, and F1-score metrics. The results showed that DeepLabV3 provided the best performance with an IoU Score of 0.918, accuracy of 0.993, precision of 0.962, recall of 0.952, and F1-score of 0.957. The advantage of the model was associated with the ability to effectively extract complex features and capture information context at different scales. The observation was an indication of the significant potential possessed by deep learning applications in iris image segmentation for biometric systems. Moreover, the optimal performance achieved by DeepLabV3 showed the possibility of depending on the technology to improve the accuracy and efficiency of biometric identification process, opening up broad opportunities for further development in iris-based security applications.
Co-Authors Achmad Arwan Agung Setia Budi, Agung Setia Agus Wahyu Widodo Ahmad Izzuddin Ainun Najib Eka Christianto Akbar, Muhammad Faithur Adel Patria Albert, Muhammad Zaidan Aldo, Muhammad Alhasyimi, Dana Mustofa Alqadri, Aikal Ichsan amiruddin, muhammad dzaky Angelica, Sherina Yosephine Annuranda, Ramansyah Eka Anto Satriyo Nugroho, Anto Satriyo Arief Andy Soebroto Aulady, Fadhli Barlian Henryranu Prasetio Budi Darma Setiawan Candra Dewi Candra Dewi Chusnah Puteri Damayanti Dahnial Syauqy Dharmawan, Fakhriz Thoriqo Dian Eka Ratnawati Diva Kurnianingtyas Edy Santoso Eko Setiawan Eko Setiawan Elsa Nuramilus Shofia Endah Utik Wahyuningtyas Faizatul Amalia Fajar, Sanhnai Fathirul Fanani, Aulia Putri Firdaus, Muhammad Alifiansyah Firza Zamzani, Muhammad Fitra Abdurrachman Bachtiar Fitriyah, Hurriyatul Fran's Dwi Saputra Atmanagara Frans Agum Gumelar Gembong Edhi Setyawan Haqiqi, Farih Akmal Herlambang, Romario Yudo Hisdianton, Oktavian Hurriyatul Fitriyah, Hurriyatul Ichsan , Mochammad Hannats Hanafi Imam Cholissodin Indriati Indriati INTAN NIRMALASARI Irfan Muzakky Nurrizqy Iunike Kartika Dewi Karuniawan Susanto Khoirin Nisa Fitrianur Kurniawan, Rafi Athallah Kusuma, Aji Ranca Lailil Muflikhah Lilik Wahyuni Luthfi Anshori M. Ali Fauzi Mahar Beta Adi Sucipto, Ekmaldzaki Royhan Malik, Hifdzul Manoeroe, Gregorio Maryamah Maryamah Merry Gricelya Nababan Merry Gricelya Nababan, Merry Gricelya Meryandha, Afra Naima Mimin Putri Raharyani Moch. Maulana Alrizzaqi Muhammad Abduh Muhammad Adiputra Muhammad Najmi Ridhani Muzayyin, Asep Nata Dirana, Pratama Putra Niken Hendrakusma Wardani Ningsih Puji Rahayu Nurkhoyri, Ageng Nurrizqy, Irfan Muzakky Nurul Auliyah Nurul Hidayat Pamungkas, Gilang Alif Pangestu Ari Wijaya Pardamean, Yohanes Pinandita, Eggi Pur Praminsya, Agam Pratama, Muhammad Naufal Rafi Pratomo Adinegoro Pricillia, Lidya Ruth Rakhmadhany Primananda, Rakhmadhany Rakhmadina Noviyanti Ramadhan, Wafdannur Ramadhani, Aryo Sheva Ramadhani, Roihaan Randi Pratama Nugraha Randy Cahya Wihandika Randy Cahya Wihandika Ridho Adi Febrian Rima Diah Wardhani Rizal Maulana, Rizal Rizqi Muh. Muqoffi Ashshidiqi Rosyidah, Dinda Inayatur Satria Dwi Nugraha Satrio Agung Wicaksono Sevtyan Eko Pambudi Siswanti Sukmawan, Sony - Supraptoa Supraptoa Sutopo Sutopo Sutrisno Sutrisno Syahwanto, Virandy Bagaskara Tegar Assyidiqi Nugroho Tibyani Tibyani Utaminingrum, Fitri Vienticentia Imanuwelita Wayan Firdaus Mahmudy Widodo, Moudy Lestari Tulus Widyana, Kurnita Ruci Wijaya Kurniawan Yusi Tyroni Mursityo Yusuf Priyo Anggodo, Yusuf Priyo Zahra Swastika Putri Zarkasyi, Muhammad Rifky Irfan Zultoni Febriansyah