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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi IPTEK The Journal for Technology and Science Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Prosiding SNATIKA Vol 01 (2011) Jurnal Sistem dan Informatika EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis MIX : Jurnal Ilmiah Manajemen Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Information System Jurnal Eksplora Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CSRID (Computer Science Research and Its Development Journal) CCIT (Creative Communication and Innovative Technology) Journal SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal of Robotics and Control Systems ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Masyarakat (JURDIMAS)
Evi Triandini
Institut Teknologi Dan Bisnis STIKOM Bali
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Social Sciences Other

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Journal : International Journal of Robotics and Control Systems

 1 
Impact of Hyperparameter Tuning on ResNet-UNet Models for Enhanced Brain Tumor Segmentation in MRI Scans Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1802

Abstract

Brain tumor segmentation in MRI scans is a crucial task in medical imaging, enabling early diagnosis and treatment planning. However, accurately segmenting tumors remains a challenge due to variations in tumor shape, size, and intensity. This study proposes a ResNet-UNet-based segmentation model using LGG dataset (from 110 patients), optimized through hyperparameter tuning to enhance segmentation performance and computational efficiency. The proposed model integrates different ResNet architectures (ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152) with UNet, evaluating their performance under various learning rates (0.01, 0.001, 0.0001), optimizer types (Adam, SGD, RMSProp), and activation functions (Sigmoid). The methodology involves training and evaluating each model using Loss Function, Mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and Iterations per Second as performance metrics. Experiments were conducted on MRI brain tumor datasets to assess the impact of hyperparameter tuning on model performance. Results show that lower learning rates (0.0001 and 0.001) improve segmentation accuracy, while Adam and RMSProp outperform SGD in minimizing segmentation errors. Deeper models (ResNet50, ResNet101, and ResNet152) achieve the highest mIoU (up to 0.902) and DSC (up to 0.928), but at the cost of slower inference speeds. ResNet50 and ResNet34 with RMSProp or Adam provide an optimal trade-off between accuracy and computational efficiency. In conclusion, hyperparameter tuning significantly impacts MRI segmentation performance, and selecting an appropriate learning rate, optimizer, and model depth is crucial for achieving high segmentation accuracy with minimal computational cost.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
Co-Authors Abdul Karim Achmad Syaifudin Agus Gian Angga Permana Alifah Putri, Athirah Hersyadea Arie Indrawan Arif Djunaidy Artana, I Gede Edy Aryanto, I Komang Agus Ady Ayu Chrisniyanti Bayu Iswara Budaya, I Gede Bintang Arya Cahya Ayuu Pertami Candra Ahmadi Chusak, Thassaporn Dandy Pramana Hostiadi Daniel Oranova Siahaan Dian Puspita Hapsari DwAyu Agung Indra Swari EDWAR EDWAR Fajar Astuti Hermawati Forca, Adrian Jaleco Franky Rawung Ganda Werla Putra Gde Sastrawangsa Gusti Ngurah Aditya Krisnawan Hashim, Uda Hendra Wijaya Hisbiyah, Yuni I Gede Putu Krisna Juliharta I Gede Suardika I Gusti Ayu Widari Upadani I Gusti Bagus Wiksuana I Ketut Dedy Suryawan I Ketut Putu Suniantara I Ketut Suniantara I Komang Dharmendra I Komang Rinartha Yasa Negara I Made Dwi Darma Artanaya I Made Suniastha Amerta I Nyoman Suraja Antarajaya Indrawan, Arie Indrianto Indrianto Iswara, Bayu Jafari, Nadya Paramitha Jayanatha, Sadu Kabnani, Ezra Tifanie Gabriela Kadek Surya Adi Saputra Karolita, Devi Krisnawan, Gusti Ngurah Aditya Kuswanto , Djoko Kuswanto, Djoko Made Pradnyana Ambara, Made Pradnyana Maneetham, Dechrit Marco Ariano Kristyanto Muhammad Faizi, Muhammad Nakkliang, Kanittha Ni Ketut Dewi Ari Jayanti Ni Luh Putri Srinadi Ni Luh Putu Indiani Ni Wayan Deriani, Ni Wayan Ni Wayan Ni Wayan Novia Ari Sandra Nur Rochmah, Nur Nurfalah, Rizal Farhan Nabila Nuryananda, Praja Firdaus Pamungkas, Yuri Perwitasari, Rayi Kurnia Puji Purwatiningsih, Aris Putra, Chrystia Aji Putra, I Gd Windu Sara Adi Putu Adi Guna Permana Putu Ayu Sita Laksmi Putu Suarma Widiada Rangkuti, Rahmah Yasinta Ratna Kartika W Ratna Kartika Wiyati Ravi Vendra Rishika Reza Fauzan Reza Fauzan Rijal, Muhammad Syamsu Riko Setya Wijaya Rusli, M Rusli, M Sadu Jayanatha Sangsawang, Thosporn Saputra, Kadek Surya Setiawan , I Wayan Agus Hery Setini, Made Shofwan Hanief Siti Rochimah Suardana, Gede Sugiarto Sugiarto S Sugiarto Sugiarto Sugiarto Sugiarto Suniantara , I Ketut Putu Suradarma, IB Suradarma, IB Tedy Apriawan Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Werla Putra, Ganda Widari Upadani, I Gusti Ayu Wijaya, I Gusti Ngurah Satria Wulandari, Riza Yohanes Priyo Atmojo Zulaikha, Ellya