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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Journal of Information Systems Engineering and Business Intelligence JUTIK : Jurnal Teknologi Informasi dan Komputer Journal of Social Work and Empowerment Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Wayan Agus Surya Darma
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences Other

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Extensive Deep Learning Models Evaluation For Indonesian Sign Language Recognition Audrey Tilanov Pramasa; Ni Putu Sutramiani; I Putu Agung Bayupati; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p04

Abstract

Sign language is a vital communication method for individuals with hearing loss or deafness, with variations reflecting unique cultural contexts. Real-time recognition of sign language can bridge communication gaps, yet­­ developing tools for Indonesian Sign Language (BISINDO) is challenging due to limited datasets. This research addresses these challenges by enhancing BISINDO detection and real-time rec­­ognition, focusing on flexible dataset collection and adaptation to varying lighting conditions. Three convolutional neural networks—InceptionV3, MobileNetV2, and ResNet50—are evaluated with optimizers SGD, Adagrad, and Adam to determine the best architecture-optimizer combination. Models were trained on a common dataset and analyzed for optimal performance. Real-time recognition uses MobileNetV2 SSD, integrating data augmentation to improve performance under diverse lighting. The system was deployed on a mobile device for practical use. Results showed the real-time model attained a mean Average Precision (mAP) of 90.34%. This study demonstrates significant advancements in BISINDO recognition and real-time application
Network Reduction Strategy and Deep Ensemble Learning for Blood Cell Detection I Nyoman Piarsa; Ni Putu Sutramiani; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p04

Abstract

Identifying and characterizing blood cells are vital for diagnosing diseases and evaluating a patient's health. Blood, consisting of plasma and cells, offers valuable insights through its biochemical and ecological features. Plasma constitutes the liquid component containing water, protein, and salt, while platelets, red blood cells (RBCs), and white blood cells (WBCs) form the solid portion. Due to diverse cell characteristics and data complexity, achieving reliable and precise cell detection remains a significant challenge. This study presents a network reduction strategy and deep ensemble learning approaches to detect blood cell types based on the YOLOv8 model. Our proposed methods aim to optimize the YOLOv8 model by reducing network depth while preserving performance and leveraging deep ensemble learning to enhance model accuracy. Based on the experiments, the NRS strategy can reduce the complexity of the YOLO model by reducing the depth and width of the YOLO network while maintaining model performance by 4%, outperforming the baseline YOLOv8 model.
Co-Authors Anak Agung Ngurah Hary Susila Arsa, Dewa Made Sri Arthana, I Made Teguh Audrey Tilanov Pramasa Aurelia, Shana Desak Ayu Sista Dewi Dwikasari, Ni Made Dita Dyanggi, Anak Agung Mayra Candra I Gusti Agung Indrawan I ketut Gede Darma Putra I Nyoman Piarsa I Putu Agung Bayupati I Putu Eka Giri Gunawan Kusuma, Nyoman Tri Rahaditya Lampung, Pramayota Fane'a M. Yogi Riyantama Isjoni Made Sudarma Ni Putu Sutramiani Pramana, Made Dimas Aldi Dwi Pranata, I Made Dicky Putri, Komang Sri Yanisa Putu Andy Novit Pranartha Putu Praba Santika Sandhiyasa, I Made Subrata Sastaparamitha, Ni Nyoman Ayu J. Wayan Gede Suka Parwita Wiguna, I Komang Arya Ganda Wiguna, I Putu Yoga Kresna