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All Journal Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences
Sutanto, Richard Evan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology Wiradinata, Trianggoro; Saputri, Theresia Ratih Dewi; Sutanto, Richard Evan; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.121

Abstract

The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.
COMPARATIVE STUDY OF CNN-BASED ARCHITECTURES ON EYE DISEASES CLASSIFICATION USING FUNDUS IMAGES TO AID OPHTHALMOLOGIST Yaurentius, Evelyn Callista; Saputri, Theresia Ratih Dewi; Tanuwijaya, Evan; Sutanto, Richard Evan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3699

Abstract

Eye health has a significant impact on quality of life, with more than 2.2 billion people experiencing vision problems. Many of these cases can be prevented or treated. The use of AI for eye disease classification helps healthcare professionals provide optimal care. However, the complexity of fundus images challenges classification performance. This study examines various Convolutional Neural Network (CNN) architectures using Transfer Learning and Adam optimization. Fundus images are processed using CLAHE (clip limit and grid size) and the Wiener filter (size) to enhance contrast and reduce noise. Afterward, ResNet-152, EfficientNet, MobileNetV1, and DenseNet-121 are tested to identify the most effective model. The study aims to determine the optimal CNN architecture for eye disease classification, assisting ophthalmologists in diagnosing eye diseases through fundus images. The best CNN model, ResNet-152, achieved an accuracy of 94.82%, outperforming other models by 3.95 - 8.29%.
Co-Authors Evan Tanuwijaya Theresia Ratih Dewi Saputri Trianggoro Wiradinata Yaurentius, Evelyn Callista Yosua Setyawan Soekamto