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All Journal Proceedings of KNASTIK JAM : Jurnal Aplikasi Manajemen PROCEEDING IC-ITECHS 2014 Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Humaniora Jurnal Entrepreneur dan Entrepreneurship International Journal of Economics, Business and Accounting Research (IJEBAR) Jurnal Informatika dan Sistem Informasi Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis Aiti: Jurnal Teknologi Informasi Teknika Journal of Applied Data Sciences Prosiding Seminar Nasional Universitas Ma Chung
Trianggoro Wiradinata
Universitas Ciputra Surabaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

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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.
Enhancing Online Batik Shopping Experience through Live Streaming Commerce and the LYFY Application Wiradinata, Trianggoro; Wibowo, Wilbert Bryan; Oktian, Yustus Eko; Maryati, Indra; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.504

Abstract

Online batik shopping often results in buyer dissatisfaction due to discrepancies between product descriptions and the actual items received. Static images and text on e-marketplace platforms are insufficient to convey the intricate details of batik designs, leading to mismatches in customer expectations. To mitigate this issue, Live Streaming Commerce (LSC) features, such as those on Shopee Live, allow sellers to showcase products in real-time, providing more accurate representations. However, sellers face challenges in managing overwhelming volume of comments during live streams, making it difficult to prioritize important queries. LYFY, a comment management app developed to streamline these interactions, aims to address this problem by improving the quality of interaction between live streamers and prospective buyers through filtering important comments. This study examines the determinants affecting the adoption of LYFY by online batik vendors. The research integrates the Task-Technology Fit (TTF), Technology Acceptance Model (TAM), and Expectation-Confirmation Model (ECM) frameworks to evaluate LYFY's performance in fulfilling user requirements. Data were collected from 243 respondents with LSC experience, and the research model underwent evaluation through Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model exhibited high reliability and validity, with values surpassing the suggested thresholds, thereby providing solid support for subsequent analysis. Key factors such as TTF, confirmation, perceived usefulness, ease of use, and satisfaction were examined to determine their impact on user adoption. The analysis revealed that TTF has the strongest influence on confirmation, perceived usefulness, satisfaction, and individual performance. Additionally, perceived ease of use and confirmation substantially influence continuance intentions and satisfaction. These results suggest that enhancing LYFY's task-technology fit and simplifying its user interface are crucial for improving user satisfaction and adoption. By addressing these areas, LYFY can better support live stream sellers, reduce product expectation discrepancies, and improve overall customer experience, particularly in the online batik market.
Co-Authors Adi Suryaputra Paramita Adrie Oktavio Amanda Teonata Astrid Kusumowidagdo Auditia Setiobudi christian christian Christian Herdinata Christina Theodora Loman Cicilia Larasati Rembulan Dyah Kusuma Wardhani Elia Rapha Tister Nursalim Elizabeth Nathania Witanto Feby Putri Candi Felicia Graciella Indra Maryati Krisnawati, Melisa Mahazoya, Aqilla Shahbani Mario, Benedictus Melania Rahadiyanti Michael Christian Chandra Michelle Chandra Permana, Belinda Putri Adi Rinabi Tanamal Savitri, Tjok Istri Vicky Setiawan, Eugene Abigail Sonata Christian Sutanto, Richard Evan Theresia Ratih Dewi Saputri Tony Antonio Wibowo, Wilbert Bryan Wienadi, Jessica Yap, Justin Yosua Setyawan Soekamto Yustus Eko Oktian