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Hidra Amnur
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hidra@pnp.ac.id
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+6282386434344
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admjitsi@gmail.com
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Kampus Politeknik Negeri Padang, Jurusan Teknologi Informasi. Gedung E. Limau Manis, Pauh. Padang - Sumatera Barat. Indonesia
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JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Published by Society of Visual Informatics
ISSN : 27224619     EISSN : 27224600     DOI : 10.30630/jitsi
Core Subject : Science,
Computer Science & IT
The journal scopes include (but not limited to) the followings: Computer Science : Artificial Intelligence, Data Mining, Database, Data Warehouse, Big Data, Machine Learning, Operating System, Algorithm Computer Engineering : Computer Architecture, Computer Network, Computer Security, Embedded system, Coud Computing, Internet of Thing, Robotics, Computer Hardware Information Technology : Information System, Internet & Mobile Computing, Geographical Information System Visualization : Virtual Reality, Augmented Reality, Multimedia, Computer Vision, Computer Graphics, Pattern & Speech Recognition, image processing Social Informatics: ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT education
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 12 Documents
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Search results for , issue "Vol 5 No 4 (2024)" : 12 Documents clear
Aplikasi Pendeteksi Kematangan Tanaman Menggunakan Metode Transformasi Ruang Warna HSI (Hue, Saturation, Intensity) dan K-NN (K- Nearest Neighbor) Hidra Amnur; Vadreas, Andrew Kurniawan; Ridwan, M.
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 4 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.4.318

Abstract

Tomatoes and chili peppers are essential commodities in the agricultural and food industry, playing a crucial role in nutritional diversity and flavor in human diets. Identifying the ripeness of these fruits is a critical step in the food supply chain, yet it is often done manually by directly observing the ripeness of chilies and tomatoes, which is time-consuming and susceptible to observer subjectivity. Therefore, a system that can identify the ripeness of tomatoes and chili peppers is needed. This system implements the HSI color space extraction method and the K-NN method. K-NN can classify plants based on colors extracted using the HSI color space, which includes three dimensions: Hue (H), Saturation (S), and Intensity (I). The research results in a model from the tomato and chili pepper dataset with an accuracy of 92% and a data split ratio of 80%:20%. This model is implemented in web and mobile formats, expected to efficiently and accurately identify the ripeness of tomatoes and chili peppers. This can help farmers determine the optimal harvest time, improve agricultural production and quality, and provide more reliable information in the food supply chain
Analisis User Experience Tiktok Shop Menggunakan Framework Heart dan Importance Performance Analysis theresiawati, theresiawati; Ananda Alvi Al Fadhli Josephine; Henki Bayu Seta; Rudhy Ho Purabaya
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 4 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.4.319

Abstract

The rapid evolution of social media usage in Indonesia, particularly on TikTok, has marked a significant shift in online interaction. Despite initial controversies over negative content, TikTok's popularity surged in 2019 due to features such as the For You Page (FYP), dance challenges, and the increased online activity during the COVID-19 pandemic. Expanding its scope, TikTok introduced e-commerce features, including TikTok Shop, positioning itself as one of Indonesia's leading social commerce platforms. This study analyzes the user experience (UX) of TikTok Shop using the HEART framework (Happiness, Engagement, Adoption, Retention, and Task Success) combined with Importance Performance Analysis (IPA). Data was collected through questionnaires distributed to TikTok Shop users to identify key indicators for enhancing user experience and correlating these with the user interface (UI) of the TikTok Shop menu. Results revealed significant findings in the Cartesian diagram, with key metrics in Quadrant II including H1 (3.767%), H3 (2.31%), A1 (3.767%), and T7 (1.856%). Redesign recommendations were implemented for items categorized as Action and positioned in Quadrants II and III. Post-redesign testing showed a notable improvement, with an average performance increase of 12.1% compared to the initial evaluation. These findings offer insights into optimizing the TikTok Shop interface to enhance user satisfaction and engagement.

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