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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Sistem Informasi Dan E-Bisnis
Wachyu Hari Haji
BINUS Entrepreneurship Center, Universitas Bina Nusantara, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Pengembangan Model Aplikasi Manajemen Insentif dan Data Luaran Penelitian pada Perguruan Tinggi Swasta Menggunakan Web Development Life Cycle Wachyu Hari Haji
Jurnal Sistem Informasi dan E-Bisnis Vol 6 No 2 (2024): Juli
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jusibi.v6i2.550

Abstract

The application of research management in universities is essential not only within universities but also outside academia to support the improvement of scientific activities. Various studies emphasize the importance of research management systems in universities by developing applications for publications, data repositories, and continuous research processes. The purpose of this study is to propose an application model of incentive management and research output data at private universities in a structured manner using the Web Development Life Cycle (WDLC). Based on the results of the study, WDLC can be used as a stage to support structuring on unstructured problems and keep users engaged throughout the development lifecycle. This proposed application model consists of an output data input process, user data, reviewer data and review process data.
Implementasi Dataset Augmentation pada Citra Etnofimedisin Menggunakan Teknik Rotation dan Channel Shift Mariana Purba; Vina Ayumi; Wachyu Hari Haji
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8776

Abstract

This study aimed to increase the quantity and variety of ethnopharmacological image datasets using image augmentation techniques, specifically rotation range augmentation (RRA) and channel shift range augmentation (CSA). The dataset augmentation was conducted to enrich the training data for the development of machine learning models used to recognize medicinal plant images. The RRA technique rotated images by random angles, providing variations in object orientation, while CSA altered the color channel values to simulate changes in lighting and the natural colors of plants. The research process included dataset collection, data preprocessing, application of both augmentation techniques, and division of the dataset into training, validation, and testing data. The results showed that the CSA technique produced 2,400 training data, 300 validation data, and 300 testing data, while the RRA technique produced the same amount of data. Therefore, the total data generated from both augmentation techniques amounted to 6,000 images, which could improve the accuracy and performance of deep learning models in recognizing ethnopharmacological images.
Co-Authors Mariana Purba Vina Ayumi