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All Journal Jurnal Manajemen Terapan dan Keuangan JURNAL SISTEM INFORMASI BISNIS ELKHA : Jurnal Teknik Elektro JOIV : International Journal on Informatics Visualization CogITo Smart Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Sains Sosio Humaniora
Dedy Setiawan
Fakultas Sains Dan Teknologi, Universitas Jambi, Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation

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Detection of Oil Palm Fruit Ripeness through Image Feature Optimization using Convolutional Neural Network Algorithm Setiawan, Dedy; Eko Prasetyo Utomo, Pradita; Alfalah, Muksin
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2687

Abstract

The increase in the need for raw materials for palm oil products in the form of food and non-food is felt by the people of Indonesia and other countries. For this reason, triggering oil palm farmers in Indonesia must be able to maximize their production. Currently, oil palm farmers in Indonesia still need help knowing the level of sustainability of oil palm fruit to maintain their production. This research was conducted to identify the maturity level of oil palm fruit using practical images for oil palm farmers in Indonesia. The Convolutional Neutral Network (CNN) algorithm is the research method used to identify pictures of oil palm fruit. The dataset collection comprised 400 images of oil palm fruits divided into three types of classes, namely images of raw, ripe, and rotten oil palm fruits. The dataset was taken from various internet sources, and photos were taken directly using a mobile phone camera according to a predetermined class. This study found that identifying the maturity level of oil palm fruit using the Convolutional Neural Network (CNN) algorithm obtained a high accuracy of 98% in the training process and 76% in the model testing process. The findings of this study can also inspire further research in optimizing image features and using the Convolutional Neural Network (CNN) algorithm more efficiently. This could include a reduction in model training time, the number of parameters, or the development of other techniques that improve algorithm performance.
Analisis Faktor Pendorong dan Penghambat Adopsi Aplikasi OVO di Kota Jambi: Pendekatan IPA dan UTAUT 2 Setiawan, Dedy; Enggrani Fitri, Lucky
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp68-80

Abstract

The purpose of this study is to analyze the interest and behavior of using OVO applications, as well as determine the importance rating using Importance Performance Analysis (IPA) of factors that influence interest and usage behavior based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). The questionnaire was distributed to OVO users in Jambi Province with a total sample of 155 respondents. This study used non-probability sampling techniques, and the analysis method used was PLS-SEM with SmartPLS 3 software. The results showed that habits were the most influential and had the highest level of importance to the interest in using OVO in Jambi City. Meanwhile, for usage behavior, the factor that most influences it is supporting conditions, which have a high level of importance and performance according to the importance-performance map analysis (IPMA) felt by OVO users in Jambi Province. The results of this research provide valuable insights for companies or developers in decision-making related to product development, marketing strategies, and resource investment. They can allocate resources more effectively based on a better understanding user preferences and needs.
Co-Authors Alfalah, Muksin Arsa, Daniel Dahmiri Dewi Fitriyani Dios Nugraha Putra Eko Prasetyo Utomo, Pradita F. Husni Hasbullah, F. Husni Fadhila Putri Zahardy, Farah Jayawarsa, A.A. Ketut Lucky Enggraini Fitri Lucky Enggrani Fitri Lutfi Luthfi Luthfi Mukhzarudfa Mukhzarudfa Musnaini Musnaini Repaldi Handi Saputra Rico Wijaya Z Sylvia Kartika Wulan Bhayangkari T. Ricy Rikhad Suheri Tri Suratno Utomo, Pradita Eko Prasetyo Wirmie Eka Putra Yuhana Yuhana Yuliusman -