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All Journal Buletin Ilmiah IMPAS Jurnal Teknik Informatika (JUTIF)
Yunita, Maria
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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KONTRIBUSI PENDAPATAN USAHA VIRGIN COCONUT OIL (VCO) TERHADAP PENDAPATAN KELUARGA DI KECAMATAN KEWAPANTE KABUPATEN SIKKA NUSA TENGGARA TIMUR Yunita, Maria; Kapa, Maximillian M.J.; Pellokila, Marthen Robinson
Buletin Ilmiah Impas Vol 25 No 1 (2024): Volume: 25 No.: 1 Edisi April 2024
Publisher : Undana Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/impas.v25i1.16321

Abstract

Virgin Cococut Oil (VCO) has been researched to provide many benefits to the people of Kewapante District, and has an effect on family income, because VCO makes business a fairly good contribution to household income. This research aims to determine:1) VCO business income, and 2) VCO business contribution in Kewapante District, Sikka Regency, East Nusa Tenggara. The research location was carried out in Waiara Village and Wairkoja Village, Kewapante District. Determining the location deliberately (purposive sampling). The sample was taken as a whole from the population with a consideration of 30 respondents. The data used is primary data and secondary data. Data analysis uses descriptive analysis, including income and contribution analysis. The research results show that: 1) the average income of the Virgin Coconut Oil (VCO) business is Rp.12.906.133, per year. 2) Virgin Coconut Oil (VCO) business contribution is 46,73%. This means that the Virgin Coconut Oil (VCO) business makes a large contribution to total household income in the medium category.
PATTERNS RECOGNITION (MAUMERE SARONG) USING EDGE DETECTION WITH PREWITT, SOBEL, LAPLACIAN OF GAUSSIAN (LOG), AND CANNY METHODS Piran, Gerfasius Take; Alfan, Hilarius; Yunita, Maria
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1972

Abstract

Maumere Ikat Weaving is a cloth made from a weaving process that requires a lot of energy and time. Maumere Ikat weaving is not only limited to artistic creations; its production also considers symbols of social, religious, cultural, and economic status. The location of an image is easy if the image is clear and sharp. Still, the exact location of the edges makes it difficult to determine if the image contains interference such as noise. Objective: Recognize a Lipa pattern (Maumere Sarong) using Prewitt, Sobel, Laplacian of Gaussian (LoG), and Canny edge detection. Methods: Prewitt, Sobel, Laplacian of Gaussian (LoG), and Canny edge detection. Results: The Lipa (Maumere Sarong) pattern recognition application using Canny edge detection can increase accuracy in recognizing a Lipa (Maumere Sarong) pattern so that it can provide knowledge for tourists and the wider community to recognize and obtain information on the Lipa (Maumere Sarong) more easily.
Implementation Of Cnn Mobile Netv2 For Classification And Detection Of Diseases In Banana Plants Through Leaf Images Yunita, Maria; Yeyen, Yustina Yesisanita; Chanda, Angie Ray; Noweng, Elisabeth Elen
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5510

Abstract

Banana plants are vulnerable to disease attacks, especially in remote areas with limited access. Banana farmers struggle to identify and classify types of diseases on banana plants early on due to limited information about the types of diseases and the characteristics of diseases that attack bananas.The purpose of this study is he development of a CNN model with a MobileNet architecture for the classification and detection of diseases through banana leaf images, which can be implemented in an Android application. The method used applies a Convolutional Neural Network (CNN) using the MobileNetV2 architecture that can help classify banana plant diseases. The banana leaf image dataset was obtained independently and additionally from the Kaggle platform up to 4135 images. The images were then divided into 6 classes consisting of healthy leaves, panama disease, moko disease, leaf pests, yellow sigatoka and black sigatoka. The image dataset was then divided again into 3 parts: training data, validation data and test data with a data division of 80:10:10. The results showed that CNN with MobileNetV2 architecture can be used for disease classification and detection with an accuracy rate of 87.26% for the test data, 89,59 for validasi and 92.71% for the training data. This model was successfully implemented on the Android platform using Android Studio to detect banana plant diseases in real time without special tools.
Co-Authors Chanda, Angie Ray Hilarius Alfan Kapa, Maximillian M.J. Marthen Robinson Pellokila Noweng, Elisabeth Elen Piran, Gerfasius Take Yeyen, Yustina Yesisanita