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All Journal SANGKEP: Jurnal Kajian Sosial Keagamaan Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Hartini, Dina
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Decision Sciences, Operations Research & Management Social Sciences

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Comparison of K-Nearest Neighbors and Convolutional Neural Network Algorithms in Potato Leaf Disease Classification Nurmayanti, Trisya; Hartini, Dina; Rohana, Tatang; Lestari, Santi Arum Puspita; Wahiddin, Deden
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5337

Abstract

tatang.rohana@ubpkarawang.ac.id3, santi.arum@ubpkarawang.ac.id4, deden.wahiddin@ubpkarawang.ac.id5ABSTRACTPotato production in Central Java was recorded to have decreased by 10.77% by the Central Statistics Agency (BPS), from 278,717 tons in 2022 to 248,700 tons in 2023. This decline is due to the fact that potatoes are susceptible to diseases such as late blight and dry spot (early blight) which can significantly reduce yields. This study aims to evaluate the performance of Convolutional Neural Network (CNN) with VGG16 architecture and K-Nearest Neighbors (KNN) to find the best method for potato late blight classification. The dataset used consists of 1500 potato leaf images divided into training, validation, and testing. This research uses pre- processing including resizing, rescaling, and data augmentation. The results show that CNN with the VGG16 model is superior in classifying potato leaf diseases compared to KNN with the MobileNetV2 model. CNN produced an accuracy of 96% while KNN with the MobileNetV2 model obtained an accuracy of 93%. These results can be used as a powerful tool in supporting potato leaf disease identification. This model makes a significant contribution to the development of disease identification techniques through digital image processing.Keywords: Potato Leaf Disease, Convolutional Neural Network, VGG16, K-Nearest
ASSEMBLING THE ISLAMIC MOSAIC OF THE SASAK TRIBE THROUGH TRADITIONAL CLOTHING Hartini, Dina; Hidayati, Baiq Rosida; Rahmayani, Dwina; Nahdiah, Fadhilatul; Hariadi, Eka Putra; Saharudin, Saharudin
SANGKéP: Jurnal Kajian Sosial Keagamaan Vol. 7 No. 1 (2024): Promoting Peace and Cultural Integration in Indonesia
Publisher : UIN Mataram dan Asosiasi Sosiologi Agama Indonesia (ASAGI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/sangkep.v7i1.9159

Abstract

The traditional clothing of the Sasak tribe is clothing that the people of Lombok usually wear at various events such as weddings, traditional ceremonies, or welcoming guests. Men's traditional clothing is called pegon, while women's traditional clothing is called lambung. When wearing traditional Sasak clothing, there are also several accessories such as keris, slewoq, dodot, and sapuq for men and pangkak, tongkak, and bendang/slewoq for women. The traditional clothing used by the Sasak tribe in daily life cannot be separated from religion, especially Islam which is the religion of the majority of the people of the island of Lombok. The aim of this research is to find out what Islamic meanings are contained in the traditional clothing of the Sasak tribe. This research is a type of qualitative descriptive research conducted in the Central Lombok area, specifically in Bebuak Village, Kopang District. The data collection techniques used in this research were direct observation and interviews. The research results show that there are many Islamic meanings contained in the traditional clothing of the Sasak-Lombok tribe, such as monotheism, purity, gentleness, tranquility, and so on. Thus, the traditional clothing of the Sasak-Lombok tribe not only has an aesthetic function but also has a religious function.
Co-Authors Erlina, Yuli Hariadi, Eka Putra Hidayati, Baiq Rosida Lestari, Santi Arum Puspita Malinti, Grace Evelyn Nahdiah, Fadhilatul Ningsih, Ermaya Sari Bayu Nurmayanti, Trisya Rahmayani, Dwina Rohana, Tatang Saharudin Saharudin Wahiddin, Deden