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All Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Al Maktabah Jurnal kajian Ilmu dan Perpustakaan
Herlini Oktaria
Institut Teknologi dan Bisnis Diniyyah Lampung
Computer Science & IT Library & Information Science

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PENERAPAN SLiMS PADA LAYANAN SIRKULASI DI PERPUSTAKAAN PERGURUAN TINGGI Mezan el-Khaeri Kesuma; Irva Yunita; Jaka Fitra; Nadya Amalia Sholiha; Herlini Oktaria
Al Maktabah : Jurnal Kajian Ilmu dan Perpustakaan Vol 6, No 2 (2021): DESEMBER
Publisher : Pusat Perpustakaan IAIN Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/mkt.v6i2.5148

Abstract

Tujuan dilakukannya penelitian ini adalah: (1) untuk mengetahui penerapan SLiMS pada layanan sirkulasi di perpustakaan Perguruan Tinggi telah berjalan secara optimal; (2) untuk mengetahui adakah masalah dalam penerapan SLiMS pada layanan sirkulasi di perpustakaan Perguruan Tinggi. Penelitian ini termasuk kedalam metode analisis deskriptif dengan pendekatan kualitatif. Hasil penelitian ini adalah dengan penerapan SLiMS   sangat membantu sekali dalam layanan perpustakaan khususnya layanan sirkulasi. Software ini juga sangat membantu mahasiswa dalam pelayanannya sebagai pemustaka dan meringankan pekerjaan serta meningkatkan performa pustakawan.
MONITORING VEGETATION HARVEST OF COFFEE TREES USING KNN-CLUSTERING ALGORITHM Dwi Handoko; Nizamiyati Nizamiyati; Herlini Oktaria; Agus Mulyanto; Muhamad Brilliant
TEKNOKOM Vol. 6 No. 1 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (264.988 KB) | DOI: 10.31943/teknokom.v6i1.90

Abstract

Coffee is one of the plantation commodities spread throughout Indonesia. Coffee is the main commodity for export in Tanggamus Regency. The prediction of crop yields based on aerial photography is the main problem in this study, then there is no dataset of aerial imagery of coffee plantations that are specifically used for the purpose of determining coffee tree vegetation on coffee plantations so that farmers can find out which land is still overgrown by other plants. in addition to coffee trees and the possibility of making predictions for crop yields from aerial imagery of the coffee plantations, this research is also another urgency. This study is intended to build an intelligent model to detect the amount of coffee tree vegetation in a plantation using the KNN-Clustering segmentation algorithm. The image of the coffee tree was taken using a drone with a height of 50 m and an area of 0.25 ha. Preprocessing was carried out. The preprocessed image is called a dataset. After that, the segmentation process is carried out using the Region Growing method to form a black and white image. After Region Growing is done, then the image in Clustering uses the KNN-Clustering method to determine the color pattern of the image in the coffee plantation to distinguish the types of vegetation in the coffee plantation. From the results of KNN-Clustering, the area of coffee tree vegetation is obtained from a total of 0.25 ha of coffee plantation images.
Co-Authors Agus Mulyanto Dwi Handoko Fitra, Jaka Irva Yunita Mezan el-Khaeri Kesuma Muhamad Brilliant Nadya Amalia Sholiha Nizamiyati Nizamiyati