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All Journal Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer
Nizamiyati Nizamiyati
Institut Teknologi dan Bisnis Diniyyah Lampung
Computer Science & IT

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Sistem Pakar Metode Case-Based Reasoning Untuk Deteksi Penyakit Stunting Pada Anak Muhamad Brilliant; Nizamiyati Nizamiyati
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol 5, No 2 (2022): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/simada.v5i2.3415

Abstract

Toddlers have a very high risk of being stunted such as chronic malnutrition, height, or body length that is too small for the same age group. During pregnancy Malnutrition can be experienced by infants, even in the early days of birth, but stunting cannot be experienced for babies under two years of age. One of the efforts to increase the level of health in children is by identifying stunting. Manual identification by measuring the child's height and weight is considered ineffective. The Focus of this research is creating an expert system for detecting stunting in children using the Case based reasoning (CBR) method using website-based technology so that it can be used anytime and anywhere.. This study uses the RnD (Research and Development) method with the stages in the ADDIE research as follows (1) Analysis (2) Design (3) Development, (4) Implementation (5) Evaluation. The results of the research are in the form of an expert system for detecting stunting in children using the Case-based reasoning (CBR) method which has succeeded in detecting/diagnosing stunting in children and can provide suggestions for several diagnostic results as well as solutions from the results of the diagnosis, the application is made website-based so that it can be used by anyone, anywhere, and anytime. From the results of application testing using the white box testing method, the scenario is appropriate and the application is running as it should.
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 Aldy Gyan Dinasta Dwi Handoko Faizafati Matus Zahro Faridah Hilmiarhani Fegi Sentiana Herlini Oktaria Muhamad Brilliant Nopi Fajriani