Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Router : Jurnal Teknik Informatika dan Terapan Modem : Jurnal Informatika dan Sains Teknologi Router : Jurnal Teknik Informatika dan Terapan
Edhy Poerwandono
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Identification of Flower Type Images Using KNN Algorithm With HSV Color Extraction and GLCM Texture Edhy Poerwandono; M. Endang Taufik
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.385

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.
Expert System For Diagnosis of Hypertension Disease Using Naive Bayes Method Edhy Poerwandono; Prakoso Angga Ilyasa
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 2 (2025): April : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i2.386

Abstract

Hypertension is a disease that occurs to arteries that causes the supply of oxygen and nutrition that the body needs to be blocked. Hypertension is often called a silent killer, because it is a kind of disease that is very harmful but comes without awareness to its victim. People with hypertension in average are up to 40 years old and it happened all of his after life . In common hypertension caused by heredity, unhealthy lifestyle, and triggered by the more salty consumption, alcohol and stress. An expert system could be the solution to solve the problem because this system works just like an expert and was created by the naïve Bayes method with the rules and basic system that are the same just like the hyperantion desease. Through this application, users can consult with this system just like usually people consult with the expert to diagnose the sign that happened to the user and find the solution of what happened to themselves.
Identification of Flower Type Images Using KNN Algorithm With HSV Color Extraction and GLCM Texture Edhy Poerwandono; M. Endang Taufik
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.385

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.
IMPLEMENTASI YOLOV8 UNTUK DETEKSI DAN KLASIFIKASI TINGKAT KEMATANGAN BUAH MANGGA BERDASARKAN CITRA DIGITAL Satria, Faldo; Edhy Poerwandono
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6381

Abstract

Penelitian ini mengembangkan sistem otomatis untuk mengklasifikasikan kematangan buah mangga menggunakan algoritma YOLOv8 berbasis citra digital, guna mengatasi ketidakakuratan sortir manual oleh petani. Sistem ini menganalisis ciri visual seperti warna dan tekstur secara real-time. Hasil pengujian menunjukkan performa model yang sangat baik dengan mean Average Precision (mAP50) mencapai 94,19%, presisi 94,52%, dan recall 91,85%. Sistem ini diimplementasikan dalam aplikasi mobile untuk petani dan distributor, memperkenalkan teknologi AI guna meningkatkan efisiensi di sektor pertanian.
Co-Authors M. Endang Taufik Prakoso Angga Ilyasa Satria, Faldo