Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Edukasi Elektro Sistemasi: Jurnal Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Nusyura, Fauzan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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

Found 4 Documents
Search

 1 
Implementasi Sistem Rekomendasi Rute Penanganan Gangguan Berbasis Android menggunakan Best First Search Nurdin, Abdul Muhamin; Nusyura, Fauzan; Rahmanti, Farah Zakiyah
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1026

Abstract

The field of information and communication technology essentially has increased development along with increasing access to information which alternates with the internet network which allows computers to be connected to each other. An internet provider is an organization that provides internet access services to its customers. Customer satisfaction with internet services is crucial, so handling internet disruption reports is also a primary focus. The process of following up on internet disruption complaints sometimes requires a long time because there is no recommended route to be resolved by technicians. Therefore, this research aims to provide a solution for recommending the shortest route using the Best First Search method based on Android. The result of this research is a mobile application for technicians to resolve visited disruption points based on the shortest route.
Aplikasi Android untuk Rekomendasi Pemilihan Buah Anggur Hijau Menggunakan VGG16 Setyawan, Nathanael Ferdian Putra; Nusyura, Fauzan; Wicaksono, Ardian Yusuf; Rahmanti, Farah Zakiyah
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3152

Abstract

This study focuses on developing an Android-based recommender system using convolutional neural networks (CNNs) to select high-quality grapes. The main objective of this study is to compare the performance of two popular CNN architectures, VGG16 and ResNet18, in classifying the quality of sour grapes. The subjective and time-consuming nature of conventional methods prompted us to search for a more efficient solution.The dataset used consists of 282 images of green grapes. The evaluation results show that the VGG16 model achieves 93% accuracy in classifying grape quality, outperforming the ResNet18 model with only 82% accuracy. These results indicate that the VGG16 architecture is more suitable for this classification task. The development of this system is expected to contribute to smart agricultural automation to improve efficiency and support the food industry.
Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes Ramadhan, Muhammad Akbar; Nusyura, Fauzan; Rahmanti, Farah Zakiyah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3444

Abstract

One way to see the signs of disease in grapevines is a change in leaf color. Ordinary people detect diseases in grapevines only based on subjective vision. On this basis, we need a system that can help the layman to be able to detect diseases in grapevines based on the color of the leaves using the Naïve Bayes algorithm classification method. This algorithm uses simple calculations, so the process is carried out faster. In this study, testing was carried out using the Naive Bayes classification model with 800 training data and 160 validation data. The accuracy results obtained are 90% using the color historgram scenario on channel RGB interval 16 and GLCM with features of dissimilarity, correlation, homogeneity, contrast pixel spacing 5. 90% accuracy is also obtained in the color histogram scenario on channel HSV with interval 16 and GLCM with features of dissimilarity, correlation, homogeneity at pixel spacing of 5. Thus, it can be concluded that the Naive Bayes classification model can gain application in identifying diseases in grapevines through leaf color analysis.
Implementation of Neural Networks in Daily PV Power Output Prediction Using Bayesian Regularization Algorithms to Assist Energy Management Systems Mahmudah, Norma; Delfianti, Rezi; Sigit, Firman Matiinu; Putra, Dimas Panji Eka Jala; Nusyura, Fauzan
Jurnal Edukasi Elektro Vol. 9 No. 2 (2025): Jurnal Edukasi Elektro Volume 9, No. 2, November 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i2.91044

Abstract

Solar power plants have several advantages, namely continuous energy production, reduced electricity demand, and low photovoltaic maintenance, so that PV power output can be optimized with reliable PV power output predictions. Implementation of Artificial Neural Network (ANN) to predict photovoltaic (PV) power output, using the Bayesian Regularization algorithm. Accurate PV power output prediction is very important in power systems. The data used are solar radiation, PV module temperature, ambient temperature, and actual PV power output, with the target being the PV power output for the next day with the PV power output output for the next day. The architecture used in this study is a Cascade Forward Neural Network (CFNN) and an Elman Neural Network (ENN). Both ANN models use daily data sets and performance evaluation using Mean Square Error (MSE). The results of the study show that ENN is more accurate than CFNN. ENN had the lowest MSE of 0.00664 at a configuration of N=8 and R of 0.9922 with a training time of 6.4 seconds, while CFNN recorded the lowest MSE of 0.024306 with N=25. ENN's ability to capture time series patterns in PV is more reliable and effective. Reliable predictions can assist in energy management systems because they help maintain supply balance, reduce the risk of failure, and improve system stability.
Co-Authors Ardian Yusuf Wicaksono Farah Zakiyah Rahmanti Firman Matiinu Sigit Mahmudah, Norma Nurdin, Abdul Muhamin Putra, Dimas Panji Eka Jala Ramadhan, Muhammad Akbar Rezi Delfianti Setyawan, Nathanael Ferdian Putra