Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Jurnal Komputer dan Teknologi (JUKOMTEK)
Toyib Hidayat, Asep
Unknown Affiliation
Computer Science & IT Library & Information Science

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Implementasi Sistem Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika Berbasis Web Toyib Hidayat, Asep; Hakim, Lukman; Rio; M.Afif Ravanza
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.304

Abstract

The process of preparing the lecture schedule at Bina Insan University is still done semi-manually with the help of Microsoft Excel and takes days, even weeks, whereas making the schedule must be done optimally and quickly because the schedule will be used for lecture activities each semester, so that the scheduling process can be carried out effectively and efficiently, so we need an application that can simplify the scheduling process, namely a scheduling application and applying the right algorithm, one of the algorithms that can be used in scheduling applications is the Genetic Algorithm. Course scheduling is a process of allocating courses, lecturers, space and time by matching predetermined rules so that all these components can be fulfilled. Genetic Algorithms are search algorithms that are based on natural selection mechanisms and natural genetics. The results of the research are in the form of a schedule that is prepared automatically without any more clashes between times and classes to be used
Forecasting Penjualan Produk Sembako Menggunakan Metode Triple Exponential Smoothing Toyib Hidayat, Asep; Puspita Sari, Dwi; Andriani, Pebrinda
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 4 (2024): RESOLUSI March 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i4.1754

Abstract

Forecasting is a very important factor in the decision making process. The forecasts made are generally based on the past which is then analyzed using certain methods. Past data is collected, researched, analyzed and linked to the passage of time. In this research, the Triple Exponential Smoothing Method is used, which is a forecasting method that is commonly used because it has simple concepts and calculations. One of the reasons for using the periodic series data smoothing method is because this method can be done with two approaches, namely the smoothing method and the exponential smoothing method. The results obtained from this research. Selecting the right ?, ?, ? values ??can produce ideal MAPE values. It is proven that to obtain ideal values ??for ?, ?, ? using the brute force method, the values ??obtained are ? = 0.1, ? = 0, 2, and ? = 0.9, we get a MAPE value of 1.92%, where previously the resulting MAPE was 7.54%.
KLASIFIKASI JENIS PENYAKIT BUAH MANGGA BERBASIS DEEP LEARNING MENGGUNAKAN ARSITEKTUR RESNET DAN MOBILENET Cornelis Rasyid, Nanda; Karman, Joni; Toyib Hidayat, Asep; Lingga Wijaya, Harma Oktavia
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.570

Abstract

Mango plantations in Indonesia face significant challenges due to pests and diseases that reduce productivity and cause economic losses for farmers. Manual identification of these issues requires expert knowledge and is often time-consuming and inaccurate. This study aims to develop a classification system for detecting various mango leaf diseases using deep learning models, specifically ResNet and MobileNet architectures. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automatic disease detection from plant images by learning patterns without explicit programming. The proposed system focuses on identifying common diseases such as leaf blight, whiteflies, and leaf caterpillars efficiently and accurately. By leveraging image-based recognition, the system allows for early diagnosis and timely intervention. The results of this research are expected to provide a technological solution that supports modern agriculture and empowers farmers with better disease management tools.
Co-Authors Andriani, Pebrinda Cornelis Rasyid, Nanda Karman, Joni Lingga Wijaya, Harma Oktavia Lukman Hakim M.Afif Ravanza Puspita Sari, Dwi Rio