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Contact Name
Edi Sutoyo
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edisutoyo53@gmail.com
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+6281381694837
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edisutoyo53@gmail.com
Editorial Address
Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
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INDONESIA
Bulletin of Computer Science and Electrical Engineering (BCSEE)
ISSN : -     EISSN : 27227324     DOI : https://doi.org/10.25008/bcsee
Bulletin of Computer Science and Electrical Engineering (BCSEE) is a biannually peer-reviewed open access journal that covers the leading edge subjects and matters in the computer science, information systems and electrical engineering disciplines. The Journal stresses on academic excellence, research rigidity, knowledge distribution, and reciprocated scholarly efforts in order to endorse theoretical, experimental and practical research at national and international levels. The BCEE publishes research articles, review articles, short communications, letters, and technical notes that meet the broad-spectrum criteria of scientific excellence in the following research areas (but not limited to): Computer Science and Engineering Electronics and Communication Engineering Electrical and Electronics Engineering and Technology Information Technology Computer Applications Bioinformatics Instrumentation and Control Engineering Medical Electronics Engineering Mobile and Pervasive Computing Software Engineering VLSI Design Optical Communication Multimedia Technology Biomedical Engineering Communication and Networking Digital Signal Processing Biometrics and Cyber Security Embedded System Technologies Power Electronics and Drives Power Systems Engineering Electrical Drives and Embedded Control Big Data Analytics and Cloud Computing Computer Networks Engineering Database Technologies Robotics and Artificial Intelligence Techniques Soft Computing Techniques Ad-hoc and Sensor Networks Parallel, Distributed and Grid Computing Data Warehousing and Data Mining Machine Learning Algorithms Image, Signal and Video Processing Mobile Computing and Application Development Wireless Technologies Web Technologies Wind, Solar, and Renewable Energy Ubiquitous Computing and Embedded Systems U And E-service, Science and Technology System Security and Security Technologies Software Testing & Analysis Integrated Circuits & Systems Radio Frequency (RF), Microwave, An Millimeter-Wave Circuits Remote Sensing and Space Systems Space Plasma Electro-Dynamics Wave Propagation Social Networking and Analysis Hardware Engineering Computer Architecture and Organization Electrical Machines XML Web Services Electromagnetic Materials for RF and Microwave Applications Antennas Open Source Technologies in Operating System Enterprise Computing Information Security Bio-Inspired Computing Computer Science Applications Nano-Electro-Mechanical System Internet of Things (IoT) Photonics and Optoelectronics Information Engineering Cryptography and Network Security MEMS and Microsystems Solid-State Devices and Nanotechnology Computer Vision Electromagnetics & Acoustics Channel Coding Theory and Applications Energy Systems Digital Signal Processing Control, Intelligent Systems Design, Modeling, and Analysis (DMA) Integrated Circuits Micro/Nano Electro Mechanical Systems (MEMS) Physical Electronics Signal Processing Human-Computer Interaction (HCI) Information Storage Management Compiler Design Invisible Computing Biotechnology Fuzzy Logic and rough set theory E-Commerce Evolutionary algorithm Knowledge Management Artificial Neural Networks (ANNs) Simulation Techniques Solid-State Device Modelling and Simulation Analysis and Design of Analog Integrated Circuits Programming Languages Graphics and Visualization Computational Biology 3D Modelling and Rendering Audio Video Broad Casting Systems Nano-Scale Transistors Analog and Mixed Mode VLSI Design Genetic Algorithms and its Applications Particle Swarm Optimization Ant Colony Optimization Any other relevant topics.....
Articles 3 Documents
Search results for , issue "Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering" : 3 Documents clear
Implementation of the Waterfall Method in Designing and Building an Income and Cost Management Information System: Case study: Limited Liability Company Adau Kapuas Arizona, Nanda Diaz; Yulia, Yulia; Adwiya, Rabiatul
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1183

Abstract

Adau Kapuas is a company operating in the transportation services sector. In processing income and costs, Adau Kapuas Limited liability companies still use bookkeeping as a data collection medium, such as recording payment and expenses, which are processed starting from recording ticket sales and goods delivery transactions, which process passenger data and goods delivery data. The income obtained from tickets and delivery of goods and the total costs are then entered back into the computer. This proves the difficulty faced by Adau Kapuas, namely having to do work twice daily. This research discusses the application of income and cost processing at Adau Kapuas Pontianak. This application was designed using Netbeans IDE 8.2. It can process admin data, types of goods, types of income, costs, bus classes, destinations, ticket sales transactions, delivery of goods, income transactions, and cost transactions. The reports produced by this application include ticket sales reports, goods delivery reports, income reports, and cost reports. With the income and expense processing application, it is hoped that it can support the performance of Adau Kapuas Pontianak in processing income and expense transactions and presenting reports more easily, quickly, and accurately.   
Enhancing Rose Leaf Disease Detection Accuracy Using Optimized CNN Parameters Nabilah, Latifah; Nisar, Nisar; Amnah, Amnah; Arfida, Septilia
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1184

Abstract

The CNN model developed in this study demonstrated remarkable performance, achieving an outstanding validation accuracy of 99.96%. Through experimentation, it was found that employing the RMSprop optimizer with a learning rate of 0.001 yielded superior results compared to the Adam optimizer utilized in previous iterations. Additionally, increasing the number of epochs from 10 to 20 resulted in a significant enhancement in accuracy, highlighting the importance of iterative training for model refinement. Moreover, the implementation of Early Stopping proved to be a valuable technique, effectively conserving training time by halting the training process once optimal accuracy levels were reached. These findings underscore the efficacy of various optimization strategies in bolstering the performance of CNN models for rose leaf disease detection. The achieved accuracy rates signify a substantial advancement in disease detection technology, holding promise for enhancing agricultural productivity and ensuring plant quality. This research contributes valuable insights into the optimization of CNN parameters, paving the way for further advancements in automated disease detection systems in the field of agriculture.
Comparison of Tomato Leaf Disease Detection Using Transfer Learning Architecture with the VGG19 Method Amelia, Indah; Nisar, Nisar
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1185

Abstract

Diseases in plants are often detrimental to agriculture, can be seen manually and require a very long time, which can lead to possible errors in disease detection. Detecting diseases in plants early can overcome these problems and reduce the risk of reduced crop production. The aim of this research is to make a comparison of quickly and accurately detecting tomato leaf diseases compared to previous researchers who used Deep Learning applications. Which can be applied effectively for image classification using the VGG19 method. The implementation of this model uses a dataset containing 2,694 images, including 3 different types of diseases. That the conclusion of this research is the fastest and most accurate way to detect tomato leaf diseases. To prove this research, results and necessary data will be presented in this paper. The accuracy obtained on the VGG-19 architecture was 91.85% with the best increase in accuracy compared to the previous journal which only produced 87% accuracy.

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