Bulletin of Computer Science and Electrical Engineering

Implementation of the Waterfall Method in Designing and Building an Income and Cost Management Information System

Nanda Diaz Arizona, Yulia Yulia, Rabiatul Adwiya — Sat, 30 Dec 2023 00:00:00 +0000

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.

Comparison of Tomato Leaf Disease Detection Using Transfer Learning Architecture with the VGG19 Method

Indah Amelia, Nisar Nisar — Tue, 19 Mar 2024 00:00:00 +0000

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.

Enhancing Rose Leaf Disease Detection Accuracy Using Optimized CNN Parameters

Latifah Nabilah, Nisar Nisar, Amnah Amnah, Septilia Arfida — Tue, 19 Mar 2024 00:00:00 +0000

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.