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All Journal Building of Informatics, Technology and Science Journal of Computer Networks, Architecture and High Performance Computing
Abul Hasani, Rofi
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Computer Science & IT Education

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Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network Resa Arif Yudianto, Muhammad; Sukmasetya, Pristi; Abul Hasani, Rofi; Sasongko, Dimas
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2575

Abstract

Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
Analysis of Outpatient Patient Visit Prediction at Muntilan Regional General Hospital Using Linear Regression Method Susanti, Dwi; Hendradi, Purwono; Abul Hasani, Rofi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5271

Abstract

Hospitals play a crucial role in public health, and understanding patient visit patterns is essential for effective service delivery. Thus, accurate predictions are vital for resource planning, service improvement, and addressing challenges like long wait times and overcrowding. This study focuses on predicting outpatient visits at RSUD Muntilan, a regional general hospital in Magelang, Indonesia. The method used was the linear regression method. The research involved data collection from the hospital's information system, pre-processing to prepare the data, dataset formation, model creation using linear regression, and model evaluation. The study utilized historical outpatient visit data FROM 2021 TO 2024 to develop a linear regression model that predicts the number of visits for the next three months. The model's performance was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 15.33%. This indicates that the model's predictions were, on average, within 15.33% of the actual values, demonstrating an accuracy of 84.67%. The successful application of the linear regression method in this study highlights its potential for improving resource allocation, enhancing service efficiency, and ultimately enhancing the overall quality of healthcare services provided by RSUD Muntilan. The findings emphasize the significance of data-driven approaches and predictive analytics in optimizing healthcare operations and meeting the evolving needs of the community. 
Co-Authors Dimas Sasongko Dwi Susanti Purwono Hendradi Resa Arif Yudianto, Muhammad Sukmasetya, Pristi