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Sriwijaya Journal of Informatics and Applications
Published by Universitas Sriwijaya
ISSN : -     EISSN : 28072391     DOI : -
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Sriwijaya Journal of Informatics and Applcations (SJIA) is a scientific periodical researchs articles of the Informatics Departement Universitas Sriwijaya. This Journal is an open access journal for scientists and engineers in informatics and Applcations area that provides online publication (two times a year). SJIA offers a good opportunity for academics and industry professionals to publish high quality and refereed papers in various areas of Informatics e.q., Machine Learning & Soft Computing, Data Mining & Big Data Analytics, Computer Vision and Pattern Recognition and Automated Reasoning, and Distributed and security System
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Articles 5 Documents
 1 
Search results for , issue "Vol 4, No 1 (2023)" : 5 Documents clear
Reconstruction Low- Resolution Image Face Using Restricted Boltzmann Machine Julian Supardi
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.72

Abstract

Low-resolution (LR) face images are one of the most challenging problems in face recognition (FR) systems. Due to the difficulty of finding the specific features of faces, the accuracy of face recognition is low. To solve this problem, some researchers are using an image reconstruction approach to improve the resolution of their images. In this research, we are trying to use the restricted Boltzmann machine (RBM) to solve the problem. Furthermore, a labelled face in the wild (lfw) database has been used to validate the proposed method. The results of the experiment show that the PSNR and SSIM of the image result are 34.05 dB and 96.8%, respectively.
Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export Widya Aprilini; Dian Palupi Rini; Hadipurnawan Satria
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.51

Abstract

Natural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with  and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports.
Risk Management Evaluation in Hospital Management Information Systems Using Framework COBIT 2019 - Case Study: Ernaldi Bahar South Sumatera Hospital Hilditia Cici Triska Amirta; Muhammad Ihsan Jambak; Pacu Putra Suarli; Yadi Utama; Ari Wedhasmara; Putri Eka Sevtiyuni
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.52

Abstract

Hospital Management Information System (SIMRS) is a system to assist service performance, reporting and data retrieval at hospitals that have been required by the government to be implemented in all hospitals in Indonesia. The existence of SIMRS is certainly an inseparable part of the service process and hospital data management, but it can also cause various IT risks to arise. Therefore, a good risk management is needed to minimize any possible IT risks that have not or have occurred. The performance of an IT risk management can be indicated from its capability levels. This study aims to determine how high the capability levels and the gap value from each process of the IT risk management at Ernaldi Bahar Hospital. The framework used as a reference in the assessment of the risk management process is COBIT 2019 which has 3 stages, namely the mapping process, capability level assessment, and conclusions. This study resulted in the value of capabilities in each process in IT risk management, the gap value, and recommendations for improvement that can be applied to SIMRS Ernaldi Bahar. The results of the measurement of the IT risk management capability of SIMRS Ernaldi Bahar in the EDM03 and DSS03 processes are at level 3, while the APO12 and DSS05 processes are at level 1. The gap values for the EDM03 and DSS03 processes is 1 level, while the gap values for the APO12 and DSS05 processes are 3 levels. Process improvement recommendations refer to COBIT 2019 best practices.
CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING Raihan Mufid Setiadi; Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.53

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Identification Types Of Student Learning Modalities In Physics Subjects With Expert Systems Using Bayes Theorem Method Muhammad Ukkasyah; Yunita Yunita; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.54

Abstract

Learning modality is a person's way of absorbing and processing information effectively and efficiently. This study aims to determine the results of the identification types of student learning modalities in physics subjects with an expert system using the Bayes theorem method, and the accuracy of the Bayes theorem method in identifying types of student learning modalities in physics subjects. This study uses the Bayes theorem method because it can produce a parameter estimate by combining information from the sample and other information that has been previously available to determine the results of the learning modality. This study uses 21 characteristics of learning modalities, 3 types of learning modalities, and 30 test cases obtained from an expert physics teacher at SMA Sumsel Jaya Palembang. Based on the tests that have been carried out, the results show that the system has an accuracy of 90% in identifying types of student learning modalities in physics subjects. It can be concluded that the Bayes theorem method can be used to identify types of student learning modalities in physics subjects.

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