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Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia
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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 18 Documents
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Search results for , issue "JUITA Vol. 11 No. 2, November 2023" : 18 Documents clear
Sentiment Analysis of the Public Towards the Kanjuruhan Tragedy with the Support Vector Machine Method Martin Parhusip; Sudianto Sudianto; Tri Ginanjar Laksana
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17405

Abstract

A tragedy occurred in the Indonesian football world during the Arema vs. Persebaya match on October 1, 2022, resulting in the loss of approximately 714 lives, including 131 fatalities and 583 injuries. The tragedy is believed to have been caused by tear gas in the spectator stands and the closure of exits at the Kanjuruhan stadium. This event sparked a diverse range of public responses on social media, which can be analyzed through sentiment analysis. In this study, we employed the Support Vector Machine (SVM) algorithm, known for its speed and accuracy in text classification, to process and analyze tweets from October 1 to 31, 2022, as well as YouTube comments related to the Kanjuruhan tragedy from October 1 to November 20, 2022. Among the different SVM kernels, the RBF kernel exhibited the highest accuracy, precision, recall, and F1 scores, reaching 76.40%, 75.74%, 76.40%, and 75.18% respectively, when predicting data with three labels. Furthermore, the RBF kernel showed the best performance for data with two labels, achieving the highest accuracy, precision, recall, and F1-Score, which increased to 81.54%, 81.56%, 81.54%, and 81.56%, respectively.
Facebook Prophet Model with Bayesian Optimization for USD Index Prediction Ahmad Fitra Hamdani; Daniel Swanjaya; Risa Helilintar
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17880

Abstract

Accuracy is the primary focus in prediction research. Optimization is conducted to improve the performance of prediction models, thereby enhancing prediction accuracy. This study aims to optimize the Facebook Prophet model by performing hyperparameter tuning using Bayesian Optimization to improve the accuracy of USD Index Value prediction. Evaluation is conducted through multiple prediction experiments using different ranges of historical data. The results of the study demonstrate that performing hyperparameter tuning on the Facebook Prophet model yields better prediction results. Prior to parameter tuning, the MAPE indicator metric is 1.38% for the historical data range of 2014-2023, and it decreases to 1.33% after parameter tuning. Further evaluation shows improved prediction performance using different ranges of historical data. For the historical data range of 2015-2023, the MAPE value decreases from 1.39% to 1.20%. Similarly, for the data range of 2016-2023, the MAPE decreases from 1.12% to 0.80%. Furthermore, for the data range of 2017-2023, there is a decrease from 0.80% to 0.76%. This is followed by the data range of 2018-2023, with a decrease from 0.75% to 0.70%. Lastly, for the data range of 2019-2023, there is a decrease from 0.63% to 0.55%. These results demonstrate that performing Hyperparameter Optimization using Bayesian Optimization consistently improves prediction accuracy in the Facebook Prophet model.
Implementation of Convolutional Neural Network Method in Identifying Fashion Image Christian Sri Kusuma Aditya; Vinna Rahmayanti Setyaning Nastiti; Qori Raditya Damayanti; Gian Bagus Sadewa
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17372

Abstract

The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.
Sikarju: Expert System of Major Recommendation to Increase the Chances of Being Accepted by University Siti Izati Nabila; Ami Anggraini Samudra; Irsyadunas Irsyadunas
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17424

Abstract

Major is one of the important factors in the world of lectures. Along with the increasing need for knowledge and skills required in the world of work, increasing the number of majors offered by tertiary institutions. The number of considerations from prospective students regarding the selection of majors causes students to be confused in determining the best major they will choose to continue their education. The research aims to design an expert system-based website that will be used to provide major recommendations. The method to be used is the forward chaining method, where this method works by matching data based on predetermined facts, then obtaining results based on matching the data. Based on the black box testing that has been done, the results show that the designed expert system is by the expected functionality. Therefore this expert system can be said to be feasible to use.
Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest Helma Herlinda; Muhammad Itqan Mazdadi; Muliadi Muliadi; Dwi Kartini; Irwan Budiman
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17920

Abstract

Social media has exerted a significant influence on the lives of the majority of individuals in the contemporary era. It not only enables communication among people within specific environments but also facilitates user connectivity in the virtual realm. Instagram is a social media platform that plays a pivotal role in the sharing of information and fostering communication among its users through the medium of photos and videos, which can be commented on by other users. The utilization of Instagram is consistently growing each year, thereby potentially yielding both positive and negative consequences. One prevalent negative consequence that frequently arises is cyberbullying. Conducting sentiment analysis on cyberbullying data can provide insights into the effectiveness of the employed methodology. This research was conducted as an experimental research, aiming to compare the performance of Random Forest and Random Forest after applying the Particle Swarm Optimization feature selection technique on three distinct data split compositions, namely 70:30, 80:20, and 90:10. The evaluation results indicate that the highest accuracy scores were achieved in the 90:10 data split configuration. Specifically, the Random Forest model yielded an accuracy of 87.50%, while the Random Forest model, after undergoing feature selection using the Particle Swarm Optimization algorithm, achieved an accuracy of 92.19%. Therefore, the implementation of Particle Swarm Optimization as a feature selection technique demonstrates the potential to enhance the accuracy of the Random Forest method.
Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart Karisma Dwi Fernanda; Arifin Puji Widodo; Julianto Lemantara
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17341

Abstract

Sakinah Mart is a retail business that focuses on determining the layout of goods based on perceptions and implementing a discount system for specific items, but without offering bundling packages. This research aims to provide recommendations using the apriori algorithm as a decision-making tool for analyzing the layout of goods and bundling packages. The apriori algorithm is a data mining technique used to discover association rules and analyze customer purchases, specifically identifying the likelihood of customers buying item X along with item Y. The algorithm consists of two main components: support and confidence. The research applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, utilizing the apriori algorithm to analyze sales transaction data. The dataset includes 2000 sales transactions with two attributes, resulting in the identification of 2 and 3 itemsets. The findings include 16 rules with a minimum support value of 42% and a minimum confidence of 85% for the layout of goods. For bundling packages, 5 rules with a minimum support value of 40% and a minimum confidence of 90% were generated. These results offer valuable recommendations to the company, using the apriori algorithm for analyzing the layout of goods and bundling packages. 
Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia Laradea Marifni; I Made Sumertajaya; Utami Dyah Syafitri
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17446

Abstract

Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4.
Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.21387

Abstract

Editor Preface and Table of Content Vol 11 No. 2 November 2024

Page 2 of 2 | Total Record : 18

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