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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 30 Documents
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Search results for , issue "JUITA Vol. 12 No. 1, May 2024" : 30 Documents clear
Implementation of Backpropagation Neural Network for Prediction Magnetocaloric Effect of Manganite Jan Setiawan; Silviana Simbolon; Yunasfi Yunasfi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20452

Abstract

In the field of magnetic cooling technology, there is still much to learn about the magnetocaloric properties of magnetic cooling materials. Research into magnetocaloric manganites exhibiting a significant maximum magnetic entropy change in the vicinity of ambient temperature yields encouraging outcomes for the advancement of magnetic refrigeration apparatus. Through a combination of chemical substitutions, changes in the amount of oxygen present, and different synthesis techniques, these manganites undergo lattice distortions that result in pseudocubic, orthorhombic, and rhombohedral structures instead of perovskite cubic structures. The present investigation used backpropagation neural networks (BPNNs) to investigate the correlations among maximum magnetic entropy change (MMEC), Curie temperature (Tc), lanthanum manganite compositions, lattice properties, and dopant ionic radii. Simbrain 3.07 was used to execute the BPNN model, and the suggested model accuracy was examined using coefficient determination. As a result, the model's predicted values for the mean absolute error, root mean square, and coefficient correlation for MMEC are 0.012, 0.022, and 0.9861, respectively. The model predicts that the Curie temperature mean absolute error, root mean square, and coefficient correlation will be 0.015, 0.021, and 0.9947, respectively. Based on these results, BPNN has the potential to be applied in predicting the MMEC and Tc of manganite as preliminary decision during experiments.
Comparative Analysis of CNN Architectures for SIBI Image Classification Yulrio Brianorman; Dewi Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20608

Abstract

The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-pixel picture dataset was used for the study. A five-stage technique consisting of Dataset Collection, Dataset Preprocessing, Model Design, Model Training, and Model Testing was applied. Performance evaluation focused on accuracy, precision, recall, and F1-Score. The results identified VGG16 as the top-performing model with an accuracy of 99.60% and an equivalent F1-Score, followed closely by ResNet50 with nearly similar performance. Inception, XCeption, and MobileNetV2 demonstrated balanced performance but with lower accuracy. This study sheds light on the best CNN models to choose for SIBI image classification, and it makes recommendations for further research that include using sophisticated data augmentation methods, investigating novel CNN architectures, and putting the models to practical use.
Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization Muhammad Rizki; Arief Hermawan; Donny Avianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20688

Abstract

This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.
Enhancing Durrotalk Chatbot Accuracy Utilizing a Hybrid Model Based on Recurrent Neural Network (RNN) Algorithm and Decision Tree Dede Rizki Darmawan; Riza Arifudin
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20868

Abstract

DurroTalk, a chatbot for new student admissions at Pondok Pesantren Durrotu Ahlissunnah Waljamaah, Semarang, integrates a hybrid model with Recurrent Neural Network (RNN) and Decision Tree. RNN, the base model, employs Natural Language Processing (NLP) to understand sentence structure and context, overcoming vanishing gradient through LSTM layers. The Decision Tree normalizes words, addressing slang and synonyms. The hybrid model boosts chatbot accuracy by 9%, reaching 77% from the initial 68%. This research signifies progress in integrating artificial intelligence into traditional education, showcasing a chatbot adept at handling non-standard language. Decision Tree integration enhances overall performance, making the chatbot proficient in understanding user inputs and generating contextually relevant responses. This study exemplifies the potential of AI, particularly chatbot technology, in modernizing educational processes at traditional institutions.
Enhancing Information Technology Adoption Potential in MSMEs: a Conceptual Model Based on TOE Framework Ainayah Syifa Hendri; Endah Sudarmilah
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21051

Abstract

The adoption of Information Technology (IT) by Micro, Small, and Medium Enterprises (MSMEs) has become essential in the digital era. Nevertheless, challenges persist, such as enhancing IT adoption in the MSMEs sector and optimizing its benefits. This research aims to create a comprehensive model based on the Technology- Organization-Environment (TOE) framework by analyzing technological, organizational, and environmental factors influencing IT adoption among MSMEs in Pangandaran, Indonesia. Employing a quantitative approach, an online questionnaire was distributed to MSMEs, and data were analyzed using Partial Least Square-Structural Equation Modeling (PLS- SEM) through SmartPLS. The study significantly contributes to understanding IT adoption, emphasizing organizational context as the primary predictor, followed by technological and environmental contexts. Positive relationships were found between four contextual constructs: complexity, top management support, organizational readiness, and competitive pressure towards IT adoption in MSMEs. Conversely, compatibility and government support exhibited negative impacts. These findings have practical implications for Indonesian MSMEs by enhancing understanding of factors influencing IT adoption to support business operations. Furthermore, these findings hold the potential to assist MSMEs and the Indonesian government in optimizing IT adoption success. The generated data can be employed by MSMEs management authorities to devise strategies for enhancing IT adoption among MSMEs.
Face Gender Classification using Combination of LPQ-Self PCA Tio Dharmawan; Danu Adi Nugroho; Muhammad Arief Hidayat
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21137

Abstract

The age factor had a significant impact on human faces, potentially influencing the performance of existing gender classification systems. This research proposed a new method that combined local descriptors such as Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) with Self-Principal Component Analysis (Self-PCA) as a feature extraction technique. The use of Self-PCA was chosen for its ability to address the age factor in human facial images, while also leveraging local descriptors to capture features from these images. The primary focus was to compare the performance of Self-PCA with LPQ+Self-PCA, along with the additional comparison of LBP+Self-PCA, in the task of gender classification using facial images. Euclidean distance served as the classifier, and the evaluation was conducted using the FG-Net and ORL datasets. The combination of LPQ+Self-PCA showed an improvement in accuracy by 57.85% compared to the combination of LBP+Self-PCA, which provided an accuracy of 56.47%. Meanwhile, using Self-PCA alone gave an accuracy of 55.37% on the FG-Net. In contrast, on the ORL dataset, both combinations gave the same accuracy result as Self-PCA, which was 90.14%, for images without blurring.
Image Classification of Room Tidiness Using VGGNet with Data Augmentation Leni Fitriani; Ayu Latifah; Moch. Rizky Cahyadiputra
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21204

Abstract

Tidiness becomes an essential aspect that everyone should maintain. Tidiness encompasses various elements, and one of the aspects closely related to it is the tidiness of a room. The tidiness of a room creates a comfortable and clean environment. The tidiness of a room is particularly crucial for individuals involved in businesses such as the hospitality industry. Therefore, a solution is needed to address this issue, and one of the approaches is to utilize Deep Learning for automatic room tidiness classification. One popular deep learning method for implementing image classification of room tidiness is the convolutional neural network (CNN), which creates a well-performing model for image classification with data augmentation. This research aims to develop an image classification model using CNN with the VGGNet architecture and data augmentation. This study is a reference for further development, with potential applications in the hospitality industry. The research results in a model that achieves an accuracy of 98.44% with a data proportion of 90% for training and validation, while the remaining 10% is used for testing purposes. The conclusion drawn from this study is that the CNN method, combined with data augmentation, can be utilized for image classification of room tidiness.
Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors Embay Rohaeti; Ani Andriyati
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21491

Abstract

Analysing pre-COVID-19 unemployment in West Java is vital for comprehending and tackling Indonesia’s economic challenges. This significance arises not only due to the region’s high unemployment rate, but also from the need to understand unemployment patterns before COVID-19, which has become more relevant now during the country’s post-pandemic recovery phase. This study evaluates four machine learning models (Random Forest, Linear SVM, RBF SVM, and Polynomial SVM) to classify employment status using demographic and job-related variables. The objective is to find the most suitable model, particularly considering the imbalanced nature of the study-case data. Data from the National Labor Force Survey (SAKERNAS) in August 2019 is utilized, comprising 54,429 respondents across districts in West Java. The four models are evaluated using holdout validation with a 70:30 stratified proportion, repeated for 100 times. Results indicate that the random forest model outperforms others in balanced accuracy, F1-score, and computational time. The random forest model also underscores the importance of gender and age in classifying employment status in West Java, suggesting a need for targeted intervention, especially for female citizens and individuals in productive age groups.
BCBimax Biclustering Algorithm with Mixed-Type Data Hanifa Izzati; Indahwati Indahwati; Anik Djuraidah
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21519

Abstract

The application of biclustering analysis to mixed data is still relatively new. Initially, biclustering analysis was primarily used on gene expression data that has an interval scale. In this research, we will transform ordinal categorical variables into interval scales using the Method of Successive Interval (MSI). The BCBimax algorithm will be applied in this study with several binarization experiments that produce the smallest Mean Square Residual (MSR) at the predetermined column and row thresholds. Next, a row and column threshold test will be carried out to find the optimal bicluster threshold. The existence of different interests in the variables for international market potential and the number of Indonesian export destination countries is the reason for the need for identification regarding the mapping of destination countries based on international trade potential. The study's results with the median threshold of all data found that the optimal MSR is at the threshold of row 7 and column 2. The number of biclusters formed is 9 which covers 74.7% of countries. Most countries in the bicluster come from the European Continent and a few countries from the African Continent are included in the bicluster.
Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21893

Abstract

Editor Preface and Table of Content Vol 12 No. 1 May 2024

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