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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 316 Documents
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Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis Abdul Fadlil; Imam Riadi; Fiki Andrianto
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.v12i1.19798

Abstract

Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.
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.
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
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.
Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model Joko Siswanto; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
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.v12i1.20210

Abstract

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.
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.
Improve Coal Blending Optimization in CFPP by Cromosom and Fitness Function Redefinition of the Genetic Algorithm Binti Solihah; Ahmad Zuhdi; Abdul Rochman; Edo Yulistama; Hilda Dwi Utari
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.v12i1.18731

Abstract

Blending coal before it enters the power plant boiler unit is necessary to adjust the coal categories according to the boiler unit specifications. The power plant must also comply with the regulations regarding coal-biomass co-firing through blending. Applying a Genetic Algorithm that only considers the composition and fitness based on the blend's quality leads to accumulation issues, decreasing coal quality. This research proposes redefining chromosomes, fitness functions, mutation rules, population determination, and output as the best chromosome used in the Genetic Algorithm. Testing uses various compositions of coal inputs from the barge, coal yard, and biomass to simulate different conditions. The test results demonstrate that the developed algorithm can provide all possible alternative blends between the coal in the barge and at the coal yard. Under specific conditions, operators can choose a blend composition that involves coal stored in the coal yard for an extended period.
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.
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.
Improving Stroke Detection with Hybrid Sampling and Cascade Generalization Widya Putri Nurmawati; Indahwati Indahwati; Farit Mochamad Afendi
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.v12i1.19386

Abstract

The prevalence of stroke in Indonesia has increased. One survey in Indonesia that contains information about the health conditions of the Indonesian people is the Indonesian Family Life Survey (IFLS). The proportion of respondents who had a stroke and non-stroke in IFLS5 showed an imbalance with an extreme level of imbalance; hence, this research aims to overcome this problem with SMOTE, SMOTE-Tomek Link, and SMOTE-ENN; then, the balanced dataset is classified using the ensemble and cascade approaches to improve the detection of stroke risk and to identify the important variables. However, the stroke respondents were still challenging to classify after imbalance class handling, presumably because of the large amount of data before and after balancing. The solution is to balance the training data with various percentages. The results showed the best percentage is applied to 5% of the training data, balanced by the SMOTE-ENN, and the ensemble method with the cascade approach increases the sensitivity and balanced accuracy values. Random forest and logistic regression combine models that produce the best performance, with a classification tree as the final model. The important variables obtained from this combination are the addition of probability from random forest, logistic regression, history of hypertension, age, and physical activity.

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