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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 400 Documents
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MAC Address Classification in Privacy Issue Using Gaussian Naïve Bayes Imam Riadi; Abdul Fadlil; Basit Adhi Prabowo
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.22571

Abstract

There have been several initiatives within standards committees to overcome privacy issues, including user tracking activity based on Media Access Control (MAC) addresses. The implementation of randomized MAC addresses on captive portals, with user-specific connection limits to address privacy concerns, introduces some problems. To address this issue, device removal based on OUI classification was proposed. Connection data taken from the RADIUS server were divided into two distinct classes, either random or not. Gaussian Naïve Bayes was utilized to classify the data with 16 distinct thresholds, and the solution with the highest accuracy was selected. The research produced results showing that all classifications had an accuracy above 96%. Values of 6 and 50% for Mac address thresholds and random percentage thresholds gave the highest accuracy of 98.1139%. This indicates that random Mac address classification in the real world can be done using the result.
LSTM Algorithm in Predicting Chronic Kidney Disease Optimized Using Genetic Algorithm Brillyando Magathan Achmad; Siti Sa'adah; Isman Kurniawan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.22965

Abstract

Chronic Kidney Disease is a health condition in which the kidneys experience a progressive decline in function. Kidneys are vital organs that filter waste and excess blood fluids. CKD can lead to excess products in the body and cause various health issues, so early detection of CKD is necessary. While traditional machine learning techniques have performed well in predicting CKD in existing studies, this study investigates the potential of long short-term memory (LSTM) optimized with Genetic Algorithm to enhance predictive accuracy and efficiency by optimizing its hyperparameters, including number of units, hidden layers, activation function, recurrent activation, and dropout rate. The result demonstrates that the optimized LSTM slightly performs better than without optimization, achieving higher precision, recall, accuracy, and f1 score by 100% respectively. This outstanding result can be attributed to several key factors, such as ensuring rigorous data preprocessing and utilizing k-fold cross-validation to make the model more reliable. This indicates the hybrid approach can be a powerful method for the early detection of CKD, leading to better patient outcomes. Despite the promising performance, further research is suggested, specifically using a larger dataset to ensure applicability to more general population and exploring other optimization methods to reduce computational cost.
Combining Oversampling and Pretrained Feature Extractor For Classification Diabetic Foot Uclear Thermogram Images I Wayan Jepriana; I Gede Bintang Arya Budaya; Gede Angga Pradipta; Putu Desiana Wulaning Ayu
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23386

Abstract

Diabetic Foot Ulcers (DFUs) represent a significant health concern, often leading to severe complications if not diagnosed and treated promptly. Early and accurate classification of DFUs is crucial for effective patient management. However, In the realm of machine learning, the imbalanced data problem is a prevalent issue that arises when the classes in a dataset are not represented equally. This study proposes a novel approach to enhance the classification performance of DFU thermogram images by integrating oversampling techniques with pretrained feature extractors. This study use pretrained model method with InceptionV3 architecture to automatically obtain features in the DFU thermogram datasets. Overall, InceptionV3 as a feature extractor resulted in satisfactory performance, achieving an accuracy of 83.1% on non-diabetic data and 81.1% on diabetic data. Subsequently, the second experiment incorporated the oversampling technique SMOTE, leading to an improvement in performance, with accuracy rising to 98.1% on non-diabetic data and 96.1% on diabetic data. Finally, the SMOTE IPF method achieved accuracy of 98.7%, with a precision of 99.1% for the diabetic class and 98.7% for the non-diabetic class, a recall of 98.2% for the diabetic class and 98.1% for the non-diabetic class, and F-Measure of 98.1% for both the diabetic and non-diabetic classes.
Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network Siti Aisyah; Yenni Angraini; Kusman Sadik; Bagus Sartono; Gerry Alfa Dito
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23464

Abstract

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.
Analysis of K-NN with the Integration of Bag of Words, TF-IDF, and N-Grams for Hate Speech Classification on Twitter Kuncoro Hadi; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23829

Abstract

Social media has emerged as one of the primary communication channels in the modern world, but it has simultaneously become a platform where hate speech can spread easily. This study attempts to evaluate the performance of a hate speech classification model using the K-Nearest Neighbors (K-NN) algorithm along with various feature extraction techniques, specifically Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and N-Grams. The dataset used in this study consists of 13169 entries, which represent a diverse range of hate speech examples commonly encountered on social media platforms. In this experimental investigation, we assess the efficacy of the model using each feature extraction technique. The findings reveal that the K-NN model exhibits optimal performance when the k parameter is set to 3 (k=3). Under this configuration, the model achieves an accuracy of 86.88%, with a precision of 88.27%, a recall of 86.88%, and an F1-Score of 86.50%. These results show that the integration of TF-IDF feature extraction technique with K-NN algorithm produces superior performance in hate speech classification.
Time Complexity of Knuth Morris Algorithm and Rejang Algorithm in Rejang-Indonesian Translator Sastya Hendri Wibowo; Rozali Toyib; Yulia Darnita; Satria Abadi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23954

Abstract

Among the pattern-matching algorithms is the Knuth-Morris algorithm. In order to minimize the number of comparisons required and, in the worst scenario, achieve an ideal O(n+m) running time, the Knuth-Morris search algorithm skips unneeded comparisons. Every character in the text and every character in the pattern must be checked at least once by the pattern-matching algorithm. The Knuth-Morris algorithm's primary goal is to preprocess the pattern string P in order to determine the failure function f, which displays P's precise shift, so that earlier comparisons can be reused. In order to extract the fundamental word of the attached sentence, words containing affixes are separated using the Rejang stemming method. The purpose of this research is to determine the time complexity of the Rejang method and the Knuth-Morris algorithm based on affix groups. The Rapid Application Development (RAD) approach, which entails planning, designing, building, and implementing, is used during the research stages. The research results have produced efficient and effective Knuth Morris algorithm and Rejang algorithm, where efficiency is indicated by the algorithm time complexity of O (log n), and effectiveness is indicated by the accuracy results of 99% against testing 6000 affixed words.
Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.24429

Abstract

Editor Preface and Table of Content JUITA Vol.12 Issue 2
Application of LSB Steganography for Encrypted Data Using Triple Transposition Vigenere on Digital Images Eko Aribowo; Windy Sayyida Amalya; Nur Rochmah Dyah Puji Astuti
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.24592

Abstract

Data security threats emerge when sensitive information is transmitted without sufficient protection, exposing it to unauthorized access. Steganography, particularly the Least Significant Bit (LSB) technique, is widely adopted due to its simplicity and minimal impact on digital image quality. Nevertheless, it is prone to detection through steganalysis attacks. This research improves the LSB method by incorporating Vigenere and Triple Transposition algorithms. The Vigenere algorithm secures text by shifting characters based on a key, mitigating the limitations of single permutations, but it remains vulnerable to frequency analysis if the key is too short. Triple Transposition enhances security by applying three rounds of encryption with distinct keys, making decryption significantly harder. This study utilizes grayscale and RGB images from the USC SIPI database. Grayscale images offer advantages in terms of storage and algorithm efficiency, while RGB images provide broader color diversity and versatile applications. By combining these methods, the proposed approach strengthens data security, ensuring embedded messages are more resilient against advanced steganalysis and unauthorized decryption attempts. The integration aims to improve the robustness of LSB steganography, addressing its limitations while effectively securing sensitive information
K-Means Centroid Optimization with Genetic Algorithm for Clustering Micro, Small, Medium Enterprises in Yogyakarta Muhammad Faris Akbar; Lisna Zahrotun
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25480

Abstract

K-Means is a widely used data clustering algorithm due to its simplicity and fast performance. However, the weakness of K-Means is in determining the cluster centroid randomly, which can result in suboptimal clustering results, especially since it tends to get stuck on local solutions. This research aims to overcome this weakness by integrating the Genetic Algorithms (GA) into the K-Means process, optimizing the initial centroid, and improving clustering quality. The method combines GA with K-Means on MSME data in Yogyakarta, where GA rearranges the cluster's initial centroid more optimally. The results showed that this method reduced the average value of the Davies-Bouldin Index (DBI) from 1,819 in conventional K-Means to 1,349 with GA integration, indicating an improvement in cluster quality by 25.9%. These results prove that integration of GA with K-Means improves clustering accuracy and improves cluster separation, as measured by a significant decrease in DBI value
Course Scheduling Using Genetic Algorithms Enhanced by Linear Regression for Data Mining Course Participants Arie Susetio Utami; Agust Isa Martinus; Freddy Wicaksono; Rangga Manggala Yudha
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25598

Abstract

Course scheduling at the beginning of each semester is an absolute must, considering changes in course instructors, changes in the availability of lecture schedules, changes in lecture infrastructure in terms of number, capacity, and time of use, changes in the number of lecture participants, both new and repeat participants. This research aims to design an optimal scheduling system by considering scheduling constraints to avoid conflicts and increase the effectiveness of lecture scheduling management. The linear regression method is used to predict the number of lecture participants using the 2019-2022 academic year data as model data and the 2023 academic year data as testing data to validate the prediction data. Lecture scheduling uses a Genetic Algorithm with a fitness function for the number of cross-class schedules - contracted by repeating students - that conflict with the chromosomes used by courses, classes, lecturers, rooms, schedules, and others. The designed scheduling system has a prediction model with high accuracy and a coefficient of determination (R-Sq.) above 95% and RMSE below 10. This scheduling system is efficient, minimizing scheduling conflicts to 0 percent

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