Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
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962 Documents
<![CDATA[IMPLEMENTATION OF THE ELECTRE METHOD IN THE RECOMMENDATION SYSTEM AND API SERVICE PROVISION FOR TOURIST DESTINATIONS IN BANYUMAS REGENCY WITH INTERACTIVE MAPPING ]]>
<![CDATA[Guntur Satya Pramudya]]>;
<![CDATA[Permadi, Ipung]]>;
<![CDATA[Chasanah, Nur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.1.1768
<![CDATA[The Banyumas Regency is a region with a diverse range of tourist attractions, making tourism one of its crucial economic sectors. The multitude of tourist destinations in Banyumas often poses a challenge for visitors in choosing destinations that align with their preferences. To address this issue, this research applies the Elimination et Choix Traduisant la Réalité (ELECTRE) method in multi-criteria decision-making. The objective of this study is to provide recommendations for suitable tourist destinations based on tourists' interests, along with an interactive mapping feature that offers a geographical overview and distance information for these destinations. Recognizing the importance of accessible tourism information, this research also implements Application Programming Interface (API) services to facilitate the integration of tourist destination data into various applications and platforms. The system is built as a web-based application using the Laravel framework and MySQL database. The output of this research is a web-based recommendation system that can be used to help tourists who want to vacation according to their preferences and the API service helps interested parties to integrate tourism data into their system.]]>
<![CDATA[SENTIMENT ANALYSIS OF ICT SERVICE USER USING NAIVE BAYES CLASSIFIER AND SVM METHODS WITH TF-IDF TEXT WEIGHTING ]]>
<![CDATA[Trisnawati, Wulan]]>;
<![CDATA[Wibowo, Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1784
<![CDATA[Pusintek is one of the government units in Indonesia responsible for managing Information and Communication Technology (ICT), providing various ICT services to users in central and regional offices through the ICT Service Catalog. The level of service fulfillment in Pusintek's IT Service Catalog significantly influences the effectiveness and efficiency in meeting service agreements, providing accurate information, and handling disruptions promptly. User satisfaction is measured through surveys to plan improvements to ICT services, but there is currently no method to classify sentiment from survey comment data. This research aims to classify sentiment and understand customer opinions and satisfaction trends regarding ICT services. The study applies the Naïve Bayes Classifier and Support Vector Machine (SVM) methods to classify positive and negative comments in user satisfaction surveys of ICT services. The data used consists of comments from the 2022 ICT user satisfaction survey results. Based on the test results, it is observed that the SVM algorithm provides higher accuracy compared to the Naïve Bayes algorithm. Utilizing the existing dataset with established opinion values, classification modeling using Naïve Bayes Classifier and Support Vector Machine (SVM) proves capable of classifying ICT user sentiment into 3 sentiment classes: Positive, Neutral, and Negative. From the data above, it is concluded that the SVM algorithm achieves the highest accuracy of 88.76%, highest precision of 89.68%, recall of 88.76%, and an f1-score of 89.12%.]]>
<![CDATA[PREDICTING FANTASY PREMIER LEAGUE POINTS USING CONVOLUTIONAL NEURAL NETWORK AND LONG SHORT TERM MEMORY ]]>
<![CDATA[Lombu, Anas Satria]]>;
<![CDATA[Paputungan, Irving Vitra]]>;
<![CDATA[Dewa, Chandra Kusuma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.1.1792
<![CDATA[Fantasy Premier League is a fantasy sports-based game focused on football, particularly the English Premier League. each manager in this game is given the opportunity to build a virtual team for one season. A virtual team consists of various player positions that will earn FPL points based on their real-word performance. This research aims to implement a deep learning algorithm to predict FPL points generated by players based on their performance in the last 5 matches using a dataset collected from August 14, 2021, to May 21, 2022. The prediction model is designed using a Convolutional Neural Network algorithm consisting of one-dimensional Convolution layers, Max Pooling, and Dense layers. Additionally, a Long Short Term Memory algorithm with LSTM layers and Dense layers totaling 64 units is added as a comparison model to determine the best performing deep learning model in this study. In the first scenario, with a 70:30 data ratio, the average Mean Squared Error values obtained for 4 player positions using CNN are 0.0052 and 0.0027 for LSTM. Meanwhile, in the second scenario with an 80:20 data ratio, the evaluation results are 0.0027 for CNN and 0.0022 for LSTM. the model evaluation results indicate that the LSTM algorithm, utilizing three gates in the model architecture, is superior in recognizing historical data sequences compared to the CNN algorithm.]]>
<![CDATA[PERFORMANCE TESTING OF ACADEMIC WEBSITE USING LOAD TESTING METHOD SUPPORTED BY APACHE JMETERTM AT XYZ UNIVERSITY ]]>
<![CDATA[Raweyai, Soni Sampari]]>;
<![CDATA[Widiasari, Indrastanti Ratna]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1796
<![CDATA[This study aims to evaluate the performance quality of the academic website of University XYZ through load testing using the Load Testing Method supported by Apache JMeterTM. The main issue addressed is how the website's performance can be measured and assessed in the context of normal, peak, and stress usage. The research methodology involves a qualitative approach to understand the meaning, interpretation, and context of the phenomenon, coupled with a quantitative approach to measure, analyze, and organize data in numerical or statistical forms. The research findings indicate that in the basic testing scenario, the website successfully met the test criteria with an average response time of approximately 0.855 seconds for GET requests, below the established maximum limit. POST requests required an average time of around 0.273 seconds with no response failures. In the peak testing scenario simulating high traffic conditions, the website remained optimal with average response times for both GET and POST requests below the 3-second limit, without response failures. Stress testing scenarios demonstrated the efficient operation of the website, even though the average response time for GET requests increased to approximately 2.564 seconds.The test results affirm that the University XYZ website functions well under various service usage conditions, including heavy loads. The overall average response time for GET requests across all scenarios is approximately 1.558 seconds, while POST requests have an average response time of around 0.355 seconds. Special attention is given to the impact of the number of threads or users and the number of students on the website's performance.]]>
<![CDATA[HYPERPARAMETER OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK FOR FLOWER IMAGE CLASSIFICATION USING GRID SEARCH ALGORITHMS ]]>
<![CDATA[Wibowo, Della Aulia]]>;
<![CDATA[Suciati, Nanik]]>;
<![CDATA[Yuniarti, Anny]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.1.1798
<![CDATA[Indonesia is a country with a tropical climate that greatly affects agriculture. Flowering plants are estimated to account for 25% of species in Indonesia; there are 416 families, 13,164 genera, and 295,383 species of flowering plants. Classification of profit types is a time- and knowledge-intensive job. Convolutional Neural Network (CNN) has revolutionized the field of computer vision by improving the accuracy of image, text, voice, and video recognition. This research is focused on developing a CNN model for Indonesian flower images by optimizing hyperparameters combined with a grid search algorithm and default parameters, as well as comparing two different CNN architectures, namely VGG16 and MobileNetV2. This research aims to improve the classification accuracy of Indonesian flower images by optimizing hyperparameters. The results of CNN research with hyperparameters combined with a grid search algorithm and using data augmentation resulted in MobileNetV2 as the best model. Grid search is designed to get the best value of each parameter. The performance of the grid search algorithm can produce an optimal combination of parameters, with a test accuracy of 89.62%..]]>
<![CDATA[ANALYSIS OF RAW MATERIAL INVENTORY PREDICTION FOR PLASTIC ORE USING A COMBINATION OF CAUSALITY AND TIME SERIES METHODS: A CASE STUDY IN A TEXTILE INDUSTRY COMPANY ]]>
<![CDATA[Frangky Rawung]]>;
<![CDATA[Agus Budi Raharjo]]>;
<![CDATA[Diana Purwitasari]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.1.1809
<![CDATA[Raw material inventory is a valuable company asset in production activities. Inadequate or excessive availability can lead to production failures or cost wastage. This research aims to predict raw material inventory based on factors such as initial stock, receipts, usage, final stock, and differences in usage. A causality-based approach with Multiple Linear Regression (MLR) is used as the basis, complemented by a time series data approach that processes data trends using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The prediction results from both models are then combined using the harmonic mean. This research utilizes a dataset of raw material inventory and applies the Root Mean Squared Error (RMSE) and R-squared (R²) performance parameters for model evaluation. The research is expected to provide useful information for companies in managing their raw material inventory and improving the efficiency of their production processes. Results show that, in the BiLSTM deep learning model, Polyethylene Terephthalate (PET) raw materials yielded an RMSE of 6.53 and an R² of 0.93. These results indicate that PET raw materials have a higher predictive value than other materials.]]>
<![CDATA[COMPARISON OF NAÏVE BAYES ALGORITHM AND SUPPORT VECTOR MACHINE IN SENTIMENT ANALYSIS OF BOYCOTT ISRAELI PRODUCTS ON TWITTER ]]>
<![CDATA[Hayurian, Laisha Amilna]]>;
<![CDATA[Hendrastuty, Nirwana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1813
<![CDATA[The Israeli-Palestinian conflict has captured the attention of Indonesians and even the world for decades, with the death toll reaching 17,000 Palestinians. Indonesians have expressed various opinions, including a proposed boycott of products that allegedly support Israel as a form of protest against the ongoing conflict. This study explores the opinions and sentiments of the Indonesian people regarding the Israel-Palestine conflict and the efforts to boycott Israeli products on social media twitter. This study aims to compare the accuracy of the two algorithms in classifying sentiment towards boycotting Israeli products. A total of 2288 comment data were processed using the Naïve Bayes and Support Vector Machine (SVM) algorithm classification methods. The results show that the Naïve Bayes algorithm has higher accuracy with a data division ratio of 70:30 and 80:30 for training data and testing data. Accuracy results with 70:30 data division reached 84% using the Naïve Bayes algorithm model, while the SVM algorithm model reached 78%. And the accuracy results with 80:20 data division reached 85% using the Naïve Bayes algorithm model, with the SVM algorithm model reaching 84%. This study provides an understanding of the concept of text mining and data mining and can be a reference for similar research.]]>
<![CDATA[DECISION TREE OPTIMIZATION IN HEART FAILURE DIAGNOSTICS: A PARTICLE SWARM OPTIMIZATION APPROACH ]]>
<![CDATA[Sumarna, Sumarna]]>;
<![CDATA[Sartini, Sartini]]>;
<![CDATA[Pangesti, Witriana Endah]]>;
<![CDATA[Suryadithia, Rachmat]]>;
<![CDATA[Riyanto, Verry]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1815
<![CDATA[The rapid advancement of technology has made the implementation of accurate diagnostic methods for serious diseases like heart failure extremely important. Heart failure, being a leading cause of death worldwide, necessitates precise and accurate diagnostic techniques. The problem with conventional diagnostic methods is that they often fail to effectively accommodate the complexity of clinical data, leading to an increase in mortality rates due to heart failure. Previous research has employed various data analysis methods, but there are still fluctuations in the accuracy of results. The aim of this study is to enhance the accuracy of heart failure diagnosis by integrating the Decision Tree (DT) method with Particle Swarm Optimization (PSO) optimization. This research involves collecting and preprocessing heart failure data, followed by the development of a DT model. This model is then optimized using the PSO technique. The study uses a dataset from the UCI Repository, involving testing and validation processes to measure the model's effectiveness. The results show a significant improvement in accuracy and the Area Under Curve (AUC) after applying PSO. Accuracy increased from 79.92% to 85.29%, and AUC from 0.706% to 0.794%. The conclusion is that the integration of DT and PSO successfully improved the accuracy and reliability of the model in diagnosing heart failure. This innovation offers potential for further research in integrating optimization techniques in health data analysis, with the possibility of application in various clinical scenarios.]]>
<![CDATA[COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA ]]>
<![CDATA[Elisa, Putri]]>;
<![CDATA[Isnain, Auliya Rahman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.1.1817
<![CDATA[Advances in technology, especially the internet, have significantly changed the way people communicate, including social media. Social media facilitates more effective and efficient online communication. Twitter has 18.45 million users in Indonesia by 2022. Discussion of mental health stigma on twitter, increased 17% in 2021 compared to the previous year. Lifestyle transformation, social pressures, and technological advancements have created new challenges in maintaining individual mental health. Discussions of mental health issues have become pros and cons on twitter. The tendency of twitter users in posting content can be known by means of sentiment analysis. Therefore, sentiment analysis can be used to classify comments and tweets related to mental health stigma into negative, positive and neutral. So, it is expected to provide a number of significant benefits in the aspect of managing mental health issues. The methods used to analyze sentiment towards mental health stigma are Random Forest, Support Vector Machine (SVM) and Naïve Bayes algorithms. Based on the research that has been done, it produces 3,095 data for the period 2020-2023. After preprocessing and labeling the data, 1,635 data (negative class), 633 data (positive class) and 208 data (neutral class) were obtained. The SVM model test results show an accuracy of 86.11%, the Random Forest model shows an accuracy of 82.55%, while the Naive Bayes model shows an accuracy of 78.19%. Therefore, it can be concluded that SVM has the best performance in classifying tweets containing mental health stigma.]]>
<![CDATA[SURICATA ACCURACY OPTIMIZATION BASED ON LIVE ANALYSIS USING ONE-CLASS SUPPORT VECTOR MACHINE METHOD AND STREAMLIT FRAMEWORK ]]>
<![CDATA[Agus Ariwanta, I Putu Yesha]]>;
<![CDATA[Ernanda Aryanto, Kadek Yota]]>;
<![CDATA[Gunadi, I Gede Aris]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.2.1822
<![CDATA[Based on data from the Checkpoint website, there are more than 10 million cyber-attacks in a single day, and the top sequence of this cyber-attack is evident in educational institutions. The IT unit of Kartini Bali Health Polytechnic has not yet conducted testing for accuracy and speed to detect suspicious activities on the computer network. The implementation of network security systems that have not undergone testing will undoubtedly have a negative impact on system providers and users. The application of Live Analysis based on a website and the One-Class Support Vector Machine (SVM) is used to optimize the capabilities of the Suricata in detecting suspicious activities on computer networks and providing visual and real-time reports. This research utilizes the Suricata for optimizing the computer network security system, with the researcher using the Streamlit Framework for Live Analysis based on a website and the One-Class Support Vector Machine (SVM) for classifying log data and visual reporting. For testing the computer network security system, tools such as Nmap, Loic, and Brutus are used. The results of the research using the One-Class Support Vector Machine (SVM) in detecting three types of attacks Port Scanning, DDOS Attack, and Brute Force Attack, show an accuracy value of 96%, precision of 95%, recall of 96%, and F1-Score of 95%. In the performance and load testing of the live analysis system using the Streamlit framework, the results show that the developed system is responsive, with CPU usage at 38%, memory usage at 62.3%, and an average system load time of 5 milliseconds.]]>
