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[OPTIMIZING YOLOV8 FOR AUTONOMOUS DRIVING: BATCH SIZE FOR BEST MEAN AVERAGE PRECISION (MAP) ]]>
<![CDATA[Hidayat, Zaids Syarif]]>;
<![CDATA[Wijaya, Yudhistira Arie]]>;
<![CDATA[Kurniawan, Rudi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1626
<![CDATA[Artificial intelligence (AI), especially computer vision, has made rapid progress in recent years. One of the rapidly developing fields in computer vision is object detection. The ability to detect objects accurately and quickly is essential for the development of autonomous driving technology or vehicles that can operate automatically without human intervention. However, the development of autonomous driving technology is still facing various challenges, especially related to the accuracy and speed of object detection by the system. The purpose of this study is to analyze the performance based on the mean average precision (mAP) value of the results of adjusting the number of epochs, batch size, and image size on one of the emerging object detection methods, YOLOv8, in the context of autonomous driving. The analysis focuses on the batch size hyperparameter on the object detection performance of YOLOv8. The research was conducted with an experimental approach where the YOLOv8 hyperparameters were modified and their performance was evaluated using the driver simulation dataset from the CARLA simulator. Object detection performance was evaluated using the mean average precision (mAP) metric. The research results with the highest mAP value are found in scheme VIII with an mAP value of 98.2% and a training time of 59.45 minutes. For scheme III, it gets the fastest training time of 36.25 minutes. Based on the mAP results, modifications to the number of batch sizes and the use of high image sizes can affect the performance and performance of object detection for autonomous driving.]]>
<![CDATA[RECOMMENDATION SYSTEM FOR SELECTING HAIRCUT MODELS BASED ON FACIAL SHAPE USING THE VIOLA-JONES METHOD ]]>
<![CDATA[Samsudin, Samsudin]]>;
<![CDATA[Ramadhani, Risky Aswi]]>;
<![CDATA[Sanjaya, Ardi]]>
<![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.2024.5.1.1628
<![CDATA[Face shape and hairstyle are two interrelated elements that will affect a person's overall appearance. The shape of the face has different shapes, such as round, square, oval, diamond, and heart-shaped, choosing a hairstyle must adjust the shape of the face in order to get the appropriate results. The problem that often arises when getting a haircut is choosing a hairstyle that does not match the shape of the face. The purpose of this research is to develop a system that can assist users in knowing the shape of the face and can provide recommendations for haircuts that match the shape of the face. This type of research uses the viola-jones method with the stages of haar-like features, integral images, AdaBoost Machine Learning, cascade classifier. The results of this study show that the system can detect the shape of the user's face and produce recommendations for haircut models based on face shape. The results of direct trials of the haircut model selection system based on face shape on 30 respondents resulted in 20% not suitable and 80% of the 30 respondents felt suitable with the results of selecting a haircut model based on face shape using the Viola-Jones method. The Viola-Jones method tested with the confision matrix obtained an accuracy value of 43%.]]>
<![CDATA[COMBINATION K-MEANS AND LSTM FOR SOCIAL MEDIA BLACK CAMPAIGN DETECTION OF INDONESIA PRESIDENTIAL CANDIDATES 2024 ]]>
<![CDATA[Priambodo, Wisnu]]>;
<![CDATA[Zuliarso, Eri]]>
<![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.2024.5.2.1635
<![CDATA[Social media has become the main platform for the public and political figures to voice opinions and run political campaigns. Despite its positive impact, social media also has negative impacts, particularly in the spread of Black Campaigns. This phenomenon has become critical, especially about the 2024 elections in Indonesia that target presidential candidates. Black campaigns can trigger conflict and damage the image of presidential candidates in the eyes of the public. Therefore, it is important to detect black campaigns against presidential candidates. This research develops a Black Campaign detection model using the K-means clustering algorithm and the Long Short-Term Memory (LSTM) approach. K-means is implemented to cluster text data on Twitter social media, while LSTM is used to learn word order patterns and detect text. The result is that K-means can effectively prepare the data, and classification using LSTM shows an accuracy of 90.28%. The comparison with Ensemble Learning classification model achieved an accuracy of 94.31%. Evaluation involved accuracy, precision, recall, and F1-score, with the result that Ensemble Learning was slightly superior in the evaluation matrix. However, compared to Ensemble Learning, LSTM has an advantage in understanding word order, which can be achieved by utilizing the advantages of Deep Learning Recurrent Neural Network architecture. Testing on sample data shows the similarity between LSTM and Ensemble Learning models in detecting Black Campaigns on Twitter social media post text data.]]>
<![CDATA[ENHANCING NETWORK PERFORMANCE LOAD BALANCING IN CYBER CAFE NETWORKS WITH DIJKSTRA ALGORITHM ON MIKROTIK ]]>
<![CDATA[Prayogi, Andi]]>;
<![CDATA[Kan, Phak Len Al Eh]]>;
<![CDATA[Pane, Muhammad Akbar Syahbana]]>;
<![CDATA[Dian, Rahmad]]>;
<![CDATA[Siregar, Ratu Mutiara]]>;
<![CDATA[Simbolon, Hasanal Fachri Satia]]>
<![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.2024.5.1.1644
<![CDATA[The internet has become a fundamental necessity in various activities today. Stream Cyber Cafe, as an internet service provider, faces the challenge of maintaining network quality so that users can comfortably engage in activities such as gaming, streaming services, and social media. Load balancing, utilizing multiple internet sources from various ISPs, becomes a solution to enhance network efficiency and responsiveness. This research focuses on implementing the Dijkstra algorithm on MikroTik devices to determine the shortest path based on DNS server latency from various internet service providers (ISP). The main steps include configuring MikroTik devices, analyzing latency connections to DNS servers, and employing the Dijkstra algorithm. The Dijkstra algorithm, utilizing a Greedy approach, considers the minimum weight from the starting node to other nodes. Testing using the "PING" command provides information on the number of hops or steps required to reach each DNS server. Dijkstra adapts the shortest path based on latency, yielding optimal load balancing efficiency. Configuring MikroTik features, such as Firewall Mangle, Routing Table, and Routing Gateway, supports the functionality of the Dijkstra algorithm. Test results show that each ISP has a different shortest path to DNS servers, and the Dijkstra algorithm can determine the shortest path by considering time or latency factors. Although the author acknowledges some technical challenges during implementation, the proposed solution successfully overcomes these challenges. Thus, the use of the Dijkstra algorithm on MikroTik proves its effectiveness in enhancing the performance and reliability of the network in the Stream Cyber Cafe environment.]]>
<![CDATA[ANALYSIS OF PUBLIC SENTIMENT ON GOOGLE PLAY STORE TIJE APPLICATION USERS USING NAÏVE BAYES CLASSIFIER METHOD ]]>
<![CDATA[Sari, Laila Atikah]]>;
<![CDATA[Ramadhita, Nindia Fitri]]>;
<![CDATA[Hasan, Firman Noor]]>
<![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.2024.5.1.1648
<![CDATA[Advances in information technology have an influence on companies and agencies to innovate. The Tije application is one of the innovations that has been made by PT Tranportasi Jakarta which is used by its users. However, each application has advantages and disadvantages, including the Tije application which has an impact on the disruption of the function of supporting user services as the purpose of making this application. This can certainly trigger a response from users which can be submitted through the review column on the Google Play Store platform. This research was conducted to analyze the sentiment of community reviews of Tije application users on the Google Play Store platform using the Naïve Bayes Classifier method. Tije application review data collection is done by web scrapping techniques on the Google Play Store using Google Colab. Then, the collected data will be processed to eliminate inappropriate elements and get sentiment content on each review, whether the review falls into the category of positive or negative sentiment towards the Tije application. The results of this study conclude that users are dissatisfied and disappointed with the services available on the Tije application. This is evidenced by the number of negative sentiments that are more dominant and in the application of the Naive Bayes algorithm in this study, obtained quite good accuracy results of 85.88%.]]>
<![CDATA[TEXT CLASSIFICATION USING INDOBERT FINE-TUNING MODELING WITH CONVOLUTIONAL NEURAL NETWORK AND BI-LSTM ]]>
<![CDATA[Zevana, Alda]]>;
<![CDATA[Riana, Dwiza]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.6.1650
<![CDATA[The technological advancements in goods delivery facilities have been increasing year by year in tandem with the growing online trade, which necessitates delivery services to fulfill the transactional process between sellers and buyers. Since 2000, top brand awards have often conducted official survey analyses to provide comparisons of goods or services, one of which includes delivery services. However, the survey rankings based on public opinion are less accurate due to users of delivery services and service companies being unaware of the specific success factors and weaknesses in their services. The aim of this research is to analyze the comparison of text mining using the Indonesian language transformation method, IndoBert. The algorithm utilized to demonstrate analysis performance employs Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). This method is utilized to determine the impact of opinion data from Twitter on the J&T Express expedition delivery service, incorporating both text preprocessing and data without text preprocessing. The IndoBert parameters vary in the learning rate section based on four factors: price, time, returns, and others. The research data consisted of 2525 comments from Twitter users regarding the delivery service spanning from January 1, 2021, to March 31, 2023. The testing showed that Bi-LSTM with text preprocessing performed 2% higher, achieving 79% at a learning rate of 1x10-6, compared to without text preprocessing at the same learning rate, which reached 77%. Additionally, CNN outperformed by 3% with a rate of 83%, compared to 80% without text preprocessing at a learning rate of 1x10-5. The highest accuracy, reaching 83%, was obtained by CNN with parameters set at 1x10-5, and the preprocessing technique was considered superior to Bi-LSTM.]]>
<![CDATA[IMPLEMENTATION OF NATURAL LANGUAGE PROCESSING (NLP) IN CONSUMER SENTIMENT ANALYSIS OF PRODUCT COMMENTS ON THE MARKETPLACE ]]>
<![CDATA[Alinda Rahmi, Nadya]]>;
<![CDATA[Wulan Dari, Rahmatia]]>
<![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.2024.5.3.1666
<![CDATA[Market product reviews are invaluable information if processed carefully. The process of analyzing product reviews is more than just considering star ratings; Comprehensive examination of the overall content of review comments is essential to extracting the nuances of meaning conveyed by the reviewer. The problem currently occurring in analyzing reviews of product purchases in the marketplace is the large number of abbreviations and non-standard language used by commenters, making it difficult for the system to understand. Therefore, a Natural Language Processing (NLP) approach is needed to improve the language in the content of review comments so as to achieve maximum performance in sentiment analysis. This research utilizes the KNN and TF-IDF algorithms, coupled with NLP techniques, to categorize Muslim fashion product reviews into two different groups that is positive and negative. The NLP-enhanced classification achieved 76.92% accuracy, 80.00% precision, and 74.07% recall, surpassing the results obtained without NLP, which had 69.23% accuracy, 80.00% precision, and 64.52 recall. %. Frequently appearing words in reviews serve as a description of collective buyer sentiment regarding the product. Positive reviews indicate customer satisfaction with the quality, speed of delivery, and price of the goods, while negative reviews indicate dissatisfaction with factors such as color differences and differences in the number of items received.]]>
<![CDATA[THALASSEMIA MINOR SCREENING APPLICATION USING THE C4.5 METHOD BASED ON LARAVEL ]]>
<![CDATA[Sohputro, Nicolas]]>;
<![CDATA[Wijayanto, Bangun]]>;
<![CDATA[Kurniawan, Yogiek Indra]]>
<![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.2024.5.2.1672
<![CDATA[Thalassemia is an inherited blood disorder that causes anaemia and weak red blood cells. Thalassemia minor is a type of thalassemia where the patient is a carrier of thalassemia and only experiences mild anaemia. To prevent an increase in the number of thalassemia cases, a screening process is held for an individual to confirm whether there is a thalassemia carrier in the body. In providing screening in Banyumas Regency, the Unsoed Medical Faculty Thalassemia Research Team encountered several problems, namely that the screening results could only show whether an individual was a carrier of thalassemia minor or not. This causes a problem because a good screening result is a probability. The second problem is the absence of an integrated information system for thalassemia control in Banyumas Regency. The solution to these two problems is to build a thalassemia minor screening application. The application uses the C4.5 data mining method to calculate the likelihood of thalassemia minor in individuals. The application is made website-based using Laravel to speed up website development. The system also uses a web service to be able to access the created C4.5 algorithm.]]>
<![CDATA[LOW CODE INTEGRATION TESTING IN OUTSYSTEMS PERSONAL ENVIRONMENT ]]>
<![CDATA[Chrisna, I Dewa Ayu Indira Wulandari]]>;
<![CDATA[Dana Sulistiyo Kusumo]]>;
<![CDATA[Rosa Reska Riskiana]]>
<![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.2024.5.2.1673
<![CDATA[As implied by its name, low code platforms enable software development with minimal or no coding involved. Consequently, ensuring the correctness of the software becomes crucial as developers are unable to directly scrutinize the logic. Furthermore, discussions about the various testing approaches applicable to such applications are relatively scarce. This study aims to conduct integration testing through both white box and black box methods, as well as exploring the types of testing that can be carried out on low code based applications. This research involves several stages, including creating a basic e-shop application and API using OutSystems, test preparation, and test execution. API testing utilizes OutSystems' BDDFramework and Postman automation testing tools, while web page integration is carried out using Katalon Studio. The test results indicate only one of the total 23 test cases was considered failed because the result did not match the expected result. Apart from that, of the four existing levels of testing, component testing can also be carried out on the OutSystems application. However, only with the black box testing method because testing is carried out without accessing the program source code. The comparative execution of API testing (white box) using two distinct testing tools reveals the superior effectiveness of Postman over BDDFramework, offering more comprehensive test outcomes and enhanced test case coverage. In the realm of UI integration testing, Katalon Studio emerges as a fitting tool, benefiting from its record and replay feature that facilitates the definition of test steps.]]>
<![CDATA[IMPLEMENTATION OF DEEP LEARNING ON FLOWER CLASSIFICATION USING CNN METHOD ]]>
<![CDATA[Pratiwi, Anggun]]>;
<![CDATA[Fauzi, Ahmad]]>
<![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.2024.5.2.1674
<![CDATA[Technological developments in the field of artificial intelligence, particularly deep learning, have made significant contributions to various applications, including pattern recognition and object classification in visual images. One of the interesting applications of deep learning is image classification, where these techniques have proven effective in tackling complex problems, such as object recognition in visual images. Flowering plants, with approximately 369,000 known species, are an interesting object of study. In an effort to classify different types of flowers quickly and efficiently, a digital approach is a must. This research aims to implement deep learning technology, especially CNN method, in flower classification. One method that can be used is Convolutional Neural Network (CNN), a deep learning algorithm that is able to process image information well. In flower type classification, supervised learning techniques are essential. By giving the label (flower type) to the algorithm as the basis of truth, the use of CNN on a large scale can produce predictions and classifications with a high level of accuracy. This research produces a classification model that is more precise and able to overcome variations in flower morphology with 2 different datasets namely Oxford17 resulting in 84% accuracy and oxford102 resulting in an accuracy value of 64%.]]>
