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[COMPARISON OF SAW AND TOPSIS METHODS TO DETERMINE THE BEST SERVICE DESK AGENT ]]>
<![CDATA[Suryani]]>;
<![CDATA[Prasetyo, Angger Totik]]>;
<![CDATA[Triyono, Gandung]]>
<![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.2024.5.1.1675
<![CDATA[Pusintek's Service Desk, as a single point of contact, has quite high work demands with many tasks and requests handled. In order to improve the performance of Service Desk agents, the organization can give awards to the best Service Desk agents. However, there are obstacles in selecting the best Service Desk agent because there is still a subjective element in the assessment of Service Desk agents. So that a decision support system is needed that is in accordance with the weight of the organization's assessment criteria. This research proposes an approach in selecting the best Service Desk agent using the Simple Additive Weighting (SAW) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in processing and ranking agent value data. This research focuses on assessing agents based on key parameters, namely ticket processing time (service response time), agent attendance data, assignment weight and assessment from other coworkers. The number of agents assessed was seventeen. The results of this study obtained the highest value using the SAW method of 2.22 for A1, while the calculation using the TOPSIS method, the highest value on A1 is 0.74 and the accuracy rate using the SAW method is 82.35% while the TOPSIS accuracy is 41.18%..]]>
<![CDATA[SENTIMENT ANALYSIS OF CUSTOMER SATISFACTION IN GOJEK AND GRAB APPLICATION REVIEWS USING THE NAIVE BAYES ALGORITHM ]]>
<![CDATA[Ananda, Ridha Faiz]]>;
<![CDATA[Syahri, Alfi]]>;
<![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.2024.5.1.1680
<![CDATA[Online motorcycle taxis are a widely favored mode of public transportation in Indonesia. There are several companies providing online motorcycle taxi services in Indonesia, with Gojek and Grab dominating the market. In this rapidly digitizing era, social media has become a platform for Indonesian citizens to express their evaluations and opinions. One common platform used by users to express their evaluations is the Google Play Store, where users can provide ratings and opinions on the applications they use, including users of Gojek and Grab applications.This research aims to understand and analyze the sentiments of the public towards the two dominant giants in the online motorcycle taxi market in Indonesia based on review data from the Google Play Store using the Naive Bayes algorithm. The data used consists of user reviews from May 14, 2023, to July 26, 2023, totaling 300 data points for each application. This data will undergo pre-processing to remove irrelevant elements. The Naive Bayes algorithm is used to classify the existing sentiments into two classes: positive and negative.The results of this research conclude that Gojek users give positive reviews at 49% and negative reviews at 51%, which include praises for the drivers and services provided by the company, complaints about the heaviness of the application, and some disruptions in the Gopay payment method. Meanwhile, Grab users give positive reviews at 67% and negative reviews at 33%, which include customer satisfaction with attractive promos, complaints about the heaviness of the application after the latest update, and the high cost of Grabexpress and Grabfood services.]]>
<![CDATA[PREDICTION OF 2024 PRESIDENTIAL ELECTION USING K-NN WITH METRIC APPROACHES CHEBYSHEV AND EUCLIDEAN BASED ON TWITTER DATA INVESTIGATION ]]>
<![CDATA[Darmawan, Steven Ryan]]>;
<![CDATA[Fatchan, Muhamad]]>;
<![CDATA[Maulana, Donny]]>
<![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.2024.5.2.1720
<![CDATA[The potential difference between the popularity of presidential candidates on social media and in the general public poses a serious challenge in predicting the outcome of the 2024 presidential election. Technical constraints in collecting, cleaning and analyzing dynamic and large-scale social media data can threaten the accuracy and validity of predictions. To overcome this problem, careful steps and in-depth understanding are needed. Therefore, this study aims to predict the winner of the 2024 presidential election from the popularity of presidential candidates Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto on Twitter. The K-Nearest Neighbor (K-NN) method with the Both Metric approach (Euclidean and Chebyshev) was used to analyze 51,192 tweet data through the Knowledge Discovery in Database (KDD) stage using Orange software. The evaluation results show almost the same performance, with AUC values of 0.725 for Euclidean and 0.720 for Chebyshev. The CA result was 55.6% for Euclidean and 55.4% for Chebyshev. Although F1, precision, and recall were almost the same, overall, the Euclidean metric was better. The prediction shows Prabowo Subianto as the most popular candidate on Twitter. Nonetheless, these results need to be interpreted with caution and strengthened with further analysis and additional data to get a more comprehensive conclusion. This research shows that K-NN with both metrics can provide predictions above 50%, reliable enough to be able to predict the most popular candidates on Twitter.]]>
<![CDATA[BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING CONVOLUTIONAL NEURAL NETWORK VV-NET METHOD ]]>
<![CDATA[Sinta Bella Agustina]]>;
<![CDATA[Erwin, Erwin]]>;
<![CDATA[Desiani, Anita]]>;
<![CDATA[Saputra, Tommy]]>
<![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.2024.5.1.1723
<![CDATA[The retina is susceptible to various diseases that can be fatal if not treated quickly. Image processing is currently very helpful for doctors to detect retinal diseases faster so that retinal diseases can be treated immediately. The first step in image processing is to improve the quality of retinal images affected by noise, aiming to increase accuracy in the process of segmentation and image extraction. accurate segmentation of retinal blood vessels is the first step in disease detection. The process of segmentation and analysis of retinal blood vessels has an important role in assisting medical professionals in identifying the severity of a disease. Image quality improvement steps in preprocessing use grayscale, median filter (denoising), and clahe. The method used for blood vessel segmentation is CNN VV-Net. Evaluation of the results of applying image quality enhancement and segmentation techniques using the VV-Net method was performed on the DRIVE, STARE, and CHASEDB_1 datasets at both stages, training and testing. The measurement results of blood vessel segmentation using the CNN VV-net method on the DRIVE dataset (accuracy 96.27%, sensitivity 84.38%, precission 75.95%, and jaccard score 66.28%), STARE dataset (accuracy 96.58%, sensitivity 82.78%, precission 76.73%, and jaccard score 65.38%), and CHASEDB_1 dataset (accuracy 97.04%, sensitivity 83.55%, precission 76.72%, and jaccard score 66.40%). From the three datasets used, the CHASEDB_1 dataset obtained better results than the DRIVE and STARE datasets.]]>
<![CDATA[REDUCING UNDER-FETCHING AND OVER-FETCHING IN REST API WITH GRAPHQL FOR WEB-BASED SOFTWARE DEVELOPMENT ]]>
<![CDATA[Muzaki, Rizki Nuzul]]>;
<![CDATA[Salam, Abu]]>
<![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.2024.5.2.1725
<![CDATA[Rest API is the most popular architectural style in website-based software development. However, Rest API has under-fetching and over-fetching problems. Under-fetching is a situation when the client has to make requests to several endpoints, while over-fetching is a situation when the client receives more data than needed. There is an alternative technology to Rest API, namely GraphQL. GraphQL has the potential to solve both under-fetching and over-fetching problems. This research aims to analyze how quickly GraphQL responds in overcoming under-fetching and over-fetching problems and conducting condition analysis to determine when it is best to use GraphQL. In this research, tests were conducted to answer these problems by applying each of the five test scenarios for under-fetching and over-fetching problems. Test results show that GraphQL can provide response speeds of 36.84% to 93.04% superior to Rest API. In the case of under-fetching, it is best to choose GraphQL when there is a need to call more than four endpoints. Meanwhile, for over-fetching problems, using the Rest API provides adequate response speed. However, if a more optimal response speed is needed, using GraphQL could be an alternative.]]>
<![CDATA[COMPARISON OF MNOTE APPLICATION DEVELOPMENT EFFICIENCY USING LOW CODE AND FULL CODE DEVELOPMENT APPROACHES ]]>
<![CDATA[Gunadi, Gagah Aji]]>;
<![CDATA[Kusumo, Dana Sulistyo]]>
<![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.2024.5.2.1728
<![CDATA[Low Code Development has become more popular in recent years as it offers the ability to develop applications faster. Before the concept of Low Code programming, problems related to the efficiency of application development time were often faced when using manual or Full Code programming approaches. The problem becomes crucial when working on a large-scale application development scope. In this research, the author tries to measure and compare the difference in development efficiency between Low Code and Full Code approaches in the development of a web-based application called MNote, an order recording application for D'Happy food and beverage restaurant in Pemalang, Central Java. The author used OutSystems as the Low Code Platform (LCP) and MongoDB, ExpressJS, ReactJS, NodeJS (MERN) in the Full Code approach. The results showed that the Low Code Development approach takes 51.12% faster than the Full Code Development approach in developing the MNote application. Based on the results of the research, it can be concluded that the use of Low Code Development has a considerable influence in terms of time efficiency and ease of database integration.]]>
<![CDATA[FILM RECOMMENDATION USING CONTENT-BASED USING ARTIFICIAL NEURAL NETWORK METHOD AND ADAM OPTIMIZATION ]]>
<![CDATA[Riaji, Dwi Hariyansyah]]>;
<![CDATA[Setiawan, Erwin Budi]]>
<![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.2024.5.1.1729
<![CDATA[This research aims to develop a more accurate and relevant content-based film recommendation system from the Netflix and Disney+ streaming platforms using the ANN method. Movie recommendation systems are a popular solution to help users find movies that match their preferences. The ANN method develops a model to learn complex patterns from film features. Additionally, Adam optimization is used to improve the speed and accuracy of the model training process. The advantage of using an ANN is its ability to learn complex patterns and improve the performance of the recommendation system over time. Adam Optimization helps improve the speed, accuracy and quality of ANN models. From this research, researchers, based on the evaluation results using the confusion matrix, obtained an accuracy value of 88.30%, using a split ratio of 80:20 and a learning rate of 0.04469992592930794. This means that most classifications can detect correctly according to sufficient data. Combining these two methods allows the film recommendation system to provide better recommendations as more data becomes available.]]>
<![CDATA[PENETRATION TESTING OF A COMPUTERIZED PSYCHOLOGICAL ASSESSMENT WEBSITE USING SEVEN ATTACK VECTORS FOR CORPORATION WEBSITE SECURITY ]]>
<![CDATA[J, Rizky Rachman]]>;
<![CDATA[Patty, Jonathan Suara]]>
<![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.2024.5.3.1731
<![CDATA[Websites, being dynamic platforms, undergo regular updates and continuous usage. Consequently, methods employed in website attacks evolve in tandem with increased security measures implemented in website systems, aiming to exploit both the website itself and its users. Website systems and features must remain prepared for potential future attacks at all times. To ensure this, penetration testing needed to be done consistently to keep up with security standards. This research aims to prove the various vulnerabilities that can be found from penetration testing in order to create recommendations on what to improve within a website. This research involves black box penetration testing of a computerized psychological testing website, developed by PT Dwi Purwa Teknologi hereinafter referred to as the client. The penetration testing simulated attacks by a foreign entity unfamiliar with the website's structure. The assessment focused on seven attack vectors: SQL injection, RCE, URL manipulation, CSRF, SSRF, XSS, and Broken Authentication and Session. Vulnerabilities resulted from poorly sanitized input forms, leading to SQL injection and RCE risks. Inadequate input validation enabled cross-site scripting attacks, while missing CSRF tokens exposed the website to CSRF threats. The research underscores the importance of penetration testing to identify and address security weaknesses, empowering the client to fortify their website against potential cyber threats.]]>
<![CDATA[PERANALYSIS OF ACADEMIC WEBSITE USING WEBQUAL 4.0 METHOD AND IMPORTANCE-PERFORMANCE ANALYSIS (IPA) ]]>
<![CDATA[Andrean, Fizal Okta]]>;
<![CDATA[Megawati]]>;
<![CDATA[Fronita, Mona]]>;
<![CDATA[Saputra, Eki]]>
<![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.2024.5.2.1732
<![CDATA[The Academic Website plays a crucial role as the primary channel for delivering academic information to the entire academic community. Its main functions include providing vital information such as Graduation Schedules, Academic Year Calendars, and Scholarship Announcements, making it an indispensable source of information for students. The quality of services on this website is a crucial aspect in meeting the information needs of students. This research aims to evaluate and enhance the quality of the website, with a primary focus on improving services for students and achieving a higher ranking in Webometrics State Islamic Religious Higher Education Institution (PTKIN), currently positioned at 18th. The research methodology utilizes WebQual 4.0 to assess the website's quality, focusing on usability, information quality, and service interaction quality. The Importance-Performance Analysis (IPA) approach is employed to guide the website's development based on the importance and actual performance of each quality attribute. The Webqual Index analysis results indicate that the website achieves a score of 0.85 or 85%, highlighting good service quality but also indicating the need for improvement in information and service interaction quality. This study produces a comprehensive guide for the necessary changes and developments in the Academic Website. The guide ensures that the website aligns with the dynamic needs of the university community, creating a virtual environment that supports and facilitates access to information for students. These improvements are expected not only to enhance the Webometrics PTKIN ranking but also to increase student satisfaction and engagement in the academic process]]>
<![CDATA[NAIVE BAYES AND PARTICLE SWARM OPTIMIZATION IN EARLY DETECTION OF CHRONIC KIDNEY DISEASE ]]>
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Suhardjono, Suhardjono]]>;
<![CDATA[Wuryanto, Anus]]>;
<![CDATA[Yuliandari, Dewi]]>;
<![CDATA[Sugiarto, Hari]]>
<![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.2024.5.3.1750
<![CDATA[Chronic Kidney Disease (CKD) is a global health problem that requires early detection to reduce the risk of complications and disease progression. The Naïve Bayes (NB) algorithm has been proven effective in detecting CKD but its accuracy still varies. The problem with previous research is that it has not fully optimized existing algorithms in terms of accuracy and efficiency. This research aims to develop a more accurate and efficient early detection method for CKD using the NB algorithm and Particle Swarm Optimization (PSO). The NB method is known for its speed and ease of implementation, with global search capabilities and PSO for parameter optimization. Dataset from the UCI repository, which includes data pre-processing, NB implementation, performance evaluation, and enhancement with PSO. The results of NB+PSO show a significant increase in accuracy of 95.75% from 95.00% and Area Under Curve (AUC) value of 0.910% from 0.802% compared to the use of NB alone. The conclusion of this study is that the combination of NB+PSO increases effectiveness in early detection of CKD. This research opens up opportunities for further development in the medical field, especially in improving the diagnostic accuracy of other diseases.]]>
