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[ANALYSIS OF THE EFFECTIVENESS OF POLYNOMIAL FIT SMOTE MESH ON IMBALANCE DATASET FOR BANK CUSTOMER CHURN PREDICTION WITH XGBOOST AND BAYESIAN OPTIMIZATION ]]>
<![CDATA[Faran, Jhiro]]>;
<![CDATA[Triayudi, Agung]]>
<![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.2024.5.3.1284
<![CDATA[The case of churn in the banking industry, namely customers who leave or no longer use bank services, is a serious problem that requires an appropriate solution. The aim of this research is to predict churn and take appropriate preventive actions using machine learning. The dataset contains 10,000 bank customer data with 14 relevant features. Only about 20% of customers experience churn, creating a data imbalance problem in classification. To overcome data imbalances, the SMOTE oversampling technique was applied. Also introduced was the development of the SMOTE technique, namely, Polynomial Fit SMOTE Mesh (PFSM). PFSM works by combining each point in the data with a linear function and producing synthetic data at each connected distance. Experimental results show that the model developed using PFSM and optimized with Bayesian Optimization for the XGBoost algorithm achieved 86.1% accuracy, 70.87% precision, 53.81% recall, and 61.17% F-score. This indicates that the approach is successful in improving predictive capabilities and identifying potential customers for churn earlier. This research has significant relevance in the banking industry, helping banks to safeguard their customers and improve banking business performance..]]>
<![CDATA[ANALYSIS AND IMPLEMENTATION OF YOLOV7 IN DETECTING PIN DEL IN REAL-TIME ]]>
<![CDATA[Iustisia Natalia Simbolon]]>;
<![CDATA[Lumbanraja, Daniel Fernandez]]>;
<![CDATA[Tampubolon, Kristina]]>
<![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.2024.5.2.1286
<![CDATA[Real-time object detection is the process of identifying and tracking objects instantly and directly without any delay between image input and output. Carrying out real-time detection is a challenge in detection systems because it requires speed and accuracy of detection. This research proposes the application of the YOLOv7 algorithm which allows object localization and classification in one stage. This detection is carried out in real time on two objects, namely PinDel and Students. This research focuses on applying the YOLOv7 algorithm to detect real-time use of Pin Del by students. In this research, several hyperparameters were adjusted until the optimal value was found, including epoch with a value of 300, as well as confidence threshold, and IoU threshold with a value of 0.5. The model evaluation results from hyperparameter experiments show good results, with precision of 0.946, recall of 0.959, and mAP@0.5 of 0.977. This research has succeeded in detecting Pin Del objects in real time by obtaining a detection speed of between 7 and 40 FPS, which shows a fast response in detecting objects in real time. This research has contributed to the development of real-time object detection technology and its application in Pin Del use cases by students.]]>
<![CDATA[TEMPORAL SPATIAL PROPERTY PROFILING AND IDENTIFICATION OF EARTHQUAKE PRONE AREAS USING ST-DBSCAN AND K-MEANS CLUSTERING ]]>
<![CDATA[Samsudin, Angga Radlisa]]>;
<![CDATA[Fudholi, Dhomas Hatta]]>;
<![CDATA[Iswari, Lizda]]>
<![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.2024.5.3.1293
<![CDATA[Indonesia is a country located at the confluence of three major tectonic plates, namely Indo-Australia, Eurasia, and the Pacific so that earthquakes often occur, one of which is in West Nusa Tenggara Province. One way to accelerate the disaster mitigation process is to analyze earthquake occurrence based on spatial temporal aspects. This study uses data from BMKG NTB Province during 2018 with a total of 3,699 earthquake events which are then analyzed using ST-DBSCAN and K-Means. ST-DBSCAN analysis was used to determine earthquake prone areas based on the date and location of the event, while k-means used the depth and magnitude of the earthquake. The results show that the distribution pattern of earthquakes in the NTB region has a stationary pattern and there are similar prone areas based on the location and time of occurrence as well as the strength and depth of the earthquake. The ST-DBSCAN method using latitude and longitude attributes produces one cluster that covers 96.33% of the total data. Meanwhile, K-Means using the depth and magnitude attributes produced four clusters. The four clusters were obtained from the cluster density using the silhouette score value between -1 and 1. The K-means analysis used a silhouette score result of 18.527 which was found in cluster 1. Earthquake prone areas in the distribution of earthquakes or types of earthquakes are located in Gangga and Bayan sub-districts of North Lombok and in Sambelia and Sembalun sub-districts of East Lombok. The sub-district with the most frequent earthquakes is Sambelia sub-district with 112 earthquakes. Then the strength of the largest earthquakes on average occurred in Gangga sub-district with magnitudes of 4 to 6.2 SR with shallow earthquake types. The prone area is located at the foot of the mountain and directly adjacent to the ocean.ith shallow earthquake types. The Prone area is at the foot of a mountain and directly adjacent to the ocean.]]>
<![CDATA[THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE ]]>
<![CDATA[Adyatma, Adrian Dwinanda]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Maryanto, Eddy]]>
<![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.2023.4.6.1310
<![CDATA[In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) from Gojek users on the Google Play Store, and performing performance validation with K-Fold at K=10, the system that uses the combined TF-IDF feature extraction of unigrams and bigrams achieves the best performance among the three systems with an accuracy of 0.84, however the accuracy of the system that uses unigrams solely has accuracy of 0.83, and 0.7 for the system that uses bigram. From the results of the research, it can be concluded that the use of the combination of unigram and bigram can increase the accuracy of the classification result.]]>
<![CDATA[RECOMMENDATION SYSTEM TO SELECT A MAJOR OF VOCATIONAL SCHOOL USING DECISION TREE ]]>
<![CDATA[Ardiansyah Risko Anwari]]>;
<![CDATA[Sukirman, Sukirman]]>
<![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.2024.5.2.1327
<![CDATA[A recommendation system is a tool that can be used to provide suggestions to users about something they are interested in, such as products, content, music, movies, or even majors at school. When registering for majors at vocational high school (SMK), some students sometimes difficult to select major based on their interests and abilities. This study aims to develop a recommendation system to select major in SMK, so that it can help prospective students choose majors according to their abilities. The method used is Research and Development (R&D), using the waterfall development model which consists of several stages, namely requirements analysis, system design, design implementation, and system testing. The algorithm used to recommend choices is a decision tree, a predictive model that maps input data to output targets based on a series of decisions or separation rules. The parameters used to recommend the selection of majors are the value data of last year's applicants. The evaluation was carried out using the system usability scale (SUS) involving 25 participants (17 males and 8 females) aged from 14 to 16 years old. Based on the analysis carried out, the results showed that SUS score is 89.7, which means that included to the excellent category in measuring adjective ranges, and acceptable in the acceptability scale. Thus it can be concluded that this department recommendation system is usable or can be used to provide advice to students in selecting a major in SMK.]]>
<![CDATA[DESIGN AND DEVELOPMENT OF COMPUTER-BASED TEST (CBT) SYSTEM IN THE ACADEMIC SELECTION PROCESS FOR RECRUITING SOLDIERS IN THE AIR FORCE ]]>
<![CDATA[Anggraini Pratiwi, Nabilla]]>;
<![CDATA[Iskandar, Dadang]]>;
<![CDATA[Nofiyati, Nofiyati]]>
<![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.2024.5.1.1334
<![CDATA[The selection of recruits is a crucial stage for the continuity of the existence of the Indonesian Air Force (TNI AU). This selection process consists of several stages, one of which is the academic stage conducted with a paper-based test model. This model requires a lengthy conventional process, indicating the need for a computer-based system to expedite the selection process. Therefore, this study proposes a solution for the academic selection of TNI AU recruits by implementing a Computer Based Test (CBT) system, aiming to enhance the efficiency of the selection process. The development of the CBT system utilizes the Waterfall method, with PHP programming language and MySQL. The research results in a CBT system designed for electronic academic selection of TNI AU recruits, equipped with Safe Exam Browser (SEB) to reduce cheating during the selection exams. According to testing results, the CBT system meets the requirements and is deemed suitable for use.]]>
<![CDATA[DOCKER-BASED MONOLITHIC AND MICROSERVICES ARCHITECTURE PERFORMANCE COMPARISON ]]>
<![CDATA[Panji Dirgantara, Deni]]>;
<![CDATA[Dana Sulistyo Kusumo]]>;
<![CDATA[Rio Guntur Utomo]]>
<![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.2024.5.2.1338
<![CDATA[Most developers still use the monolithic architecture, where all components of an application are combined into one integrated system, so each part depends on other components. The monolithic architecture has weaknesses, such as when a failure occurs in one component, all parts cannot be executed because each component relies on one other component. Microservices can be a solution to this, considering that in the microservices architecture, each element or service is created and put separately, so when a failure occurs in one component, other components will not be affected and can still run normally. This research aims to determine the implementation and performance comparison between monolithic architecture and microservices Architecture in the Agreeculture Market web app. Agreeculture Market is a web application that aims to facilitate the transaction process of agricultural commodities and make it easier for agricultural commodity producers to market their products. The measurement method used to measure the performance of both architectures is load testing using JMeter and performance tools from task manager and comparing the response time, throughput, disk usage, CPU usage, and memory usage of both used architectures. With two measurement schemes with Docker and without Docker, the result of this research is a performance comparison between the two architectures, where the backend application Agreeculture Market, which uses microservices architecture with Docker and API gateway, performs better than the monolithic architecture version. Conversely, the monolithic architecture performs better than the microservices architecture in the scheme without Docker and API gateway.]]>
<![CDATA[TOPIC CLASSIFICATION ON TWITTER USING CNN WITH WORD2VEC FEATURE EXPANSION ]]>
<![CDATA[Bintang Ramadhan, Rifaldy]]>;
<![CDATA[Budi Setiawan, Erwin]]>
<![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.2024.5.1.1342
<![CDATA[Twitter is a social networking site that enables users to communicate with their followers by sending them short messages known as "tweets." Each tweet has a character limit of 280 characters. The minimum limit of tweets resulted in writing short tweets and increased use of word variations. This makes tweets difficult to understand without the help of the topic, thus tweets should be classified. This study aims to classify topics of Twitter using word2vec feature expansion to decrease vocabulary ambiguities in topic classification. This type of research is system design research. Feature expansion is a machine learning technique used to extract new features (or variables) from the dataset's existing features. A model's complexity and expressive power are intended to be increased through feature expansion in order to improve performance and generalization. Data were processed using Convolutional Neural Network (CNN). The results indicate that there is an important contribution in increasing understanding of topic classification in Twitter data with Word2Vec, and the CNN application is able to assist some obstacles in analyzing short text with high word variations.]]>
<![CDATA[K-MEANS CLUSTERING WITH COMPARISON OF ELBOW AND SILHOUETTE METHODS FOR MEDICINES CLUSTERING BASED ON USER REVIEWS ]]>
<![CDATA[Safitri Juanita]]>;
<![CDATA[Cahyono, Raynaldi Dwi]]>
<![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.2024.5.1.1349
<![CDATA[The dissemination of medicine information allows users or customers to make massive assessments of medicines, containing positive and negative reviews—one of a site that provides medicine information online, named drugs.com, contains reviews and ratings of each medicine by variant disease. This site has extensive data collection that has not been processed to produce helpful information for medicine information seekers or the medicine industry. Therefore, research is needed to cluster medicines based on data review from drugs.com. The contribution of this study proposes the best model to cluster user reviews for medicines using K-Means by comparing 2 (two) techniques to determine the optimal number of clustering, Silhouette and Elbow. This study aims to recommend the best K-Means clustering method for processing extensive data reviews and ratings, and cluster results help medical experts, medicine information seekers, or pharmaceutical businesses determine the market share of medicines. The results show that the K-Means model performs best when clustering using the Silhouette method with a DBI value of 0.261 and producing 2 clusters. Meanwhile, the Elbow model has the best performance value of 0.460 and produces 3 clusters. This study also shows that clustering results with both methods produce three medicine cluster groups based on reviews: moderate, unpopular and famous.]]>
<![CDATA[FISH FRESHNESS PREDICTION WITH CONVOLUTIONAL NEURAL NETWORK METHOD BASED ON FISH EYE IMAGE ANALYSIS ]]>
<![CDATA[Mahendra, Robby]]>;
<![CDATA[Faurina, Ruvita]]>
<![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.2024.5.3.1351
<![CDATA[The potential for fish resources in Bengkulu waters is abundant, but quality must be maintained for safety and selling value. Changes in the skin, eyes, gills and flesh of fish indicate a decrease in quality due to enzyme, chemical and bacterial activity. The process of sorting fish by fishermen or sellers is still often done manually, which is sometimes inaccurate due to limited vision. With advances in computing technology, classification algorithms are needed that can identify and differentiate between fresh fish and non-fresh fish. This research uses a Convolutional Neural Network with DenseNet201, VGG16, and InceptionV3 architecture. The dataset contains 880 Belato Alepes Djedaba fish eye images, with a ratio of 80:15:5 for train, validation, and test. DenseNet201 has the best performance compared to VGG16 and InceptionV3. Accuracy on DenseNet201 test data 98%, InceptionV3 95%, and VGG16 91%. The classification results of the best model using 8 images with various scenarios show that all images were successfully classified 100% correctly. This research makes a contribution to the field of fishery product processing technology which allows fish quality classification to be carried out quickly and accurately, as well as increasing efficiency in ensuring the quality of fish for consumption.]]>
