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[VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE ]]>
<![CDATA[Althaf Pramasetya Perkasa, Mochamad]]>;
<![CDATA[El Akbar, R. Reza]]>;
<![CDATA[Al Husaini, Muhammad]]>;
<![CDATA[Rizal, Randi]]>
<![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.2024.5.3.2015
<![CDATA[In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLOv7 in overall object detection performance. Therefore, based on these results, YOLOv8 can be considered as the model that is close to the best performance in detecting objects in the dataset used. This study not only provides insights into the performance and characteristics of the YOLOv7, YOLOv8, and YOLOv9 models in the context of object detection in soccer matches but also results in a dataset ready for additional analysis or for training deep learning models.]]>
<![CDATA[DESIGN OF PATIENT MEDICAL RECORD FILE TRACER INFORMATION SYSTEM WITH WATERFALL METHOD ]]>
<![CDATA[Agnia, Fadilatul]]>;
<![CDATA[Syahidin, Yuda]]>;
<![CDATA[Elvira, Shinta]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2020
<![CDATA[Tracer is essential since it serves as a barrier and substitute for medical record files when they come from the storage rack. Tracer Helps locate medical record files if they are not on the storage shelf and shows where the storage shelf is when the file is saved again. The problem that arises from Hospital X in Bandung area is that it has not implemented the use of Tracer during the procedure of retrieving medical record files and the procedure of searching for files when files are needed only relying on expedition books. The purpose of this study is to prevent and reduce misfiles and dropouts of file for medical record and simplify the procedure of tracking file for medical record. Descriptive qualitative research methods are applied to the design of its system. Data is gathered through interviews and observation. Waterfall method for system development. Microsoft Visual Studio 2010 and Microsoft Access as the coding process and database and produces medical record file tracer card output. The software functions flawlessly, and every menu can be accessed.]]>
<![CDATA[EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME ]]>
<![CDATA[Wido Paramadini, Adanti]]>;
<![CDATA[Dasril Aldo]]>;
<![CDATA[Yoka Fathoni, M.]]>;
<![CDATA[Yohani Setiya Rafika Nur]]>;
<![CDATA[Dading Qolbu Adi]]>
<![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.2024.5.3.2021
<![CDATA[Systemic Inflammatory Response Syndrome (SIRS) is a generalized inflammatory condition, triggered by various factors such as infection or trauma, which can lead to serious complications if not treated quickly. This condition is characterized by symptoms such as fever or hypothermia, tachycardia, tachypnea, and changes in white blood cell count. Complications that can arise from SIRS include Acute Respiratory Distress Syndrome (ARDS), which results in fluid in the alveoli and requires mechanical ventilation; acute encephalopathy, which leads to brain dysfunction; Asidosis Metabolik, indicating liver damage; hemolysis, which results in the breakdown of red blood cells; and Deep Vein Thrombosis (DVT), which is at risk of causing pulmonary embolism. To overcome this diagnostic challenge, this study implements the Dempster-Shafer method in an expert system, where it allows the aggregation and combination of various sources of evidence to produce degrees of belief and degrees of plausibility for each diagnostic hypothesis. By accounting for uncertainties and contradictions in the data, the system improves diagnostic accuracy through dynamically weighting and updating beliefs based on available evidence. This process allows early and accurate identification of SIRS complications, supporting appropriate medical intervention. System evaluation showed diagnostic accuracy of 93%, confirming the potential of expert systems in supporting rapid and precise clinical decision-making in managing SIRS complications.]]>
<![CDATA[STACKING ENSEMBLE LEARNING AND INSTANCE HARDNESS THRESHOLD FOR BANK TERM DEPOSIT ACCEPTANCE CLASSIFICATION ON IMBALANCED DATASET ]]>
<![CDATA[Bangun Watono]]>;
<![CDATA[Ema Utami]]>;
<![CDATA[Dhani Ariatmanto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2022
<![CDATA[Bank term deposits are a popular banking product with relatively high interest rates. Predicting potential customers is crucial for banks to maximize revenue from this product. Therefore, bank term deposits acceptance classification is an important challenge in the banking industry to optimize marketing strategies. Previous studies have been conducted using machine learning classification techniques with the imbalanced Bank Marketing Dataset from the UCI Repository. However, the accuracy results obtained still need to be improved. Using the same dataset, this study proposes an Instance Hardness Threshold (IHT) undersampling technique to handle imbalanced datasets and Stacking Ensemble Learning (SEL) for classification. In this SEL, Decision Tree, Random Forest, and XGBoost are selected as base classifiers and Logistic Regression as meta classifier. The model trained on SEL with the dataset undersampled using IHT shows a high accuracy rate of 98.80% and an AUC-ROC of 0.9821. This performance is significantly better than the model trained with the dataset without undersampling, which achieved an accuracy of 90.30% and an AUC-ROC of 0.6898. The findings of this research demonstrate that implementing of the suggested IHT undersampling technique combined with SEL has been evaluated to effectively enhance the performance of term deposit classification on the dataset.]]>
<![CDATA[ANALYSIS AND IMPLEMENTATION OF SENTIMENT SYSTEM ON THE ELECTABILITY OF INDONESIAN PRESIDENTIAL CANDIDATES 2024 USING SUPPORT VECTOR MACHINE METHOD ]]>
<![CDATA[Harahap, Jasmine Avrile Kaniasari]]>;
<![CDATA[Syaifullah JS, Wahyu]]>;
<![CDATA[Idhom, Mohammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2029
<![CDATA[Indonesia is a country that implements democracy in choosing presidential candidates through the election process. People have their own views on the presidential candidates they support, and in this digital era, social media is the main platform for people to express their opinions. Public opinion can be positive or negative, public opinion, hate speech, and various other comments that can cause hostility, insults, debates, and disputes. In this study, data modeling using the Support Vector Machine (SVM) method will be evaluated using a confusion matrix. The data used for anies data is 1607 tweets, prabowo data is 1761 tweets, and ganjar data is 1607 tweets with the keywords “anies baswedan”, “prabowo subianto”, and “ganjar pranowo” with the data collection period from November - December 2023. The results of this study show that the sentiment classification model has good performance. For Anies Baswedan data, the SVM model achieved accuracy of 86.64%, precision of 86.69%, recall of 86.64%, and f1-score of 86.62%. For Prabowo Subianto data, the model achieved an accuracy of 90.65%, precision of 90.81%, recall of 90.65%, and f1-score of 90.61%. Meanwhile, for Ganjar Pranowo data, the model achieved an accuracy of 93.78%, precision of 93.67%, recall of 93.78%, and f1-score of 93.72%. These results show that the system is able to classify people's sentiment.]]>
<![CDATA[IMPLEMENTATION OF HYPERPARAMETER TUNING IN RANDOM FOREST ALGORITHM FOR LOAN APPROVAL PREDICTION ]]>
<![CDATA[Sandhi Bhakti, Dwi]]>;
<![CDATA[Prasetyo, Agung]]>;
<![CDATA[Arsi, Primandani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2032
<![CDATA[The risk of non-performing loan is a significant issue in the financial industry, including banks and cooperatives. Loan default risks can occur due to various reasons, and one of them is the negligence of staff or subjective decision-making in loan approval. The proposed solution is to enhance an objective and accurate loan approval decision-making system through the application of machine learning technology, aiming to reduce the risk of loan default. The Random Forest algorithm has proven to be the best in predicting loan approval compared to other supervised learning models. Optimization was performed on the Random Forest algorithm through hyperparameter tuning and data balancing using SMOTE. The best accuracy obtained from several experiments was 86.2%. By implementing optimizations on the Random Forest algorithm, it is expected that the model can make loan approval predictions more objectively and accurately, serving as a reference for future loan approval system development.]]>
<![CDATA[ANALYSIS AND IMPLEMENTATION OF AES-128 ALGORITHM IN SUKAHARJA KARAWANG VILLAGE SERVICE SYSTEM ]]>
<![CDATA[Fariz Duta Nugraha]]>;
<![CDATA[Kiki Ahmad Baihaqi]]>;
<![CDATA[Hilda Yulia Novita]]>;
<![CDATA[Siregar, Amril Mutoi]]>
<![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.2024.5.3.2038
<![CDATA[Data security in databases is needed in the industrial era 4.0 to prevent attacks and unwanted things from happening, one of the biggest cases that has been widely reported is data leakage, in this study aims to implement and analyze the Advanced Encryption Standard Algorithm, one of the data security algorithms with a block chiper type that has 4 transformations (SubByte, ShiftColumn, MixColumn, AddRoundKey), or what we usually call the Cryptography method. Cryptography is a method that is often used to secure important data in databases, in this article the Advanced Encryption Standard Algorithm is used to secure citizen data and family card data in the Sukaharja Karawang Village service system. The method in this research is the observation method, the data is obtained from each head of the neighborhood in Sukaharja Karawang Village with the permission of the head of Sukaharja Karawang Village. Citizen data and family cards were encrypted and analyzed for resource requirements in storing encryption results and time in returning and displaying original data. The results of the analysis obtained the amount of resources required 1.5MB to store family card data, which before encryption required 352KB. Citizen data requires a resource of 6.5MB, before encryption it takes 1.5MB. As for the AES resilience test stage using the Bruteforce attack method with the help of Hashcat software version 6.2.5 with 4 trial processes, One encrypted address data was taken for this test, but out of 4 attempts none of them showed that the data could be cracked.]]>
<![CDATA[IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS ]]>
<![CDATA[Jayidan, Zirji]]>;
<![CDATA[Siregar, Amril Mutoi]]>;
<![CDATA[Faisal, Sutan]]>;
<![CDATA[Hikmayanti, Hanny]]>
<![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.2024.5.3.2047
<![CDATA[This study aims to improve the accuracy of heart disease prediction using Principal Component Analysis (PCA) for feature extraction and various machine learning algorithms. The dataset consists of 334 rows with 49 attributes, 5 classes and 31 target diagnoses. The five algorithms used were K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Results show that algorithms using PCA achieve high accuracy, especially RF, LR, and DT with accuracy up to 1.00. This research highlights the potential of PCA-based machine learning models in early diagnosis of heart disease.]]>
<![CDATA[IMPLEMENTATION OF THE FMADM ALGORITHM AND SAW METHOD IN BOARDING HOUSE SEARCH ]]>
<![CDATA[Baun, Sindy Cristine]]>;
<![CDATA[Purnomo, Hindriyanto Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2056
<![CDATA[Current developments have made many developments, one of which is boarding houses. There are many immigrants from outside the region who want to study at Nusa Cendana University, Kupang City but have difficulty in finding a boarding house because of many considerations such as what facilities are provided by the boarding house owner. The lack of information on boarding house occupancy makes it difficult for prospective residents who are looking for boarding houses to obtain information about boarding houses with the criteria of each boarding house, to overcome this problem the Fuzzy Multi Attribute Decision Making (FMADM) Algorithm and Simple Additive Weighting (SAW) Method are needed with the aim of making it easier for female students to find boarding houses that suit their wishes and the best around Nusa Cendana University, Kupang, NTT. After analysis, the FMADM algorithm turned out to be able to help determine the weight of the value of each criterion in finding the best boarding house and also the SAW method can be implemented very well so that it can make it easier to add up the weight value of each criterion by doing alternative ranking. The results of the research that have been studied show that using the FMADM algorithm and the SAW method can produce the best alternative as the best solution from other alternatives, with Kost Putri Bilm@t being the best alternative out of 100 other alternatives studied with a ranking value of 4.106667. With the best alternative obtained, it shows that by using the FMADM algorithm and the SAW method, the number of samples used is large, the level of validity also often increases.]]>
<![CDATA[COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE-DRIVEN OBJECTIVE WEIGHTING AND MULTI-ATTRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS IN DETERMINING THE BEST PRODUCTION EMPLOYEES ]]>
<![CDATA[Hadad, Sitna Hajar]]>;
<![CDATA[Subhan, Subhan]]>;
<![CDATA[Setiawansyah, Setiawansyah]]>;
<![CDATA[Arshad, Muhammad Waqas]]>;
<![CDATA[Yudhistira, Aditia]]>;
<![CDATA[Rahmanto, Yuri]]>
<![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.2024.5.3.2057
<![CDATA[The problem that occurs in the selection of the best production employees is the lack of transparency and objectivity in the selection process. Without clear procedures and well-defined criteria, employee selection decisions can be influenced by subjective preferences or irrelevant non-performance factors. This can result in injustice in employee selection and lower the morale and motivation of unselected employees. The purpose of the combination of LOPCOW and MAIRCA in determining the best production employees is to provide a holistic and adaptive framework in the employee performance evaluation process. LOPCOW allows decision makers to dynamically adjust the weight of criteria according to the level of volatility or change in the relevant environment or situation. LOPCOW offers an adaptive and responsive approach in determining the weight of criteria, enabling decision makers to respond quickly to changes occurring in the relevant environment or situation. MAIRCA is an analytical method used to assist decision makers in evaluating and selecting alternatives based on several relevant criteria or attributes. MAIRCA provides a strong framework for decision makers to make more informed and informed decisions. Combining these two methods results in a more comprehensive and accurate understanding of production employee performance, thus enabling managers to identify the most effective employees and provide rewards or development accordingly. The final results of the ranking of the best production employees obtained by JR employees get 1st place, YP employees get 2nd place, and AJL employees get 3rd place.]]>
