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 RANDOM FOREST, K-NEAREST NEIGHBOR, DECISION TREE, AND XGBOOST ALGORITHMS FOR DETECTING STUNTING IN TODDLERS ]]>
<![CDATA[Bimawan, Zaynuri Ilham]]>;
<![CDATA[Astuti, Tri]]>;
<![CDATA[Arsi, Primandani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2629
<![CDATA[Stunting is a significant health issue in many developing countries, including Indonesia. Advances in health technology have opened new opportunities to improve the accuracy and efficiency of detecting stunting in young children, with one such advancement being Machine Learning technology. This study compares various Machine Learning algorithms for detecting stunting in children. The methodology includes data collection, data exploration, data preprocessing, feature extraction, model classification, and model evaluation. The results show that Random Forest demonstrates superior performance with the highest accuracy of 0.999132, recall of 0.999132, and a macro-averaged F1-score of 0.998906, making it the most consistent model for predicting child nutritional status. K-Nearest Neighbor also shows very good performance with an accuracy of 0.999050 and an F1-score of 0.998748. Decision Tree has an accuracy of 0.999091 and an F1-score of 0.998705, closely matching the performance of Random Forest and KNN. XGBoost, with an accuracy of 0.991033 and an F1-score of 0.987495, performs lower than the other three models. Therefore, Random Forest is the recommended choice for implementing stunting prediction in children.]]>
<![CDATA[PUBLIC SENTIMENT ANALYSIS OF 'DIRTY VOTE' DOCUMENTARY FILM ON TWITTER USING NAÏVE BAYES WITH GRID SEARCH OPTIMIZATION ]]>
<![CDATA[Bagaskara, Febrian Chrissma]]>;
<![CDATA[Syahrullah, Syahrullah]]>;
<![CDATA[Hendra, Andi]]>;
<![CDATA[Lamasitudju, Chairunnisa]]>;
<![CDATA[Rinianty, Rinianty]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2682
<![CDATA[The film "Dirty Vote" provides a realistic depiction of alleged fraud issues within Indonesia's democratic system, released ahead of the 2024 elections. This has sparked various public opinions, both in favor of and against the film, potentially affecting the stability of Indonesia’s democratic system. The aim of this research is to analyze the public's reaction to the "Dirty Vote" documentary, which could serve as a consideration for assessing public awareness in rationally responding to a film and improving the quality of democracy in Indonesia. This research will test the accuracy of data used in classification using the Naive Bayes Classifier based on collected Twitter data. The evaluation results of the Naive Bayes model for sentiment classification showed an accuracy of 86%, with a precision of 84% and a recall of 91%. When compared to the implementation of hyperparameter tuning using grid search with a stratified k-fold combination and parameter configurations for alpha: [0,1], binarize: [0.0], and fit prior: [true, false], better results were obtained with an accuracy of 90%, a precision of 87%, and a recall of 94%. This demonstrates that using parameter optimization methods from grid search can help improve the accuracy of a classification model. It is hoped that this research will contribute significantly to the development of Indonesia’s democratic system, particularly in raising public awareness to think more rationally and critically when evaluating and analyzing a film.]]>
<![CDATA[PARTICLE SWARM OPTIMIZATION AND GRIDSEARCH OPTIMIZATION ON SUPPORT VECTOR MACHINE ALGORITHM ON SENTIMENT ANALYSIS OF DONALD TRUMP'S ASSASSINATION ATTEMPT ]]>
<![CDATA[Putra, Rinaldi Febryatna Duriat]]>;
<![CDATA[Sudewo, Andika Hasbigumdi]]>;
<![CDATA[Wibowo, Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2713
<![CDATA[Donald Trump is the 45th president of the United States, serving from 2017 to 2021. Within the 2024 race, Donald Trump is once more running for president of the United States from the Republican Party. Whereas campaigning in Butler, Pennsylvania, United States, a shooting occurrence happened that was distinguished as an endeavored death of Donald Trump. The occurrence gave rise to different master and con opinions among the open. This consider points to decide the propensity of open conclusion towards the endeavored death of Donald Trump and to classify estimations with respect to the occurrence. This think about compares the Molecule Swarm Optimization (PSO) and GridSearch optimization approaches on the Back Vector Machine (SVM) calculation to get the greatest level of precision from optimizing the calculation. In this think about, the dataset utilized was tweet information from July 15, 2024, totaling 1,586, which had been labeled with positive, neutral and negative estimations. The comes about of the tests carried out with comparison proportions of 90:10, 80:20, 70:30, and 60:40 appear that the optimization strategy through PSO can increment the exactness of the SVM calculation by 2.39% when compared to the GridSearch strategy.]]>
<![CDATA[COMPARISON OF K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION ALGORITHMS IN PATTERN RECOGNITION OF TAPIS FABRIC MOTIFS USING NON-GRAYSCALE LBP FEATURE EXTRACTION ]]>
<![CDATA[Octaviani, Adelia]]>;
<![CDATA[Kharisma Putra, Muhammad Pajar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2720
<![CDATA[Tapis fabric is a traditional garment of the Lampung people, made from cotton threads and adorned with silver or gold thread motifs. Tapis fabric is an important cultural heritage for the people of Lampung, Indonesia, with its motifs holding deep historical and symbolic meanings. The aim of this research is to develop a classification model for Tapis fabric patterns using K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms. This involves utilizing Local Binary Pattern (LBP) without converting the images to grayscale, thereby preserving the color in Tapis fabric motifs. The goal is to compare the performance of the two algorithms based on accuracy, precision, recall, and F1-score metrics. The application of digital image processing technology, particularly through the use of LBP feature extraction and appropriate classification algorithms, provides a significant contribution to facilitating the identification and classification of Tapis fabric. This research focuses on the development and identification of classification techniques to more accurately and efficiently distinguish the complex and varied Tapis fabric motifs. In this study, the KNN algorithm was applied with various k values, while the SVM algorithm was tested with different kernels, including RBF, linear, polynomial, and sigmoid. The results indicate that the KNN algorithm with k = 3 achieved the best results with an accuracy of 94%, while the SVM algorithm with the RBF kernel achieved the highest accuracy of 84%. These results show that KNN is more effective than SVM in the context of Tapis fabric motif classification for this study.]]>
<![CDATA[THE TEN CRITICAL SUCCESS FACTORS IN SOFTWARE DEVELOPMENT PROJECTS ]]>
<![CDATA[Indrajit, Richardus Eko]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2721
<![CDATA[In the rapidly evolving landscape of technology, software development plays a critical role in driving innovation and enhancing organizational efficiency. This paper explores the Critical Success Factors (CSFs) that significantly influence the outcomes of software development projects. By examining key components such as stakeholder involvement, effective commu- nication, and robust project management practices, the paper provides a comprehensive framework that can guide teams toward achieving their project goals. The analysis highlights the importance of well-defined requirements, skilled teams, and strong project management, while also addressing challenges unique to global software development. The findings suggest that a multifaceted approach, which integrates technical proficiency with effective communication and stakeholder engagement, is essential for navigating the complexities of software development and ensuring project success.]]>
<![CDATA[SENTIMENT ANALYSIS AND ENTITY DETECTION ON NEWS HEADLINES TO SUPPORT INVESTMENT DECISIONS ]]>
<![CDATA[Adhi, Ajar Parama]]>;
<![CDATA[Umuri, Khairil]]>;
<![CDATA[Triyono, Gandung]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3434
<![CDATA[Accurate investment decisions are often influenced by information available in the media. News headlines, as part of information media, can provide an initial picture of market sentiment and ongoing trends. This research examines the importance of making appropriate investment decisions with a focus on sentiment analysis and entity detection in news headlines as supporting tools. Through machine learning-based sentiment analysis and Named Entity Recognition (NER) techniques, this study identifies opinions and entities such as company names, stock indices, and industry sectors in news headlines. This research compares three machine learning algorithms, namely SVM, Naive Bayes, and Random Forest using cross-validation. The result shows that the best algorithm is SVM with weighted average F1-score of 76,68%. Furthermore, hyperparameter optimization is performed using Optuna for the SVM algorithm, which is an innovation in the context of sentiment analysis on news headlines in Indonesia. The result shows an increase in weighted average F1-score to 78,14%. For NER, a rule-based method is used by utilizing the Jaro-Winkler string similarity function. The combination of sentiment analysis and NER is then presented in the form of a dashboard using Google Looker Studio tools, with data from sentiment analysis and NER results being processed periodically and automatically using Google Workflows. This research makes a significant contribution by expanding the scope of analysis from just one or a few issuers to all entities published on news portals thanks to NER support, making the results relevant to support investment decisions that are responsive to dynamic market changes.]]>
<![CDATA[PERFORMANCE COMPARISON OF FASTER R-CONVOLUTIONAL NEURAL NETWORK (CNN) AND EFFICIENTNET FOR TRAIN DETECTION UNDER DIVERSE LIGHTING AND IMAGE QUALITY CONDITIONS ]]>
<![CDATA[Riska, Suastika Yulia]]>;
<![CDATA[Noercholis, Achmad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3438
<![CDATA[Object detection using computer vision has seen rapid advancements, especially with the advent of deep learning architectures such as Faster R-CNN and EfficientNet. This study compares the performance of the two models in detecting trains in various lighting conditions and noise disturbances. The dataset used consisted of 4500 train images which were divided into 70% for training, 20% for validation, and 10% for testing, reflecting real-world conditions. The evaluation was carried out using the Intersection over Union (IoU), Average Precision (AP), and Average Recall (AR) metrics. The results show that Faster R-CNN consistently excels in terms of detection accuracy, especially in less than ideal lighting conditions and under rain noise interference. In sufficient lighting conditions, Faster R-CNN showed a slightly superior AP score with a score of 0.844. As the lighting decreased, the difference between the two models became more pronounced, with Faster R-CNN recording an AP value of 0.810. In conditions with rain noise interference, the object detection performance of both models decreased more significantly, but the Faster R-CNN still excelled with an AP value of 0.798. Although EfficientNet is more efficient in terms of training speed, with a time of 5 hours and 37 minutes, and a smaller model size, Faster R-CNN shows higher reliability in complex environmental situations. This research provides important insights for the development of reliable and efficient train detection systems, taking into account the trade-off between resource efficiency and detection accuracy.]]>
<![CDATA[IMPLEMENTATION OF MACHINE LEARNING ON EMPLOYEE ATTRITION BASED ON PERFORMANCE PARAMETERS USING PARTICLE SWARM OPTIMIZATION AND ENSEMBLE CLASSIFER METHODS ]]>
<![CDATA[Fauziah, Difari Afreyna]]>;
<![CDATA[Muliawan, Agung]]>;
<![CDATA[Dimyati, Muhaimin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3442
<![CDATA[This research aims to apply machine learning to predict the start of employee attrition by considering performance parameters and other related factors in the company environment. Employee attrition refers to employee turnover in an organization for various reasons such as resignation, moving, retirement, and so on. This research uses a dataset originating from the IBM HR Analytics Employee Attrition dataset available on Kaggle (https://www.kaggle.com/) which consists of 35 attributes. Particle Swarm Optimization (PSO) method is a dimension reduction method to improve the efficiency and performance of machine learning models by reducing unnecessary data. The machine learning approaches used in the early prediction of employee attrition in this research are Support Vector Machine, Deep Learning and Neural Network methods. This research will combine the dimensionality reduction process with machine learning to obtain employee attrition prediction results that are optimized using the Ensemble method, namely Bagging and Boosting to increase the accuracy value of the prediction results. The results of this research show that applying dimensionality reduction using the PSO method can improve the accuracy of results on the IBM HR Analytics Employee Attrition dataset. The best accuracy in attrition prediction was obtained by the Deep Learning method with an accuracy value of 86.94%, a precision value of 88.90%, and a recall value of 96.40% after combining it with PSO and optimizing with Bagging.]]>
<![CDATA[DEVELOPMENT OF AN E-CANTEEN SYSTEM WITH EXTREME PROGRAMMING TO OPTIMIZE EFFICIENCY, TRANSPARENCY, AND ACCOUNTABILITY IN CANTEEN MANAGEMENT AT THE FACULTY OF ENGINEERING, MATARAM UNIVERSITY ]]>
<![CDATA[Murpratiwi, Santi Ika]]>;
<![CDATA[Al Qadri, Ramadhani]]>;
<![CDATA[Widiartha, Ida Bagus Ketut]]>;
<![CDATA[Irmawati, Budi]]>;
<![CDATA[Afwani, Royana]]>;
<![CDATA[Agitha, Nadiyasari]]>;
<![CDATA[Rassy, Regania Pasca]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3449
<![CDATA[The e-Canteen system enhances efficiency and user experience in electronic-based canteen services. The study aimed to develop a platform streamlining transactions, inventory management, and interaction between customers and canteen service providers. Before implementation, critical issues identified included manual inventory management and slow ordering processes, which created inefficiencies. The system allows customers to order, pay, and track stock availability online, leading to a more efficient and convenient purchasing process. For canteen service providers, automated inventory management helps optimize stock control and reduces manual errors, promising a more streamlined and error-free operation. The E-Canteen system offers significant benefits to both customers and service providers. Customers enjoy a more efficient and convenient purchasing process, while service providers benefit from optimized stock control and reduced manual errors, fostering a more productive and error-free work environment. Additionally, BLU Unram can monitor canteen performance, enabling data-driven decisions to improve services and policies. The system was developed using the Extreme Programming (XP) method, which ensured a user-centered design and rapid adaptation to feedback. Findings from the study demonstrated a 30% improvement in operational efficiency, with user satisfaction significantly increased according to internal surveys. The E-Canteen system addresses the operational challenges of managing canteen services and integrates smoothly with modern technological advancements, providing a scalable and adaptive solution for future growth. This system effectively resolves issues in traditional canteen management, offering benefits to customers and service providers regarding efficiency, convenience, and service quality.]]>
<![CDATA[COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING TOMATO LEAF DISEASES ]]>
<![CDATA[Andrianto, Diky]]>;
<![CDATA[Prathivi, Rastri]]>;
<![CDATA[Liu, Meifang]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3477
<![CDATA[Tomato plants are usually easily attacked by diseases, either viruses or fungi, resulting in a significant reduction in the quality and quantity of crop production. Tomato production is at risk from various diseases affecting the leaves. Early diagnosis of these diseases allows farmers to take preventive action and protect their crops. The use of artificial intelligence, especially deep learning, has greatly improved plant disease detection systems. Advances in computer vision, particularly Convolutional Neural Networks (CNN), have shown reliable results in image classification and identification. Below is previous research on identifying tomato leaf diseases.]]>
