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[DEPRESSION DETECTION ON TWITTER USING GATED RECURRENT UNIT ]]>
<![CDATA[Holle, Alfransis Perugia Bennybeng]]>;
<![CDATA[Warih Maharani]]>
<![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.2024.5.1.1187
<![CDATA[In the present era, technological advancements have significantly impacted society, particularly in the use of social media. One popular social media platform is Twitter, where people could share moments, thoughts, and statuses. However, since the COVID-19 pandemic, the usage of Twitter increased, and some users began exhibiting symptoms of depression. The condition of depression required a means to channel emotions that could assist users in coping. By employing the GRU method and Word2Vec feature extraction, we developed a depression detection system capable of analyzing users' Twitter posts and identifying potential signs of depression. The dataset used in this research was obtained from 165 participants who agreed to utilize their personal Twitter data and completed a questionnaire based on the Depression Anxiety and Stress Scales-42 (DASS-42). The questionnaire results served as labels that were processed for Word2Vec feature extraction and subsequently fed into the GRU model. The evaluation revealed an accuracy rate of 57.58% and an f1-score of 56.25. By using the bidirectional layer in the model, there is an improvement in precision, recall, and f1-score values.]]>
<![CDATA[OPTIMIZATION OF HYPERPARAMETERS FOR LSTM-BASED SENTIMENT ANALYSIS ON FACIAL SERUM DATASETS ]]>
<![CDATA[Saputri, Merly]]>;
<![CDATA[Hermanto, Teguh Iman]]>;
<![CDATA[Nugroho, Imam Ma'ruf]]>
<![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.2024.5.1.1192
<![CDATA[Air pollution and environmental pollutants directly exposed to the skin can damage the skin by accelerating premature aging, increasing the risk of acne, and causing hyperpigmentation. Skincare products such as facial serums containing vitamin C, niacinamide, and vitamin E can effectively address these issues. Awareness of the importance of using facial serums is increasing, so information about product quality through user reviews is essential before placing an order. Sentiment analysis used to classify product reviews into positive or negative, thus providing an overview of the product quality sought before placing an order. This research uses the Long Short-Term Memory (LSTM) method for the sentiment classification process. In this process, the text is converted into a number vector through feature extraction using Word2Vec. In addition, several hyperparameters such as the number of epochs, batch size, and activation function are tested to obtain optimal accuracy results. Testing the number of epochs was conducted with variations of 10, 15, and 20 to determine the performance of the resulting model as the number of epochs increased. Testing the batch size is done to evaluate the batch size in influencing the performance of the model. The batch sizes tested were 16, 32, and 64. In addition, choosing the best activation function can help the LSTM model learn more complex patterns and improve performance in sentiment analysis. The activation functions tested were Softmax, Sigmoid, and Softplus. The results of this study show that the optimal combination of the number of epochs 20, batch size 16, and Softmax activation function can provide optimal accuracy of 96.45%.]]>
<![CDATA[COMPARISON OF ACCURACY LEVELS OF RANDOM FOREST AND K-NEAREST NEIGHBOR (KNN) ALGORITHMS FOR CLASSIFYING SMOOTH BANK CREDIT PAYMENTS ]]>
<![CDATA[Aji Santoso, Bayu]]>;
<![CDATA[Kusrini, Kusrini]]>;
<![CDATA[Hartanto, Anggit 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.2024.5.1.1195
<![CDATA[Providing credit is one of the bank offers offered to customers, but extending credit to customers who are not appropriate can cause problems such as customers who do not pay installments on time and even delay payment of installments for several months until bad credit occurs so that this can be detrimental to the bank. Therefore, in this study a comparative method will be carried out to find out which method is the best in classifying the smoothness of bank credit payments. It is hoped that the results of the research can be used as material for consideration by the bank in the selection of bank credit customers. In this study using a dataset from the UCI Machine Learning Repository, the credit payment data totaled 29,998. The dataset is split by dividing 70% train data and 30% test data with the amount of each data, namely 24000 train data and 6000 test data. Meanwhile, the labels used are Eligible and Ineligible. In this study, implementing the data mining process using the CRISP-DM framework and using the Python programming language. From the results of the evaluation using the confusion matrix, the best accuracy value was obtained for the random forest algorithm, namely 82.22%, precision of 80.44%, recall of 82.22% and f1-score of 80.0%. Meanwhile, the KNN algorithm obtains an accuracy value of 81.55%, a precision of 79.5%, a recall of 81.55% and an f1-score of 79.11%. Based on the results of this evaluation, the Random Forest algorithm has the best accuracy compared to the KNN algorithm in classifying bank credit payments.]]>
<![CDATA[ANALYSIS OF PUBLIC SENTIMENT RELATED TO THE FAILURE OF INDONESIA TO HOST U-20 USING MULTINOMIAL NAÏVE BAYES CLASSIFIER ]]>
<![CDATA[Zaini, Fachri]]>;
<![CDATA[Sari, Jessica Windi]]>;
<![CDATA[Hasan, Firman Noor]]>
<![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.2023.4.6.1209
<![CDATA[The case of Indonesia's failure to host the U-20 World Cup in 2023 has become a hot topic of discussion in Indonesia. The rejection of the Israel U-20 national team and security factors by FIFA are considered the main reasons for the cancellation. This raises many issues and controversies from various parties. In this study, sentiment analysis using the Naive Bayes algorithm was conducted. Researchers use the naive bayes algorithm because this algorithm has high accuracy with simple calculations. The data obtained in this study came from 250 tweets of Twitter data with a ratio of training and test data of 7:3. The results showed good data classification with 97.26% accuracy, 93.33% precision, and 100% recall. In conclusion, the classification model developed can describe public sentiment related to Indonesia's failure in the U-20 World Cup well.]]>
<![CDATA[SYSTEMATIC LITERATURE REVIEW ON INFORMATION SECURITY RISK MANAGEMENT IN PUBLIC SERVICE ORGANIZATIONS ]]>
<![CDATA[Rifia Andita]]>;
<![CDATA[Faizan Aditya]]>
<![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.2024.5.1.1223
<![CDATA[For an organization, information security is a priority. Within the rapid growth of information technology, information becomes easier to access, processed, and used in organization globally. Using information systems in government will improve efficiency, effectiveness, transparency, and accountability in respect of good governance. Regarding the use of information technology sometimes it does not align with its purpose, because there is uncertainty or particular risk that must be faced in using IT. The study conducts a systematic literature review (SLR) to understand the steps and frameworks for information security risk management. Data sources such as IEEE Xplore, ScienceDirect, Proquest, and ACM from 2009 to 2023 are used to obtain literature. Sixteen papers were obtained to complete this study. This research identifies three frameworks that can be used in information security risk management: ISO 27005, NIST SP 800-30, and Cobit 5 For Risk. stages in information security risk management in general are Context Formation, Risk Identification, Risk Assessment, Risk Treatment, and Risk Monitoring.]]>
<![CDATA[PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING ]]>
<![CDATA[Nurhadi, Nurhadi]]>;
<![CDATA[Winanto, Eko Arip]]>;
<![CDATA[Said, Rahaini Mohd]]>;
<![CDATA[Jasmir, Jasmir]]>;
<![CDATA[Afuan, Lasmedi]]>
<![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.2024.5.2.1228
<![CDATA[Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.]]>
<![CDATA[APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY ]]>
<![CDATA[Muhamad Fadel]]>;
<![CDATA[Kanasfi, Kanasfi]]>;
<![CDATA[Wibowo, Arief]]>
<![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.2024.5.1.1229
<![CDATA[Employee compliance is a commitment to comply with regulations and stay away from matters that are prohibited in the laws and or company regulations which if not obeyed, then employees are given disciplinary sanctions. Employee discipline is an obligation and willingness of employees in obeying all existing rules in a company to achieve its vision and mission, a high-level employee disciplinary violation rate of 38% at PT. HCI who are engaged in financial service sector can have a negative impact on a company's reputation, meanwhile a low level of employee disciplinary violations in a company can have a positive impact on the company's reputation.This paper aims to predict the possibility of employees committing discipline violations and evaluating the performance of accuracy by using Machine Learning Random Forest, Decision Tree, and Naive Bayes techniques. The test results prove that the Machine Learning Random Forest technique is the best model with the highest value in terms of accuracy with a value of 87.30%, while the Machine Learning Decision Tree and Naive Bayes technique has a value of 83.28%and 70.27% respectively, the value from each of the Machine Learning techniques, the comparison was made using majority voting techniques, so as to produce a total accuracy value of 85.31%.With this high accuracy value, the Random Forest model is proven to have better performance individually in analyzing the prediction of disciplinary violations in the application of human resources at company, while the total accuracy value uses a majority voting model of 85.31%, slightly decreased due to the high level of accuracy of the Naïve Bayes model compared to other algorithm models.]]>
<![CDATA[REDESIGNING THE UI/UX WEBSITE FOR THE INDUSTRIAL ENGINEERING DEPARTMENT AT MULAWARMAN UNIVERSITY USING DESIGN THINKING METHOD ]]>
<![CDATA[Hadi, Amirul]]>;
<![CDATA[Sukmono, Yudi]]>;
<![CDATA[Harjanto, Arif]]>;
<![CDATA[Suprihanto, Didit]]>
<![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.2024.5.1.1252
<![CDATA[The industrial engineering department website of Mulawarman University faces challenges in disseminating academic information due to inadequate maintenance. This research utilizes the design thinking method to enhance the website's user interface and user experience (UI/UX) and evaluate its usability against the current version. The decision to use design thinking is grounded in its established effectiveness in improving website UI/UX. The study involves empathizing, defining, ideating, prototyping, and testing phases. User needs and concerns were identified through interviews, observations, and tools such as affinity diagrams, user personas, and user journey maps. Creative concepts were developed and organized using the priority matrix, sitemap, and user flow. Wireframes and mockups facilitated pre-construction design visualization. The redesigned UI/UX prototype resulted in a significantly improved user experience, as evidenced by the results of the user experience questionnaire and system usability scale. The initial UEQ scores indicated low usability, with attractiveness at 0.85, perspicuity at 1.01, efficiency at 0.71, dependability at 0.76, stimulation at 0.41, and novelty at -0.18. Following the redesign, scores notably increased, attractiveness at 2.06, perspicuity at 1.89, efficiency at 1.94, dependability at 1.79, stimulation at 2.05, and novelty at 1.64. SUS testing revealed an average rise to 76 from 63 points, indicating enhanced user satisfaction, acceptance, and descriptive ratings. The website's grade improved from C to B after the redesign. In conclusion, the UI/UX redesign successfully enhanced website usability and user experience.]]>
<![CDATA[JOB-POSITION RECOMMENDER SYSTEM USING KNOWLEDGE BASED RECOMMENDATION METHOD AT ATMI POLYTECHNIC SURAKARTA ]]>
<![CDATA[Pratiwi, Dinita Christy]]>;
<![CDATA[Atina, Vihi]]>;
<![CDATA[Maulindar, Joni]]>
<![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.2024.5.1.1258
<![CDATA[ATMI Polytechnic Surakarta, one of the vocational colleges in Surakarta, currently has 152 employees and 13 managerial positions. The human resource management (HRM) unit has a strategy for selecting study program leaders and managers, but the procedure is still done by hand and is not based on standardized calculations. Therefore, a job-position recommender system is needed. This system aims to recommend candidates with the highest similarity score to the desired job criteria. The recommendation system was developed using the Knowledge-Based Recommendation method and the system development method employs a prototype. The stages included communication, quick planning, quick design modeling, prototype construction, and deployment, delivery and feedback. The calculation results show that an employee with the initials ADR has the highest similarity score for the job-position as head of the D3 Industrial Mechanical Engineering (TMI) study program with a score of 0.87. Therefore, this system can be used as a reference mechanism in building a job recommendation system at ATMI Polytechnic Surakarta.]]>
<![CDATA[ANALYSIS FEATURE EXTRACTION FOR OPTIMIZING ARRHYTHMIA CLASSIFICATION FROM ELECTROCARDIOGRAM SIGNALS ]]>
<![CDATA[Satria Mandala]]>;
<![CDATA[Ramadhan, Yusril]]>
<![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.2024.5.1.1267
<![CDATA[Heart disease is the primary cause of death globally, with arrhythmias, such as Premature Atrial Contraction (PAC), Atrial Fibrillation (AF), and Premature Ventricular Contraction (PVC), being critical heart rhythm abnormalities. Although numerous studies have utilized feature extraction from electrocardiogram (ECG) signals to detect these conditions, optimal accuracy has not been achieved. Therefore, this research aims to identify relevant features and achieve better results by using dynamic feature extraction methods. The extracted features used are RR Interval, PR Interval, and QRS Complex. By combining 2 feature extractions - RR Interval & PR Interval, RR Interval & QRS Complex, and PR Interval & QRS Complex - this study achieves a high level of accuracy on the RR Interval & QRS Complex feature extraction, reaching 97.60%, with a specificity of 98.30% and sensitivity of 96.58%.]]>
