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[IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION IN K-NEAREST NEIGHBOR ALGORITHM AS OPTIMIZATION HEPATITIS C CLASSIFICATION ]]>
<![CDATA[Susi Setianingsih]]>;
<![CDATA[Maria Ulfa Chasanah]]>;
<![CDATA[Yogiek Indra Kurniawan]]>;
<![CDATA[Lasmedi Afuan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.2.980
<![CDATA[Hepatitis has become a public health problem that is generally caused by infection with the hepatitis virus. One type of hepatitis caused by a virus is Hepatitis C. This disease can cause patients to experience inflammation of the liver. In the worst conditions, it can even lead to death. Initial predictions need to be made to increase the awareness of each individual against the threat of Hepatitis C by using the K-Nearest Neighbor method. K-Nearest Neighbor is a classification method that can give a pretty good percentage result in classifying, especially when using large training data. However, K-Nearest Neighbor still has a weakness, namely the determination of the value of K that is less precise so that it can reduce classification performance. To overcome these shortcomings, the researchers used the implementation of Particle Swarm Optimization on K-Nearest Neighbor to find the optimal K value. The existence of this implementation is expected to be able to increase the value of accuracy in classification and overcome solutions to weaknesses in the K-Nearest Neighbor algorithm. From the results of the K-Nearest Neighbor test, the accuracy value is 97.24% at K=5 and K=3. As for the results of testing the implementation of Particle Swarm Optimization on the K-Nearest Neighbor, there was an increase in the accuracy value of 2.07% to 99.31%. This test shows that the implementation of PSO can overcome the shortcomings of KNN and this model can be used as the best solution to determine the classification of Hepatitis C disease.]]>
<![CDATA[SCIENTIFIC ARTICLES RECOMMENDATION SYSTEM BASED ON USER’S RELATEDNESS USING ITEM-BASED COLLABORATIVE FILTERING METHOD ]]>
<![CDATA[Ferzha Putra Utama]]>;
<![CDATA[Triska Mardiansyah]]>;
<![CDATA[Ruvita Faurina]]>;
<![CDATA[Arie Vatresia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.702
<![CDATA[Scientific article recommendation still remains one of the challenging issues in education, including learning process. Difficulties in finding related articles from research history and research interest have been experienced by students in collage affecting the duration of study and research time. This paper proposed a new solution by building a search engine to collect and to recommend articles related to student research topics. The system combined the web scraping method as an article data retrieval technique on google scholar and item-based collaborative filtering to recommend the article. Parameters result produced based on items of user’s history, including item-searched, clicked, and downloaded. The system was built on a web-based scientific article recommendation system using python programming language. This system recommends articles based on the preferences of users and other users who are affiliated and who have an interest in the same item. This research showed that the validation result from the system obtained a recommendation accuracy value over 0.516801. The percentage of the RMSE error value of the recommendation system is 8.62%, or in other words that the accuracy of the recommendation system is 91.28%.]]>
<![CDATA[ANALYSIS OF E-OFFICE SYSTEM USER SATISFACTION AT LAND OFFICE PEKANBARU CITY USING END USER COMPUTING SATISFACTION METHOD ]]>
<![CDATA[Fachrurozi]]>;
<![CDATA[Febi Nur Salisah]]>;
<![CDATA[Tengku Khairil Ahsyar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.723
<![CDATA[An E-Office system is available at the National Land Agency, which is part of the Ministry of Agrarian Affairs and Spatial Planning and the National Land Agency. It is designed to make office administration easier. However, the use of E-Office is still not optimally used, such as the minutes feature that is underused, errors in sending letters that are sent not as desired, the dashboard display is less attractive and the guide menu cannot be accessed because of a link error. The purpose of this study is to evaluate user satisfaction with the E-Office system and identify its results. The End User Computing Satisfaction (EUCS) method with six variables (content, accuracy, format, ease of use, timeliness, user satisfaction) was used by researchers to assess user satisfaction with the E-Office system. 37 individuals from the E-Office system user population comprised the sample. The results of the proposed hypothesis test show that the hypothesis is accepted and has a significant effect of 78.9% on user satisfaction.]]>
<![CDATA[PERFORMANCE OF K-MEANS CLUSTERING AND KNN CLASSIFIER IN FISH FEED SELLER DETERMINATION MODELS ]]>
<![CDATA[Esmi Nur Fitri]]>;
<![CDATA[M. Hafidz Ariansyah]]>;
<![CDATA[Sri Winarno]]>;
<![CDATA[Fikri Budiman]]>;
<![CDATA[Asih Rohmani]]>;
<![CDATA[Junta Zeniarja]]>;
<![CDATA[Edi Sugiarto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.725
<![CDATA[Feed is a crucial variable because it can determine the success of fish farming. Farmers can use two types of artificial feed, namely alternative feed and pellets. Many cultivators need pellets as the main food for the fish they are cultivating because the pellets contain a composition that has been adjusted to their needs based on the type and age of the fish. However, currently, cultivators are facing problem, namely the high price of fish pellets on the market. Therefore, an analysis of the classification of the selection of fish feed sellers is needed according to several criteria like the number of types of feed, price, order, delivery, payment, availability of discounts, and the number of assessments. This study conducted a predictive analysis to determine the criteria for selecting fish feed sellers in Kendal Regency by utilizing the K-Means Clustering and KNN Classifier methods in the classification method. This research aims to compare the fish feed seller classification method where the pattern of fish feed seller is identified by K-Means Clustering and KNN Classifier, and then the researcher conducts performance appraisal and evaluation. The results of this study are decision-making patterns to help formulate strategies for cultivators and other interested parties. For verifying the method used, measurements were made to obtain an accuracy value where K-Means was 98.6% and KNN was 86.7%.The results of this study indicate that the K-Means Clustering and KNN Classifier methods can classify the selection of freshwater fish feed sellers in Kendal Regency.]]>
<![CDATA[IMPROVING PERFORMANCE OF STUDENTS’ GRADE CLASSIFICATION MODEL USES NAÏVE BAYES GAUSSIAN TUNING MODEL AND FEATURE SELECTION ]]>
<![CDATA[M Hafidz Ariansyah]]>;
<![CDATA[Esmi Nur Fitri]]>;
<![CDATA[Sri Winarno]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.737
<![CDATA[Student grades are a relevant variable for predicting student academic performance. In achieving good and quality student performance, it is necessary to analyze or evaluate the factors that influence student performance. When a educator can predict students' academic performance from the start, the educator can adjust the way of learning so that learning can run effectively. The purpose of this research is to study how it is applied to determine the interrelationships between variables and find out which variables have an effect, then use it as a feature selection technique. Then, researchers review the most popular classifier, Gaussian Naïve Bayes (GNB). Next, we survey the feature selection models and discuss the feature selection approach. In this study, researchers will classify student grades based on existing features to evaluate student performance, so it can guide educators in selecting learning methods and assist students in planning the learning process. The result is that applying Gaussian Naïve Bayes (GNB) without feature selection has a lower accuracy of 10.12% while using feature selection the accuracy increases to 10.12%.]]>
<![CDATA[IMPROVING ONLINE MEETING EFFICIENCY USING LATENT DIRICHLET ALLOCATION (LDA) AND SOCIAL NETWORK ANALYSIS (SNA) METHODS ]]>
<![CDATA[Megananda Hervita Permata Sari]]>;
<![CDATA[Utomo Budiyanto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.751
<![CDATA[The pandemic period can change the habits of a person and organization, where all meetings are not held face-to-face/offline but virtually, so it is not uncommon for meetings to be attended by employees who are not Persons in Charge (PIC) on certain meeting topics. This study aims to identify trends in time, day, and duration of meetings within the Secretariat General of the Ministry of Finance and to cluster meeting matters into several themes so that further identification can be carried out to provide recommendations for units having duties related to the meeting using networking analysis. This study uses the Natural Language Processing (NLP) method with Latent Dirichlet Allocation (LDA) which can conclude the factors that represent topics to produce topic clustering and Social Network Analysis (SNA) modeling using the Degree Centrality method to find out the closest relationship between topics and names. unit based on the highest centrality value and the possibility of a unit attending a meeting that discusses a particular topic. Data used in this reseacrh are meetings held during April 2020 up to April 2022 with 59,891 data records. The modeling results shows clustering result dashboard based on meeting topics and to produce an analysis of which meeting topics are often discussed and become a concern. The results of the research are expected to be used to provide recommendations to unit leaders to assign meeting dispositions for each PIC to attend the meeting.]]>
<![CDATA[FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE ]]>
<![CDATA[Wida Sofiya]]>;
<![CDATA[Erwin Budi Setiawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.855
<![CDATA[Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets.]]>
<![CDATA[IDENTIFYING POSSIBLE RUMOR SPREADERS ON TWITTER USING THE SVM AND FEATURE LEVEL EXTRACTION ]]>
<![CDATA[Claudia Mei Serin Sitio]]>;
<![CDATA[Yuliant Sibaroni]]>;
<![CDATA[Sri Suryani Prasetiyowati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.868
<![CDATA[In everyday life, many events occur and give rise to various kinds of information, which are also rumors. Rumors can cause fear and influence public opinion about the event in question. Identifying possible rumor spreaders is extremely helpful in preventing the spread of rumors. Feature extraction can be done to expand the feature set, which consists of conversational features in the form of social networks formed from user replies, user features such as following, tweet count, verified, etc., and tweet features with text analysis such as punctuation and sentiment values. These features become instances used for classification. This study aims to identify possible spreaders of rumors on Twitter with the SVM classification model. This instance-based classification algorithm is good for linear and non-linear classification. In the non-linear classification, additional kernels are used, such as linear, RBF, and sigmoid. The research focuses on getting the best model with high performance values from all the models and kernel functions that have been defined. It was found that the SVM classification model with the RBF kernel has a high overall performance value for each data combination with a ratio of the amount of data is 1:1 or the difference is very large. This model gives accurate results with an average of 97.02%. With a wide distribution of data, the SVM classification model with the RBF kernel is able to map the data properly.]]>
<![CDATA[FUEL INCREASE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM AS FEATURE SELECTION ]]>
<![CDATA[Laura Imanuela Mustamu]]>;
<![CDATA[Yuliant Sibaroni]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.881
<![CDATA[BBM, or fuel oil, is one of the essential needs of the Indonesian people. The government's policy regarding the increase in fuel prices raises many opinions from the public. Twitter is one of the social media that Indonesian people often use to express opinions on a topic. In this study, sentiment analysis was carried out on public opinion regarding the fuel price increase policy from Twitter social media. This research is expected to help determine public opinion regarding the fuel price increase policy with positive, neutral and negative sentiments. The sentiment analysis method used is the Support Vector Machine (SVM) classification algorithm. The results of the accuracy of SVM were compared with accuracy by adding a feature selection process. The Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) algorithms are used for the feature selection method. After several experiments using the three methods, the SVM method with the Radial Basis Function (RBF) kernel produced the best accuracy of 71.2%. The combination of the SVM method with the RBF and PSO kernels obtained an accuracy of 68.84%, and the combination of the RBF and GA kernel SVM methods obtained an accuracy of 69.52%.]]>
<![CDATA[DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW ]]>
<![CDATA[Canny Siska Georgina]]>;
<![CDATA[Farroh Sakinah]]>;
<![CDATA[M. Ryan Fadholi]]>;
<![CDATA[Setiadi Yazid]]>;
<![CDATA[Wenni Syafitri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.3.886
<![CDATA[Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process.]]>
