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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<![CDATA[STREAM CIPHER ALGORITHM FOR ENCRYPTING TEXT USING LOGISTIC MAP, AUTO PARAMETERS LINEAR CONGRUENTIAL GENERATOR (APLCG), AND GRAY CODE ]]>
<![CDATA[Fanggidae, Adriana]]>;
<![CDATA[Polly, Yulianto Triwahyuadi]]>;
<![CDATA[Sina, Derwin Rony]]>;
<![CDATA[Letelay, Kornelis]]>;
<![CDATA[Nabuasa, Yelly Yosiana]]>;
<![CDATA[Boru, Meiton]]>;
<![CDATA[Ledoh, Juan Rizky Mannuel]]>
<![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.2024.5.2.1535
<![CDATA[One aspect frequently posing a challenge in cryptography pertains to the length of the secret key that users must remember. Achieving the requisite key length for cryptographic algorithms necessitates key padding. However, it is crucial to note that key padding is susceptible to predictable patterns. Both the Linear Congruential Generator (LCG) and gray code are algorithms employed to generate sequences of padded key bits. Regrettably, LCG requires the determination of two pre-defined parameters, whereas the Auto Parameters Linear Congruential Generator (APLCG) automatically establishes these parameters. These parameters play a pivotal role in generating unique sequences of random integers. To fortify key security, the generation of new keys is performed using a modified logistic map, an enhancement of the standard logistic map that exhibits random behavior consistently. Stream cipher, an encryption algorithm, necessitates a continuous key stream matching the bit or byte length of the message. We conducted experiments on stream cipher algorithms employing key streams generated from APLCG, gray code, and modified logistic map. Twenty text documents were utilized as test samples. The outcomes indicate that stream ciphers employing APLCG, gray code, and modified logistic map demonstrate high-security performance based on the statistical analysis conducted.]]>
<![CDATA[IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC ]]>
<![CDATA[Cahyani, Salsabila Nida]]>;
<![CDATA[Saraswati, Galuh Wilujeng]]>
<![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.2023.4.6.1536
<![CDATA[The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%.]]>
<![CDATA[ANALYSIS OF THE INFLUENCE OF BUSINESS INTELLIGENCE ON BEVERAGE SALES KONNICHIWA COFFEE USING THE METHOD EQUIVALENCE CLASS TRANSFORMATION ]]>
<![CDATA[Satria, Leonard Vincent]]>;
<![CDATA[Ayunda, Afifah Trista]]>
<![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.2023.4.6.1552
<![CDATA[Konnichiwa Coffee shop is one of the business beverage that sells various types of drinks. Determination of price discounts or product promotions sometimes doesn’t match what customers wants and needs. Another obstacle found was that there were no promotions for consumers who bought directly at Konnichiwa Coffee outlets. This causes less than optimal sales strategies and store promotion strategies. Determining menus that are often purchased simultaneously by consumers can be a reference for owners in determining promotional strategies. Therefore, this research was conducted to look for association patterns between menus that can implement business intelligence (BI) in the association rules method. One of the association rules algorithms is the ECLAT algorithm. The ECLAT algorithm is used because it is more efficient and faster in terms of time. The data used in this research were 214 products from 100 transactions with 26 types of drink menus. The resulting pattern refers to a minimum support value of 3% and a minimum confidence of 30%. This means that transaction data that has association patterns or that were purchased together is only 3% of the total transaction data with a confidence level of 30%. From the results obtained, the Java Latte, Kopi Latte and Sapporo Latte menus are the menus that are most often purchased together so they can be used as a marketing strategy for Konnichiwa Coffee.]]>
<![CDATA[REAL-TIME DROWSY FACE DETECTION FOR ONLINE LEARNING BASED ON RANDOM FOREST AND DECISION TREE ALGORITHMS ]]>
<![CDATA[Ani Dijah Rahajoe]]>;
<![CDATA[Subekti, Mohamad Rafli Agung]]>;
<![CDATA[Suriansyah, Muhammad]]>
<![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.2023.4.6.1554
<![CDATA[In the current era, technology regarding artificial intelligence has developed rapidly and has been used in various areas of life. Face detection is one of the applications of Artificial Intelligence. This research aims to detect students' faces during the online learning process and succeeded in getting positive feedback when tested on students. Student detection includes drowsy and alertness. The method is via webcam in real-time so that the screen will show whether the student is drowsy or alert. In the trial, the teacher can find out who is in a drowsy and alert condition. On the other hand, students can find out that they fall into the drowsy or alert category. So that both parties immediately respond to what should be done based on the classification results. The algorithms used are Decision Tree and Random Forest. The accuracy results of the Random Forest algorithm are better than the Decision Tree algorithm, namely 65 percent, while the Decision Tree algorithm is 58 percent. The division of training data and test data uses a Kfold of 5. When Kfold is equal to 2, both algorithms have the highest accuracy, where Random Forest has an accuracy of 85 percent, and Decision Tre has an accuracy of 65 percent.]]>
<![CDATA[REAL TIME ONLINE EXAM PROCTORING SYSTEM IN HIGHER EDUCATION USING WEBRTC TECHNOLOGY ]]>
<![CDATA[Maoeretz Engel, Mychael]]>;
<![CDATA[Agustinus, Jeems Terri]]>
<![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.2023.4.6.1564
<![CDATA[The low level of trust in online exam results from students is a major problem because it is difficult to monitor whether test takers are taking the exam honestly according to their own abilities. Even though it has been assisted by the presence of video conferencing applications such as Zoom, Google Meet, Cisco Webex and similar applications, online exam proctoring is still unable to run effectively. Cheating in online exams, such as using dual monitors, is very possible for exam participants. Therefore, as a future preventive measure in the online exam process, a system is needed that can accommodate this concern. This research will create an online exam supervision system with WebRTC technology which has features to accommodate real-time supervision. The System Development Life Cycle method will be used in software development with 5 main stages, namely Requirement Analysis, Design, Development, Testing, and Maintenance. Implementation of the system was carried out during the online examination process for a class at one of the universities in Surabaya. Finally, the test results show that features such as: Live Proctoring get a score of 4.5; Attention Alert gets a score of 5; Exam Lock scored 4.5; Live Alert scored 4.5; and Tab & Window Detection got a score of 4; shows that this system has succeeded in providing a solution in online exam proctoring needs.]]>
<![CDATA[COMPARISON OF ALGORITHM BETWEEN CLASSIFICATION & REGRESSION TREES AND SUPPORT VECTOR MACHINE IN DETERMINING STUDENT ACCEPTANCE IN STATE UNIVERSITIES ]]>
<![CDATA[M. Anwar Sadat]]>;
<![CDATA[Pujiono, Pujiono]]>;
<![CDATA[Pambudi, Anggun]]>;
<![CDATA[Ibad, Sholihul]]>
<![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.2023.4.6.1565
<![CDATA[Higher education entrance selection activities are intended to obtain superior student candidates. The opportunity to take part in the selection is given to all high school graduate students and equivalent. The student entrance test at PTN consists of three types of selection routes, namely the SNMPTN or invitation route, the SBMPTN, and the independent examination held by state universities. Starting from the dataset, data selection was carried out from 143 students' data and 7 attribute selections were carried out using preprocessing using data transformation first. The aim of using data transformation is to simplify the data training process for MAN 1 students in Cirebon. Preprocessing for prediction of classification results, accuracy of testing data for 143 students is implemented in the program and the resulting calculation process will be more efficient. After going through the preprocessing stage, the data is divided into training data and testing data using 10-fold cross validation. Next, for the classification process, a comparison of two methods will be used, namely for the first method using CART, the second method using SVM by adding Gain ratio weighting. The results of the research show that in the first experiment the researcher carried out a comparative trial of cross validation and classification performance and used the CART and SVM algorithms. The results comparison using the CART algorithm gets an accuracy of 86.10% and the SVM algorithm method for classifying students entering PTN was 86.71%.]]>
<![CDATA[STATE OF THE ART ANALYSIS ON BATTERY-RELATED THREATS AND DEFENSES OF IOT DEVICES USING KITCHENHAM ]]>
<![CDATA[Azka Ghafara Putra Agung]]>;
<![CDATA[Aditya Pradana]]>;
<![CDATA[Rahmat Budiarto]]>
<![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.2024.5.1.1567
<![CDATA[The Internet of Things (IoT) keeps growing in size every year, but its growth also companied with threats to its security. This paper centers on the research article that focuses on various attacks on IoT system and devices through power drain techniques targeting IoT devices. This paper discusses various existing attack models, and security model. The main objective is to reveal the state of the art of the security issues of IoT related to attacks to the devices’ power. The literature review is performed by implementing Kitchenham method and utilizing Google Scholar and Science Direct databases. 42 publications between 2010 and 2023, fulfilling the selection criteria are selected and comprehensively reviewed. To counteract power drain-induced Denial of Service (DoS) threats, the paper evaluates existing defense mechanisms specifically tailored to mitigate these attacks. These defenses encompass adaptive power management strategies, hardware-level security enhancements, and network-level security measures. The effectiveness, practicality, and trade-offs of these defense mechanisms are examined. The combination of these papers offers comprehensive insights into battery-related security concerns in the IoT landscape, with sleep deprivation attacks, Denial of Service-induced battery drain, and Vampire attack, highlighting the importance of robust security measures in the IoT ecosystem.]]>
<![CDATA[OPTIMIZING BUTTERFLY CLASSIFICATION THROUGH TRANSFER LEARNING: FINE-TUNING APPROACH WITH NASNETMOBILE AND MOBILENETV2 ]]>
<![CDATA[Putri, Ni Kadek Devi Adnyaswari]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Putra, Permana Langgeng Wicaksono Ellwid]]>
<![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.2024.5.3.1583
<![CDATA[Butterflies play a significant role in ecosystems, especially as indicators of the state of biological balance. Each butterfly species is distinctly different, although some also show differences with very subtle traits. Etymologists recognize butterfly species through manual taxonomy and image analysis, which is time-consuming and costly. Previous research has tried to use computer vision technology, but it has shortcomings because it uses a small distribution of data, resulting in a lack of programs for recognizing various other types of butterflies. Therefore, this research is made to apply computer vision technology with the application of transfer learning, which can improve pattern recognition on image data without the need to start the training process from scratch. Transfer learning has a main method, which is fine-tuning. Fine-tuning is the process of matching parameter values that match the architecture and freezing certain layers of the architecture. The use of this fine-tuning process causes a significant increase in accuracy. The difference in accuracy results can be seen before and after using the fine-tuning process. Thus, this research focuses on using two Convolutional Neural Network architectures, namely MobileNetV2 and NASNetMobile. Both architectures have satisfactory accuracy in classifying 75 butterfly species by applying the transfer learning method. The results achieved on both architectures using fine-tuning can produce an accuracy of 86% for MobileNetV2, while NASNetMobile has a slight difference in accuracy of 85%.]]>
<![CDATA[GRAPHICAL COMPUTING FOR BATIK PATTERN DESIGN BASED ON L-SYSTEM ]]>
<![CDATA[Hidayat, Eka Wahyu]]>;
<![CDATA[Anshary, Muhammad Adi Khairul]]>;
<![CDATA[Nur Shofa, Rahmi]]>
<![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.2024.5.2.1595
<![CDATA[The challenge faced by the batik industry in the industrial era 5.0 is the adaptation to technology in the production process. One way to overcome this challenge is to start from the basics in the batik industry, namely the creative process of designing batik patterns. It is important to pay special attention to this process to enhance digital transformation in the batik industry. The purpose of this paper is to present the design and creation of batik patterns using the L-System-based fractal approach. Previous research has shown that the L-System can be used to model plant growth in 2D and 3D contexts. In a similar way, the L-System is used in this study to create batik patterns. Experiments were conducted through three stages, namely Data Acquisition, Data Identification, and Modeling. The experiment results in a dataset of batik motifs that can be used as parameters to replace line segments in the L-System. The design and creation of batik patterns using the L-System only needs to be done once, so that from one pattern, a variety of different motifs can be produced easily by simply changing the parameters. This shows that the design and creation of batik patterns using L-System is more efficient and practical. In addition, the fractal dimension calculation is used to understand and describe the fractal properties of the resulting objects. In this study, it was found that there are four motifs without ornaments that have higher fractal dimension values than motifs with equivalent ornaments.]]>
<![CDATA[BOARDING HOUSE RECOMMENDATION WITH COLLABORATIVE FILTERING USING THE GENERATIVE ADVERSARIAL NETWORKS (GANS) METHOD ]]>
<![CDATA[Septariken, Mohammad Fajra]]>;
<![CDATA[Richasdy , Donni]]>;
<![CDATA[Dharayani, Ramanti]]>
<![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.2024.5.2.1598
<![CDATA[This research represents a concerted effort to tackle the pressing challenge of facilitating a personalized and efficient boarding house recommendation system tailored to individual user preferences, particularly among students. The overarching objective is to streamline and simplify the often arduous task of locating suitable accommodations by harnessing the potential of Collaborative Filtering. The deliberate selection of Collaborative Filtering as the cornerstone of this recommendation system stems from its proven efficacy in scrutinizing intricate user behavior patterns and deriving precise, tailored recommendations. Leveraging historical boarding house data, this methodology meticulously identifies patterns and similarities among users to offer suggestions finely aligned with their specific preferences. Integral to this research methodology is the concurrent utilization of Generative Adversarial Networks (GANs), serving a pivotal role in evaluating the system's accuracy. This dual-pronged approach, amalgamating Collaborative Filtering for recommendation generation and GANs for accuracy assessment, aims to ensure the system's efficacy in delivering precise, individualized suggestions. The findings of this study underscore a promising outcome – a system proficient in furnishing boarding house recommendations remarkably attuned to user preferences. This system's potential transcends the realm of student housing, presenting opportunities for broader applications across diverse fields requiring personalized recommendation systems. Crucially, the study's meticulous optimization of the GANs model, involving meticulous parameter adjustments including epoch count, optimizer selection (Adam), employment of mean absolute error (MAE) function, and fine-tuning a learning rate of 0.002, culminated in an outstanding achievement. The resultant MAE value of 0.0180 denotes minimal prediction errors, signifying estimations remarkably proximate to actual test data values, thus solidifying the system's reliability and precision. Ultimately, the successful development and evaluation of this boarding house recommendation system hold profound implications, promising to significantly enhance student experiences in discovering accommodations aligned with their preferences. Furthermore, this study's methodological approach paves the way for future research and wider applications in diverse domains seeking effective, personalized recommendation systems.]]>
