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[MATURITY CLASSIFICATION SYSTEM OF TOMATO BASED ON RGB COLOR FEATURES USING BACKPROPAGATION ARTIFICIAL NEURAL NETWORK METHOD ]]>
<![CDATA[Massie, Gary Jeremi]]>;
<![CDATA[Pratama, Azir Zuldani]]>;
<![CDATA[Sakira, Tiara Putri]]>;
<![CDATA[Kaswar, Andi Baso]]>;
<![CDATA[Andayani, Dyah Darma]]>
<![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.2024.5.1.732
<![CDATA[Determining the ripeness level of tomatoes, for now, is still done manually (conventional), and in general, determining the ripeness of tomatoes using the manual method often gets inconsistent results due to differences in everyone's perception so in determining ripe or not ripe tomatoes to be not very accurate. There have been various previous studies that have been conducted, especially in terms of classifying maturity levels, but from these studies, the level of accuracy achieved is relatively low. Therefore, the researcher proposes research on Tomato Fruit Maturity Classification System Based on RGB Color Features Using the Backpropagation Neural Network Method. This research consists of the image acquisition stage, the preprocessing stage, the image segmentation stage including performing morphological operations, the RGB feature extraction stage, and the last stage is conducting Image Classification using Backpropagation Neural Networks. From the results of the training phase, the resulting computational time is 87,735 seconds with an overall accuracy rate of 99.04%. And based on the results of the testing phase, the architecture of the backpropagation neural network that has been built can accurately classify the ripeness level of tomatoes, from a total of 90 test images, with an accuracy of 98.88% obtained and a more efficient computational time of 30.390 seconds. This can help farmers in harvesting tomatoes.]]>
<![CDATA[CLASSIFICATION OF THE LEVEL OF SUGAR CONTENT IN PAPAYA FRUIT BASED ON COLOR FEATURES USING ARTIFICIAL NEURAL NETWORK ]]>
<![CDATA[Nurfitri, Andi Aisyah]]>;
<![CDATA[Kaparang, Adam Indra]]>;
<![CDATA[Hidayat, Muh. Taufik]]>;
<![CDATA[Kaswar, Andi Baso]]>;
<![CDATA[Andayani, Dyah Darma]]>
<![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.2023.4.6.733
<![CDATA[Papaya (Carica papaya L) is consumed by many people because it is beneficial for health. Along with increasing consumption or enthusiasts of papaya, the quality of papaya needs to be considered. One of the determining factors of the quality of papaya is its physical characteristics, which can be seen from its color, shape, and texture. Papaya of good quality has a delicious and sweet taste. The sweet taste of papaya is certainly influenced by the sugar content contained in it. However, to determine the sugar content in papaya is only done by human assessment based on its physical characteristics, this assessment is often less accurate. With a system that can determine the sugar content in papaya, it will make it easier for farmers to sort papaya fruit. Therefore, in this study, it is proposed to classify the level of sugar content in papaya based on color features using an Artificial Neural Network. The proposed method consists of 5 stages, namely, image acquisition, preprocessing, segmentation with the Otsu method, morphological operations, and classification with artificial neural networks. The number of papaya datasets used is 300 images which are divided into 3 classes, low class, medium class, and tal class. Based on the results of the tests that have been carried out, an accuracy of 92.85% is obtained for the training data, and for the test data, an accuracy of 100% is obtained. These results indicate that the proposed method can classify the level of sugar content in papaya fruit accurately.]]>
<![CDATA[CLASSIFICATION OF RICE QUALITY LEVELS BASED ON COLOR AND SHAPE FEATURES USING ARTIFICIAL NEURAL NETWORK BASED ON DIGITAL IMAGE PROCESSING ]]>
<![CDATA[Asnidar, Asnidar]]>;
<![CDATA[Perdana, Am Akbar Mabrur]]>;
<![CDATA[Ilham, Muhammad Ryan]]>;
<![CDATA[Kaswar, Andi Baso]]>;
<![CDATA[Andayani, Dyah Darma]]>
<![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.2023.4.6.734
<![CDATA[Rice is the staple food of most Indonesians. In identifying the quality of rice, it can be seen from physical characteristics such as the color and shape of rice, because these characteristics can make an object can be identified properly and clearly. In general, what is done in determining the quality of rice by looking at its color and shape. But usually the human eye in identifying objects is sometimes less accurate which is influenced by several factors, such as age. So, several studies were conducted that tried to solve the problem by using digital image processing. However, the accuracy results obtained are still not accurate, because the datasets used in the previous study were relatively small, namely around 80 images, although the average level of accuracy obtained was quite high, but the number of datasets used was very small so that the level of accuracy was still inaccurate. Therefore, in this study, it is proposed that the title of classification of rice quality levels using JST based on digital image processing which divides rice into 3 classifications, namely, good, good enough, and not good where in this study using 330 digital images to produce a more accurate level of accuracy. In this study, there are several stages, namely, image retrieval, preprocessing, segmentation, morphological, feature extraction, and classification using artificial neural networks. Based on the research conducted, training accuracy was produced with an average accuracy of 98,75% while the test accuracy was produced with an average accuracy of 98,89%.]]>
<![CDATA[TRANSFER LEARNING IMPLEMENTATION ON IMAGE RECOGNITION OF INDONESIAN TRADITIONAL HOUSES ]]>
<![CDATA[Firmansah, R Arif]]>;
<![CDATA[Santoso, Handri]]>;
<![CDATA[Anwar, Agus]]>
<![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.2023.4.6.767
<![CDATA[Indonesia is the largest archipelago in the world that has cultural diversity, one of Indonesia's cultural wealth is the architectural uniqueness of the types of traditional houses that come from different tribes and regions. in this era of digitalization, the younger generation of this nation must continue to preserve cultural wealth, one of which is by building a system that can document and provide learning about image recognition of the archipelago's traditional houses. Thanks to Artificial Intelligence Technology, it is possible to create a smart model that functions as an image recognition with system learning by working with a neural network called deep learning, which is supported by a transfer learning algorithm that can utilize previous models that have been trained, one of which is the MobileNetV2, Resnet50, VGG16 and Xception models as an effort to get a model with high accuracy with limited dataset conditions. So, the purpose as well as the update of this research is to build an image recognition model of Indonesian traditional houses with the transfer learning method. The methods and stages used are CRISP-DM (Cross Industry Standard Process for Data Mining), a standard used to build applications that aim to gain insight from a dataset, the image dataset used in this study was created with the image scraper technique from the internet. The conclusion of this research is that an image recognition model of Indonesian traditional houses is produced by training experiments from 5 transfer learning models that have been determined and the greatest accuracy is obtained, namely 0.96% of the MobileNetV2 transfer training method, the potential for further development for future research is to add more classes and amount of data and design a more detailed and detailed deployment model.]]>
<![CDATA[EFFECTIVENESS OF SECURITY THROUGH OBSCURITY METHODS TO AVOID WEB APPLICATION VULNERABILITY SCANNERS ]]>
<![CDATA[Kurniawan, Azis]]>;
<![CDATA[Ramli , Kalamullah]]>
<![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.2023.4.6.778
<![CDATA[The concept of security through obscurity is not recommended by the National Institute of Standards and Technology (NIST) as a form of system security. Basically this concept hides assets as difficult as possible so that it is not easy for attackers to find them, so that it can be used to avoid vulnerability scanner applications that are widely used by attackers to find out web system weaknesses. This research was conducted by modifying the web application firewall (WAF) and testing using the SQLMap and OWASP Zed Attack Proxy (ZAP) vulnerability scanner applications. The results of the study show that SQLMap takes up to 1238 times longer to complete a scan on a modified web application firewall than without modification, while OWASP ZAP cannot complete a scan on the same treatment. Thus the concept of security through obscurity can be applied to web security to extend vulnerability scanning time.]]>
<![CDATA[CYBERBULLYING DETECTION ON TWITTER USES THE SUPPORT VECTOR MACHINE METHOD ]]>
<![CDATA[Kusuma, Bayu Indra]]>;
<![CDATA[Aryo Nugroho]]>
<![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.2024.5.1.809
<![CDATA[Social media is a platform that provides facilities for users to engage in various social activities. However, the increasing popularity of social media in the modern era also cannot be separated from the occurrence of several negative impacts, one of which is cyberbullying. Cyberbullying is an action that is done online that can harm the mental and emotional condition of an individual. To reduce this problem, this research aims to investigate the performance of the C-SVC and Nu-SVC algorithms from the Support Vector Machine method in classifying cyberbullying sentences. The data used is comments data from the @puanmaharani_ri account on Twitter, which was collected from September 25, 2020, to September 29, 2022, totaling 5,000 data. After the data is collected, it is labeled and preprocessed, and then the data will be weighted using the TF-IDF method. The result of the TF-IDF will be displayed in the form of a word cloud. Next, the Support Vector Machine method will classify cyberbullying sentences using several percentages split combinations such as 60%, 70%, 80%, and 90%. The test results show that the C-SVC method has the highest accuracy of 79.6% at a 70% percentage split, while Nu-SVC has the highest accuracy of 78.9% at a 60% percentage split. From these results, it can be concluded that the Support Vector Machine method with the C-SVC algorithm provides better results than Nu-SVC in classifying cyberbullying sentences.]]>
<![CDATA[COMPARISON OF LOGISTIC REGRESSION, MULTINOMIALNB, SVM, AND K-NN METHODS ON SENTIMENT ANALYSIS OF GOJEK APP REVIEWS ON THE GOOGLE PLAY STORE ]]>
<![CDATA[Maulana, Audenza]]>;
<![CDATA[Inayah Khasnaputri Afifah]]>;
<![CDATA[Asghafi Mubarrak]]>;
<![CDATA[Kiagus Rachmat Fauzan]]>;
<![CDATA[Ardhan Dwintara]]>;
<![CDATA[Zen, Bita Parga]]>
<![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.2023.4.6.863
<![CDATA[Today's community activities cannot be separated from the name of transportation because it makes it very easy for people's daily activities. With the existence of transportation, people can more quickly reach their destination. With the Gojek application on the Google Play Store, it will help people travel or deliver goods. To determine service quality, sentiment analysis can be used to classify reviews. The purpose of this study is to compare which method has the best accuracy, so that it can classify reviews into positive or negative sentiments. The methods tested in this study are Logistic Regression, MultinomialNB, SVM, and K-NN. Performance assessment methods include score accuracy, recall, and precision, classification reports, and confusion matrix to determine the appropriate method for classifying reviews into positive or negative categories. Of the four methods tested, the one with the highest performance is the Logistic Regression method. Accuracy, recall and precision scores of the Logistic Regression method were 82.45%, 82.49%, 82.45% and 82.43%, respectively. Classification report also shows good results. In the confusion matrix, there are 111 and 124 True positives and True negatives. There are only 22 and 28 False positive and False negative results respectively. The method that has the lowest score is K-NN, with score accuracy, recall, and precision respectively were 52.28%, 59.43%, 93, 52%, and 65.65%. Classification report shows quite bad results. In the connection matrix, it produces True positives and True negatives 130 and 19. There are only 127 and 9 False positive and False negative results respectively. The results of this study state that using the Logistic Regression method is suitable for use in classifying positive and negative reviews in the review dataset on the Gojek application on the Google Play Store.]]>
<![CDATA[CLOTHING RECOMMENDATION AND FACE SWAP MODEL BASED ON VGG16, AUTOENCODER, AND FACIAL LANDMARK POINTS ]]>
<![CDATA[Ramadhanti, Imada]]>;
<![CDATA[Prasetiadi, Agi]]>;
<![CDATA[Kresna A, Iqsyahiro]]>
<![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.2024.5.1.1016
<![CDATA[The selection of clothes in e-commerce sometimes contains doubts about the clothes that consumers choose because the clothes are not yet known to suit the consumer's body. So this research provides a solution through a clothing recommendation model according to the size and concept of clothing. Furthermore, there is a face exchange model whose job is to exchange faces between the consumer's face and the face on the recommended clothing. The dataset used in the classification model is clothing that is put into 8 classes with variations in size, clothing concept, and veiled or without headscarves, while making the autoencoder model requires source and target face datasets of 3,000 faces each. The method used to make clothing model recommendations is VGG16 and the face exchange model uses the autoencoder and facial landmark points methods. The results of the classification model with 2 different architectures obtain an accuracy of 97.01% and 94.49% respectively. Then the results of the autoencoder models for the 12 models produced the lowest loss values with autoencoder I of 0.00012951 and in autoencoder II of 8.01e-05. The face landmark point method is used if the autoencoder method does not produce a good face swap.]]>
<![CDATA[ENTERPRISE ARCHITECTURE WITH TOGAF IN THE INDONESIAN FOOTWEAR INDUSTRY, CASE STUDY AT XYZ FOOTWEAR INDUSTRY ]]>
<![CDATA[Vinardo]]>;
<![CDATA[Dazki, Erick]]>;
<![CDATA[Eko Indrajit, Richardus]]>
<![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.2023.4.6.1017
<![CDATA[The footwear industry is one of the industries that absorbs a lot of labor force, and automatically this industry has a great impact on the economic growth of the local community. The competition in the footwear industry involves global competition with footwear industries from all over the world. Companies that can provide good quality and efficient costs will be the choice for production places for various world footwear brands. The use of appropriate and integrated information technology that covers all processes, both internal and external company processes, will increase the operational effectiveness of the company. The design of this information technology utilization is built by adopting the TOGAF framework. TOGAF is a standard methodology and framework for enterprise architecture that has been widely used by leading organizations in the world to improve their business effectiveness and efficiency by utilizing information technology. This journal will provide design and implementation of TOGAF standards in the footwear industry, with study case at XYZ footwear industry to deliver enterprise architecture for footwear industry in Indonesia, so they can improve competitiveness among other footwear industry globally.]]>
<![CDATA[COMPARISON OF NAIVE BAYES, DECISION TREE, AND RANDOM FOREST ALGORITHMS IN CLASSIFYING LEARNING STYLES OF UNIVERSITAS KRISTEN INDONESIA TORAJA STUDENTS ]]>
<![CDATA[Garonga, Melki]]>;
<![CDATA[Rita Tanduk]]>
<![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.2023.4.6.1020
<![CDATA[Learning style is an individual's habit or way of absorbing, processing, and managing information. This factor is very important in achieving learning goals. However, in reality, learning styles are often overlooked in the learning process, which can lead to suboptimal absorption of lessons and affect the quality of education. Various models have been developed by educational experts to identify students' learning styles, one of which is the VAK model (Visualization Auditory Kinesthetic) for grouping learning styles. This study compares algorithms in classifying learning styles using the VAK model. The results showed that the most dominant learning style was kinesthetic with a percentage of 46.9% or 478 students. The algorithm modeling showed that Naive Bayes had the highest accuracy with a value of 75%, while Random Forest had the lowest accuracy with a value of 59%. This suggests that Naive Bayes is more suitable for classifying students' learning styles. In conclusion, understanding students' learning styles is crucial for effective education. The VAK model is one way to identify learning styles, and Naive Bayes is a suitable algorithm for classifying students' learning styles. By considering learning styles, educators can tailor their teaching methods to better suit their students' needs and improve the quality of education.]]>
