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[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[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[POLAK-RIBIERE CONJUGATE GRADIENT ALGORITHM IN PREDICTING THE PERCENTAGE OF OPEN UNEMPLOYMENT IN NORTH SUMATRA PROVINCE ]]>
<![CDATA[Amalya, Nanda]]>;
<![CDATA[Solikhun, Solikhun]]>
<![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.2024.5.1.1047
<![CDATA[The economic problem that has a direct impact on human life and welfare is unemployment. One of the cities in Indonesia with the highest unemployment rate is North Sumatra Province. For example, Tebing Tinggi City had the highest unemployment rate of 9.73% in 2017, while Nias Selatan had the lowest percentage of 0.31%. This research is important to do in order to anticipate the unemployment rate in North Sumatra for any party, be it the government or the private sector, so that they can work together to overcome the problem of unemployment in the future which is the main problem in the economy. For example, the government creates programs to help reduce the number of unemployed, provide preparation or do other things, helping people to become more imaginative and have skills so they can compete in the world market. Predicting unemployment has been the subject of many studies, one of which is by utilizing artificial neural networks. This study aims to predict the percentage of unemployed in North Sumatra from 2022 to 2026, using the Backpropagation Neural Network Algorithm, the Conjugate Gradient Polak-Ribiere method and Matlab version 2011 for research and data analysis. This research utilizes open action rate stimulation data for the population of North Sumatra based on residents aged over 15 years from 2017 to 2021. Using five architectural models, namely: 4-50-1, 4-55-1, 4-70- 1, 4-75-1, and 4-77-1. The final results were obtained using the most accurate architectural model, namely model 4-75-1 which has a Mean Squared Error (MSE) of 0.0000004288 and an accuracy rate of 100% with a time of 00.09 at epoch 452.]]>
<![CDATA[USER INTERFACE DISPLAY DESIGN TO ASSIST FOOD WASTE MANAGEMENT USING THE USER CENTERED DESIGN METHOD ]]>
<![CDATA[Kamarulredzuan, Moch Baiz]]>;
<![CDATA[Setiawan, Dadang]]>;
<![CDATA[Sulistiyo Kusumo, Dana]]>
<![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.2024.5.1.1115
<![CDATA[The designed user interface is mobile-based which aims to help manage food waste, especially for Bandung area students. The user interface display contains education about food waste, food waste banks and food donations. It is hoped that the user interface display created can help in managing food waste. Food waste in Indonesia is very concerning, for example in the city of Bandung there are 772.69 M3/day, which is 44.51% of the total of various types of food waste in 2021. Therefore a solution is needed that can help manage food waste. By utilizing technology, we can provide education about waste using a smartphone. Then a user interface display design is made using the user centered design method. The user centered design method is needed because it has an interface design process that focuses on usability goals. This method begins with collecting user information through questionnaires, then analyzing user information and obtaining user needs, then creating user flows, wireframes, mockups, and prototypes. After that, a usability test is carried out using the system usability scale method to see whether the user interface display that is made can meet user needs. The result of the system usability scale score is 87,5 and is in the excellent category, indicating that the user interface is good.]]>
<![CDATA[GENERATIVE ADVERSARIAL NETWORKS FOR ANTERIOR CRUCIATE LIGAMENT INJURY DETECTION ]]>
<![CDATA[Mulyani, Sri Hasta]]>;
<![CDATA[Diqi, Mohammad]]>;
<![CDATA[Salsabil, Husna Arwa]]>
<![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.2024.5.1.1150
<![CDATA[This research explores the application of Generative Adversarial Networks (GANs) for detecting and classifying Anterior Cruciate Ligament (ACL) injuries using MRI images. The study utilized a dataset of 917 MRI images, each labeled as healthy, partially injured, or completely ruptured, to train the model. The performance of the GAN model was evaluated using a confusion matrix and a classification report, yielding an overall accuracy of 92%. The model demonstrated high proficiency in identifying healthy ACLs and partially injured ACLs but encountered some challenges in accurately identifying completely ruptured ACLs. Despite this, the results suggest that machine learning techniques, particularly GANs, have significant potential for enhancing the accuracy and efficiency of ACL injury detection. The ability of the model to distinguish between different degrees of injury could potentially aid in treatment planning. However, the study also underscores the need for further refinement of the model, particularly in improving its sensitivity in detecting severe ACL injuries. This research highlights the potential of machine learning in medical imaging and provides a solid foundation for future research in ACL injury detection and classification.]]>
<![CDATA[DIGITAL IMAGE CLASSIFICATION OF HERBAL LEAVES USING KNN AND CNN WITH GLCM FEATURES ]]>
<![CDATA[Zahirah, Dinna]]>;
<![CDATA[Purnawansyah, Purnawansyah]]>;
<![CDATA[Kurniati, Nia]]>;
<![CDATA[Darwis, Herdianti]]>
<![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.2024.5.1.1162
<![CDATA[Geographical position and having a tropical climate make Indonesia known for its abundant biodiversity, one of which is herbal leaves. Indonesia has more than 2039 species that fall into the category of herbal medicinal plants. Herbal leaves are plants that are used as an alternative to natural disease healing. The large number of herbal leaf plants makes it difficult for people to distinguish between herbal plants and non-herbal plants, except when herbal leaf plants bear fruit or bloom. With advances in technology, many studies have been conducted to identify types of herbal plants, one of which is to identify the characteristics of the leaves. In this study, image recognition of herbal leaves was carried out using the K-Nearest Neighbor and Convolutional Neural Network methods with feature extraction of the Gray Level Co-occurance Matrix. By using these 2 methods, the data collected in this study were 480 leaf images which were then divided into 80% testing data and 20% training data. The data used are in the form of Sauropus androgynus and Moringa leaves. Based on the test results, the Convolutional Neural Network method which is suggested in the herbal leaf image classification which has an accuracy value of 96%..]]>
<![CDATA[IMPLEMENTATION OF LOAD BALANCING WITH PER CONNECTION CLASSIFIER AND FAILOVER AND UTILIZATION OF TELEGRAM BOT (CASE STUDY : PT TUJUH MEDIA ANGKASA) ]]>
<![CDATA[Ariya Pramudita]]>;
<![CDATA[Rushendra, Rushendra]]>
<![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.2024.5.1.1165
<![CDATA[For customers of PT. Seven Media Angkasa, which is engaged in providing fast online shopping services in Indonesia, definitely needs stable internet to process requests from customers. Even though it already has 2 ISPs, sometimes there are frequent downtimes which will disrupt the service process for customers who want to shop. In this case, one of the Load Balancing methods is the Per Connection Classifier (PCC) which is able to specify a packet to the gateway of a particular connection. Failover for backing up The weakness of the PCC method is Failover which can switch automatically if one of the systems fails so that it becomes a backup for the system that has failed. Added Telegram Bot as a DHCP Alert which can detect if there is a DHCP Rouge. By using the PCC method, it is able to maximize bandwidth usage and minimize the occurrence of downtime in sending or receiving data. So with the addition of the Failover method, if there is a temporary delay when many incoming requests can interfere with performance, Failover can move manually or automatically if one of the systems fails so that it becomes a backup for a failed system. If gateway 1 is disconnected, the backup gateway will replace gateway 1. If gateway 1 returns to normal, the connection path is used again to become gateway 1. Likewise with gateway 2 when it is disconnected. From testing on the speedtest.net tools, it was found that the Load Balancing applied was able to combine 2 ISPs into one, namely Download to 19.18 Mbps and Upload to 18.47 Mbps. The Telegram Bot is able to send notifications when there is a counter DHCP Server with the contents of the message successfully getting a Mac Address or unknown server from the counter DHCP server, namely DC: 2C: 6E: 81: CF: 34.]]>
<![CDATA[PERSONALITY DETECTION ON TWITTER USER USING XGBOOST ALGORITHM ]]>
<![CDATA[Adinda Putri Rosyadi]]>;
<![CDATA[Warih Maharani]]>;
<![CDATA[Prati Hutari Gani]]>
<![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.2024.5.1.1166
<![CDATA[Personality is a person's identity that is addressed to the public. The Big Five personality is the most commonly used personality model. Detecting a person's personality is still a difficult task today. Because personality detection still often requires humans to fill out lengthy questionnaires to evaluate various personality traits. Therefore, a system that is able to identify personality easily and specifically is needed. By using social media, individuals often express their feelings. Twitter is the most popular social networking platform today. In this research, we use the XGBoost Algorithm, a powerful machine learning method, to create a personality detection system that improves upon existing approaches. Our research aims to determine how well the XGBoost algorithm can recognize Big Five personality features in Twitter users. We achieved encouraging results through in-depth investigation and experimentation. The XGBoost algorithm successfully developed a model that can recognize all Big Five personality trait labels but with different precision, recall and f1-score values. The highest value was obtained for the Extroversion label with a precision of 0.92, recall of 1.00 and f1-score of 0.96. Meanwhile, the lowest value is owned by the Agreeableness label with a precision value of 0.29, recall 0.29, and f1-score of 0.29. This research demonstrates the potential of the XGBoost Algorithm for personality discovery on social media platforms, providing a fast and accurate method to identify distinctive characteristics. Overall, the results of this study demonstrate the efficiency of the XGBoost Algorithm in the context of personality recognition, opening the door for further development in understanding and evaluating human behavior through social media platforms such as Twitter.]]>
<![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.]]>
