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.
Articles
962 Documents
<![CDATA[HARDWARE DESIGN OF THE TOUCHLESS HAND CODE AND CONVOLUTIONAL NEURAL NETWORKS - BASED AUTOMATIC DOOR SECURITY SYSTEM ]]>
<![CDATA[Prihanto, Surya]]>;
<![CDATA[Effendy, Nazrul]]>;
<![CDATA[Nopriadi, Nopriadi]]>
<![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.2023.4.6.1117
<![CDATA[The spread of viruses and bacteria through touching door surfaces is essential in maintaining public hygiene and health. In this context, a hand-coded touchless automatic door hardware design has been developed to reduce the spread of diseases through touch. This research aims to create a plan that includes interface development and hardware design to open and close doors automatically without contact. In this research, the automatic door hardware response is tested based on the numeric input from the hand code represented by the numeric database. The input and output control is connected to Python's graphical user interface (GUI). The GUI system design involves tools to connect the Python programming language and the Arduino microcontroller. Based on the experimental results, the hardware design of the automatic door security system based on hand code and Convolutional Neural Networks functions appropriately.]]>
<![CDATA[IMPLEMENTATION OF LSTM (LONG SHORT TERM MEMORY) ALGORITHM TO PREDICT WEATHER IN CENTRAL JAVA ]]>
<![CDATA[Irwan, Rhedy]]>;
<![CDATA[Andono, Pulung Nurtantio]]>;
<![CDATA[Al Zami, Farrikh]]>;
<![CDATA[Ocky Saputra, Filmada]]>;
<![CDATA[Megantara, Rama Aria]]>;
<![CDATA[Handoko, L. Budi]]>;
<![CDATA[Umam, Chaerul]]>
<![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.2023.4.6.1118
<![CDATA[Agro-indutrial agricultural production such as red onions in Indonesia has a very important share in driving Indonesia's economic growth, especially in Central Java province which contributed 28.15% of the total national red onion production in 2021. Weather conditions have a major influence on the red onion planting process until the red onions are ready to be harvested. In this study, the objective is to predict various types of weather such as rainfall, air temperature, and air humidity in seven districts in Central Java, namely Brebes, Temanggung, Demak, Boyolali, Kendal, Pati, and Tegal. To do this, the use of the LSTM (Long Short Term Memory) algorithm with its ability to store memory longer than RNN will be reliable for predicting various types of weather in the future. This research was developed with the CRISP-DM (Cross Industry Process Model for Data Mining) method which has a goal-oriented approach, this method is a mature and widely accepted method in Data Mining with various applications in Machine Learning. With the final results from 39 models by using the evaluation of the average value of train MSE 0.013, test RMSE 0.11, test MSE of 0.02, test RMSE 0.12 and succeed to predict 5 days or months ahead from the last data that is provided.]]>
<![CDATA[COMPARISON OF LEAST SQUARE AND QUADRATIC METHODS ON PREDICTION THE NUMBER OF NEW STUDENT APPLICANTS ]]>
<![CDATA[Atin Hasanah]]>;
<![CDATA[Kusrini, Kusrini]]>;
<![CDATA[Kusnawi, Kusnawi]]>
<![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.2023.4.6.1124
<![CDATA[New student registration is held every year with several mechanisms. However, in recent years the number of applicants has decreased even though it had experienced a surge in the previous year. So that, it is necessary to have a prediction to predict the number of applicants in the coming year. In addition, the results of these predictions can be used as material for consideration in determining the quota/ceiling for the number of new student admissions in the following academic year. This research used the Least Square and Quadratic methods to predict the number of new student applicants based on data on the number of applicants from the 2014/2015 to 2022/2023 academic years. Performance testing of the two methods was tested with three (3) testing methods : MAE, MAPE, and MSE. The performance test found that the Quadratic method is more suitable with the MAPE value in the "Good" forecasting accuracy category, which is 11%. For the MAE value, it gets 452,17 and an MSE of 302069,04. While Least Square produces a MAPE value in the "Enough" forecasting accuracy category of 30%, for the MAE value, it gets 996,97 and an MSE of 1494205,36.]]>
<![CDATA[IMPLEMENTATION OF RANDOM FOREST AND SMOTE METHODS FOR ECONOMIC STATUS CLASSIFICATION IN CIREBON CITY ]]>
<![CDATA[Sholihah, Neneng Nur]]>;
<![CDATA[Hermawan, Arief]]>
<![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.2023.4.6.1135
<![CDATA[The Indonesian government has tried various methods to eradicate poverty throughout the country, one of which is the fair distribution of social assistance to households based on their economic status classification. The determination of social assistance recipients can be influenced by political factors or personal relationships and can be misused, leading to assistance being given to individuals with specific political connections or support. This research aims to develop a household economic status classification system in Cirebon City using the Random Forest algorithm to address these issues. The data used in this research experienced an imbalance in the number of class instances, where the high-class instances were much fewer than the low and medium classes. To address this, the Synthetic Minority Oversampling Technique (SMOTE) was employed. In this study, various testing scenarios were conducted to obtain the best model for accurately predicting household economic status. Based on the research results, the best testing was achieved using Random Forest and SMOTE with a random state of 0, obtaining an accuracy of 93% and excellent performance in classifying each class. When testing unlabeled data, Random Forest successfully predicted 24 out of 30 actual data, resulting in an accuracy rate of 80%. Although this accuracy is lower than what was achieved by Random Forest and SMOTE with a random state of 0, it can be said that the application successfully classifies household economic status in Cirebon City effectively.]]>
<![CDATA[STORAGE SERVER DATABASE UTILIZATION FORECASTING USING HOLT-WINTERS AND ARIMA METHODS IN E-GOVERNMENT SYSTEM. STUDY AT KEMENKEU RI ]]>
<![CDATA[Adinda Krida Wicaksono]]>;
<![CDATA[Teguh Prasetyo]]>;
<![CDATA[Nazori Az]]>
<![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.2023.4.6.1147
<![CDATA[Storage is a storage medium which is an important part of the system infrastructure. Storage utilization is one of the variables in determining the needs and performance of a system. If the storage is lacking or depleted it will cause the system to be hampered and the possibility of interference occurring. There were eight complaints or reports of system disturbances recorded in the Ministry of Finance's ticketing service desk application caused by anomalies in storage or disk capacity. Things like this can be anticipated if we know the storage capacity requirements for the future. In compiling ICT capacity analysis at Pusintek there has been no use of past data to plan future storage requirements. The use of data mining algorithms can be used to obtain forecast values from storage utilization. The use of the ARIMA model and Holt-Winters Exponential Smoothing as a method used to predict inventory is considered to be a fairly accurate model. Both algorithms are used to compare which accuracy is better in predicting storage requirements by measuring RMSE. Research data was obtained from monthly utilization reports from January 2021 to December 2022. From the evaluation results it can be concluded that forecasting storage server database utilization using the Holt-Winters method is better than the ARIMA method with the RMSE results for the Holt-Winters method being 14332.661717740748 and the RMSE method ARIMA is 20498.977982137938. The results of this forecasting can be utilized in planning database server storage needs.]]>
<![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[OPTIMIZATION OF DISEASE PREDICTION ACCURACY THROUGH ARTIFICIAL NEURAL NETWORK (ANN) ALGORITHMS IN DIAGNESE APPLICATION ]]>
<![CDATA[Faurina, Ruvita]]>;
<![CDATA[Gazali, M. Jumli]]>;
<![CDATA[Herani, Icha Dwi Aprilia]]>
<![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.2024.5.2.1182
<![CDATA[This research aims to enhance the accuracy and speed of diagnoses in the Diagnese application by implementing the ANN algorithm for disease prediction. The dataset used for experimentation was featuring binary data types, containing 131 symptoms used to predict 41 types of diseases. The Diagnese application assists patients in identifying diseases and finding suitable specialist doctors based on reported symptoms. To achieve this goal, researchers explored various machine learning algorithms, such as decision trees, SVM, Random Forest, Logistic Regression, and ANN. Through comprehensive analysis, the ANN algorithm outperforms other algorithms and showcases the best performance. The research results demonstrate that integrating this application can significantly improve diagnostic accuracy and speed, thereby potentially reducing treatment delays and enhancing patient health outcomes. The neural network model displayed exceptional accuracy across training, validation, and testing datasets, scoring 97%, 99%, and 95%, respectively. Overall, this study showcases the potential of implementing the ANN algorithm within Diagnese applications to elevate the accuracy and efficiency of disease diagnosis. The application of this model is expected to augment the efficiency and precision of the medical diagnosis process, enabling doctors to make more accurate decisions and provide more effective patient care.]]>
