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[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.]]>
<![CDATA[PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING ]]>
<![CDATA[Nurhadi, Nurhadi]]>;
<![CDATA[Winanto, Eko Arip]]>;
<![CDATA[Said, Rahaini Mohd]]>;
<![CDATA[Jasmir, Jasmir]]>;
<![CDATA[Afuan, Lasmedi]]>
<![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.2024.5.2.1228
<![CDATA[Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.]]>
<![CDATA[ANALYSIS AND IMPLEMENTATION OF YOLOV7 IN DETECTING PIN DEL IN REAL-TIME ]]>
<![CDATA[Iustisia Natalia Simbolon]]>;
<![CDATA[Lumbanraja, Daniel Fernandez]]>;
<![CDATA[Tampubolon, Kristina]]>
<![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.2024.5.2.1286
<![CDATA[Real-time object detection is the process of identifying and tracking objects instantly and directly without any delay between image input and output. Carrying out real-time detection is a challenge in detection systems because it requires speed and accuracy of detection. This research proposes the application of the YOLOv7 algorithm which allows object localization and classification in one stage. This detection is carried out in real time on two objects, namely PinDel and Students. This research focuses on applying the YOLOv7 algorithm to detect real-time use of Pin Del by students. In this research, several hyperparameters were adjusted until the optimal value was found, including epoch with a value of 300, as well as confidence threshold, and IoU threshold with a value of 0.5. The model evaluation results from hyperparameter experiments show good results, with precision of 0.946, recall of 0.959, and mAP@0.5 of 0.977. This research has succeeded in detecting Pin Del objects in real time by obtaining a detection speed of between 7 and 40 FPS, which shows a fast response in detecting objects in real time. This research has contributed to the development of real-time object detection technology and its application in Pin Del use cases by students.]]>
<![CDATA[RECOMMENDATION SYSTEM TO SELECT A MAJOR OF VOCATIONAL SCHOOL USING DECISION TREE ]]>
<![CDATA[Ardiansyah Risko Anwari]]>;
<![CDATA[Sukirman, Sukirman]]>
<![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.2024.5.2.1327
<![CDATA[A recommendation system is a tool that can be used to provide suggestions to users about something they are interested in, such as products, content, music, movies, or even majors at school. When registering for majors at vocational high school (SMK), some students sometimes difficult to select major based on their interests and abilities. This study aims to develop a recommendation system to select major in SMK, so that it can help prospective students choose majors according to their abilities. The method used is Research and Development (R&D), using the waterfall development model which consists of several stages, namely requirements analysis, system design, design implementation, and system testing. The algorithm used to recommend choices is a decision tree, a predictive model that maps input data to output targets based on a series of decisions or separation rules. The parameters used to recommend the selection of majors are the value data of last year's applicants. The evaluation was carried out using the system usability scale (SUS) involving 25 participants (17 males and 8 females) aged from 14 to 16 years old. Based on the analysis carried out, the results showed that SUS score is 89.7, which means that included to the excellent category in measuring adjective ranges, and acceptable in the acceptability scale. Thus it can be concluded that this department recommendation system is usable or can be used to provide advice to students in selecting a major in SMK.]]>
<![CDATA[DOCKER-BASED MONOLITHIC AND MICROSERVICES ARCHITECTURE PERFORMANCE COMPARISON ]]>
<![CDATA[Panji Dirgantara, Deni]]>;
<![CDATA[Dana Sulistyo Kusumo]]>;
<![CDATA[Rio Guntur Utomo]]>
<![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.2024.5.2.1338
<![CDATA[Most developers still use the monolithic architecture, where all components of an application are combined into one integrated system, so each part depends on other components. The monolithic architecture has weaknesses, such as when a failure occurs in one component, all parts cannot be executed because each component relies on one other component. Microservices can be a solution to this, considering that in the microservices architecture, each element or service is created and put separately, so when a failure occurs in one component, other components will not be affected and can still run normally. This research aims to determine the implementation and performance comparison between monolithic architecture and microservices Architecture in the Agreeculture Market web app. Agreeculture Market is a web application that aims to facilitate the transaction process of agricultural commodities and make it easier for agricultural commodity producers to market their products. The measurement method used to measure the performance of both architectures is load testing using JMeter and performance tools from task manager and comparing the response time, throughput, disk usage, CPU usage, and memory usage of both used architectures. With two measurement schemes with Docker and without Docker, the result of this research is a performance comparison between the two architectures, where the backend application Agreeculture Market, which uses microservices architecture with Docker and API gateway, performs better than the monolithic architecture version. Conversely, the monolithic architecture performs better than the microservices architecture in the scheme without Docker and API gateway.]]>
<![CDATA[DETECTION OF VEHICLE TYPE AND LICENSE PLATE WITH CONVOLUTIONAL NEURAL NETWORK MODEL YOLOV7 ]]>
<![CDATA[Suhartono, Suhartono]]>;
<![CDATA[Zain, Satria Gunawan]]>;
<![CDATA[Ardilla, Andi]]>
<![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.2024.5.2.1430
<![CDATA[This research was conducted in response to issues related to the efficiency and effectiveness of vehicle type and license plate detection. The increasingly congested traffic conditions and the expanding use of motor vehicles have posed challenges in traffic monitoring and regulation. Therefore, there is a need to develop a solution that can save time and resources while providing more comprehensive information in vehicle monitoring. This research implements the Convolutional Neural Network (CNN) algorithm with the latest YOLOv7 model from YOLO to detect vehicle types and vehicle number plates simultaneously to make it more efficient and effective, save time and resources, and provide more complete information. The research method used is Research and Development (R&D) with an experimental approach. The stages include image acquisition, labeling, dataset sharing, YOLOv7 model training, testing, prediction results, and conversion to text using Optical Character Recognition (OCR). The research results show that the ResNet34 model architecture achieves a total accuracy of 89.7% for 3x3 convolution layers and 88.6% for 5x5 convolution layers. The YOLOv5 architecture performs well on 3x3 convolution layers with an overall accuracy of 71.9%, and 58.3% for 5x5 convolution layers. However, the YOLOv7 and Mobilenet architectures tend to have lower accuracy, namely the Mobilenet architecture with a 3x3 convolution layer with a total accuracy of 63.4%, and 73.4% for the 5x5 convolution layer. Computing speed is also considered, with YOLOv5 and YOLOv7 having higher speeds than ResNet34 and Mobilenet. Tests were carried out in various lighting conditions, resulting in accurate detection of vehicle types and vehicle number plates of 90% in the morning, 85% in the afternoon and 77% at night. Overall, the system succeeded in recognizing objects with an accuracy of 84% from a total of 720 data tested, but the accuracy of converting vehicle number plates using OCR reached 22%. The results of this research demonstrate the performance and effectiveness of the YOLOv7 algorithm in detecting vehicle types and vehicle number plates, as well as providing insight into accuracy in various lighting conditions and OCR conversion.]]>
<![CDATA[A ROBUST AND IMPERCEPTIBLE FOR DIGITAL IMAGE ENCRYPTION USING CHACHA20 ]]>
<![CDATA[Nugroho, Widhi Bagus]]>;
<![CDATA[Susanto, Ajib]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Doheir, Mohamed]]>
<![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.2024.5.2.1470
<![CDATA[In the current era, data security is mandatory because it protects our personal data from being used by irresponsible people. The objective of this research is to show the robustness of the method we propose to encrypt images using the chacha20 algorithm which is included in the symmetric encryption cryptography technique and uses one key for both encryption and decryption processes. we use the encryption method by reading the bits from a digital image which is processed using the chacha20 algorithm to get the results of the digital image encryption. The results of this study indicate that the Chacha20 algorithm is secure to use when encrypting and decrypting digital images. The average MSE value generated by the chacha20 algorithm is 0.1232. The average PSNR value is 57.4784. The average value of UACI is 49.99%. The average value of NPCR is 99.602%. The test values were acquired by executing encryption and decryption processes on 5 distinct colour digital images with different size. Additionally, this study displays histograms for the original digital image, as well as for the encrypted and decrypted digital images, illustrating the pixel distribution in each. The histogram also serves as material for analysis of the success of the encryption and decryption processes in digital images.]]>
<![CDATA[OPTIMIZING RAW MATERIAL INVENTORY MANAGEMENT OF MSME PRODUCT USING EXTREME GRADIENT BOOSTING (XGBOOST) REGRESSOR ALGORITHM: A SALES PREDICTION APPROACH ]]>
<![CDATA[Muhammad Khusni Fikri]]>;
<![CDATA[Farrikh Al Zami]]>;
<![CDATA[Ika Novita Dewi]]>;
<![CDATA[Abu Salam]]>;
<![CDATA[Ifan Rizqa]]>;
<![CDATA[Mila Sartika]]>;
<![CDATA[Diana Aqmala]]>
<![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.2024.5.2.1487
<![CDATA[Micro, Small and Medium Enterprises or MSMEs have a very important role for the survival of the economic sector in Indonesia. However, as the development of MSMEs, followed by a series of problems that arise. One of them is the problem of sales, business people have difficulty in determining the number of product sales in the future so that there is often an accumulation of raw materials or unsold products. This study aims to help MSMEs optimize raw material management by predicting product sales using the XGBoost Regressor Algorithm. Recently, the algorithm is very famous in the competition because of its reliability and no one has applied it to predict MSME product sales. Based on several other studies, this algorithm is accurate in predicting a value, such as predicting stock prices and the number of accidents in Bali, Indonesia. This research uses historical product sales data and weather data consisting of air temperature and relative humidity in Semarang Indonesia to train and evaluate the performance of the model. The prediction model was performed with predetermined variables and resulted in MAE 3.0752730568649156, MSE 38.25842541629838, and RMSE 6.185339555456788. In the end, it is concluded that the model built with XGBoost Regressor has a low error rate so that it can accurately predict the sales of an MSME product and optimize the management of raw materials for related products.]]>
<![CDATA[CUSTOMER LOYALTY SEGMENTATION IN ONLINE STORE USING LRFM AND MLRFM IN COMBINATION WITH RM K-MEANS ALGORITHM ]]>
<![CDATA[Utomo, Angelina Caroline]]>;
<![CDATA[Handojo, Andreas]]>;
<![CDATA[Octavia, Tanti]]>
<![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.2024.5.2.1497
<![CDATA[The rapid development of online business in recent years has driven Store X to embark on a digital transformation. By the end of 2020, Store X relocate their conventional business to online business. The greatest obstacle and key to success for online business operators, such as Store X, is gaining and retaining consumer loyalty in the face of an increasing number of competitors. Therefore, the company must be able to identify the character (behavior) of its clients to provide appropriate treatment. Each customer's behavior is unique, which means they must all be treated differently. However, all this time, Online Store X has provided the same treatment (as much of a discount) to all its customers due to the lack of information regarding their customers’ characteristics. Therefore, in this study, customers of Online Store X were segmented based on their transactional behavior using online transaction history data from March 2021 to March 2023. Two customer analysis models, LRFM and MLRFM, will be combined with RM K-Means to find the best combination through Silhouette Coefficient values. The optimal number of clusters (k) is then determined using the Elbow Method. The results indicate that the optimal number of clusters for both combinations is K=3, with the combination of MLRFM and RM K-Means is the best combination. The finest combination has a silhouette coefficient value of 0.8609. Based on this combination, it is also known that 2,053 customers in cluster 3 are loyal customers, while 2,339 customers in cluster 1 and 2 are lost customers. The results of this study were also implemented on websites built for X Store using Python programming languages and MySQL databases, making it easier for companies to see data visualization.]]>
<![CDATA[CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING ]]>
<![CDATA[Anjar Setiawan]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Ariatmanto, Dhani]]>
<![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.2024.5.2.1521
<![CDATA[Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.]]>
