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[CLASSIFICATION OF SUGAR LEVELS IN BANANA FRUIT BASED ON COLOR FEATURES USING DIGITAL IMAGE PROCESSING-BASED ARTIFICIAL NEURAL NETWORKS ]]>
<![CDATA[S, Mushawwir]]>;
<![CDATA[Burhan, Rafli Ananta]]>;
<![CDATA[Yuliarni, Tarisa]]>;
<![CDATA[Kaswar, Andi Baso]]>;
<![CDATA[Andayani, Dyah Darma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1420
<![CDATA[Bananas are a fruit that has many benefits for human health, because bananas contain a source of vitamins, minerals and carbohydrates. Bananas are a fruit that is often consumed by Indonesian people because of their sweet taste. With this sweet taste, of course bananas have quite high sugar levels, so diabetes sufferers must pay attention to this when choosing bananas. The level of sugar content in bananas can be distinguished by looking at the ripeness of the fruit. To differentiate between them, of course, we use human vision, but human observation also has weaknesses and errors can occur in the process, whether due to lack of lighting, visual impairment, or age. Therefore, this study proposes a classification of the level of sugar content in bananas in the RGB color space using artificial neural networks (ANN). The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, RGB feature extraction, and classification stage. In this study, 300 samples of banana fruit images were used. 210 datasets will be used for training and 90 datasets for testing. The dataset is divided into 3 classes, namely low sugar content, medium sugar content, and high sugar content. Based on the test results that have been carried out, the accuracy of the classification results is 97.78%, the misclassification is 2.22%, and the computing time is 375 seconds. These results show that the proposed method can accurately classify the level of sugar content in bananas.]]>
<![CDATA[COMPARISON OF SUPPORT VECTOR MACHINE AND INDOBERT IN NON-FUNCTIONAL REQUIREMENT CLASSIFICATION OF APPLICATION USER REVIEWS ]]>
<![CDATA[Rais Kumar, Abdul Ghofur]]>;
<![CDATA[Sukmono, Yudi]]>;
<![CDATA[Burhandenny, Aji Ery]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1424
<![CDATA[User reviews of mobile applications have become a valuable source of information for evaluating the quality of an application. It is crucial for application developers to understand what users express in their reviews. One aspect that can be analyzed from user reviews is Non-Functional Requirement (NFR). Classifying reviews based on NFR is essential in understanding how an application can be enhanced. Although user reviews have the potential to provide valuable insights into NFR, manually processing thousands of user reviews is a laborious and inefficient task. Therefore, artificial intelligence methods are employed to automatically classify user reviews into relevant NFR categories. This research discusses the performance comparison of the SVM and IndoBERT algorithms in NFR classification. The study involves collecting application review data from 2018 to 2023, sourced from Google Playstore and Apple Appstore, followed by annotating the review data based on ISO 25010. Subsequently, the data is allocated into training and testing sets with an 80:20 ratio. Further, a data preprocessing phase is conducted, which includes steps such as lowercasing, tokenization, special character removal, text normalization, and text stemming. The next step involves training the SVM and IndoBERT algorithms on the dataset. Finally, the evaluation is carried out by calculating the F1-score. The research results indicate that the IndoBERT model outperforms the SVM model. The IndoBERT algorithm excels in recognizing NFR in reviews, achieving an F1-score of 93%, while the SVM algorithm achieves an F1-score of 91%.]]>
<![CDATA[INTEGRATION OF ESP32-CAM WITH ANDROID AND IOT BASED ENGLISH-INDONESIAN TRANSLATION APPLICATION USING OCR TECHNOLOGY ]]>
<![CDATA[Nurhaliza, Siti]]>;
<![CDATA[Putri, Kirana Alyssa]]>;
<![CDATA[Iqlimah Attyyatullatifah]]>;
<![CDATA[I’zaaz Akhdan Muhadzdzab]]>;
<![CDATA[Atiqah Meutia Hilda]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1489
<![CDATA[Language is a constant element in global human interaction, particularly English. This research presents the design and development of an innovative Android and IoT-based translation application, which facilitates seamless English-Indonesian translation. By utilizing Optical Character Recognition (OCR) technology for text input, the app is enhanced by the integration of ESP32-CAM, a versatile microcontroller with a camera module. This unique combination promises accurate and efficient translation, bridging language barriers while exploring the potential of the Internet of Things (IoT) in linguistic applications. This research reveals the intricate process of creating this translator tool, using the Dart programming language and Flutter framework in Android app development, with the support of Visual Studio Code as the software development environment, as well as the Arduino IDE for the ESP32-CAM microcontroller. It shows how OCR technology and ESP32-CAM significantly enhance the translation experience in an increasingly connected world.]]>
<![CDATA[DETECTION OF ACTIONS BISINDO (INDONESIAN SIGN LANGUAGE) INTO TEXT-TO-SPEECH USING LONG SHORT-TERM MEMORY WITH MEDIAPIPE HOLISTICS ]]>
<![CDATA[Agustin, Risda Rosdiana]]>;
<![CDATA[Maulana, Hendra]]>;
<![CDATA[Mandyartha, Eka Prakarsa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1492
<![CDATA[Sign language is frequently used by those who have difficulty hearing or speaking to communicate. Because it is a non-verbal language that expresses meaning through hand and body gestures, sign language is an essential form of communication for people who rely on it. The objective of this work is to develop a detection that can understand actions made in Indonesian Sign Language (BISINDO), translate them into text, and use speech recognition (Text- to-Speech) to provide audio responses. In particular at Sekolah Luar Biasa, the main objective is to assist and enhance communication among persons with impairments. Long Short-Term Memory (LSTM) and Mediapipe Holistics are use to achieve its objectives. It is demonstrated how LSTM and Mediapipe Holistics enhance performance and accuracy using two different dataset types. The first dataset landmarks created using the Mediapipe Holistics model, while the second dataset provides original shots devoid of landmarks. Batch size and epoch settings are among the many parameters needed for training and testing processes. Model using the landmark-free dataset only manages to reach an accuracy of approximately 89.33%, the model using the landmark with mediapipe of accuracy of about 96.67%. Furthermore, the landmark-based model exhibits strong F1 scores, recall, and precision. The research successfully recognizes a number of BISINDO acts, such as "saya" (I), "kamu" (you), "ayah" (father), "ibu" (mother), and others present in the dataset. On the basis of the gestures it has identified can also make speech.]]>
<![CDATA[OPTIMIZING YOLOV8 FOR AUTONOMOUS DRIVING: BATCH SIZE FOR BEST MEAN AVERAGE PRECISION (MAP) ]]>
<![CDATA[Hidayat, Zaids Syarif]]>;
<![CDATA[Wijaya, Yudhistira Arie]]>;
<![CDATA[Kurniawan, Rudi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1626
<![CDATA[Artificial intelligence (AI), especially computer vision, has made rapid progress in recent years. One of the rapidly developing fields in computer vision is object detection. The ability to detect objects accurately and quickly is essential for the development of autonomous driving technology or vehicles that can operate automatically without human intervention. However, the development of autonomous driving technology is still facing various challenges, especially related to the accuracy and speed of object detection by the system. The purpose of this study is to analyze the performance based on the mean average precision (mAP) value of the results of adjusting the number of epochs, batch size, and image size on one of the emerging object detection methods, YOLOv8, in the context of autonomous driving. The analysis focuses on the batch size hyperparameter on the object detection performance of YOLOv8. The research was conducted with an experimental approach where the YOLOv8 hyperparameters were modified and their performance was evaluated using the driver simulation dataset from the CARLA simulator. Object detection performance was evaluated using the mean average precision (mAP) metric. The research results with the highest mAP value are found in scheme VIII with an mAP value of 98.2% and a training time of 59.45 minutes. For scheme III, it gets the fastest training time of 36.25 minutes. Based on the mAP results, modifications to the number of batch sizes and the use of high image sizes can affect the performance and performance of object detection for autonomous driving.]]>
<![CDATA[ANALYSIS OF USER SATISFACTION LEVEL IN INLISLITE LIBRARY SYSTEM USING END USER COMPUTING SATISFACTION (EUCS) ]]>
<![CDATA[I Made Kerisna Laksana]]>;
<![CDATA[I Made Ardwi Pradnyana]]>;
<![CDATA[I Gusti Lanang Agung Raditya Putra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1834
<![CDATA[The Denpasar Library and Archives Office has implemented an automation service called INLISLite for the development of information and communication technology-based library management and services. However, there are obstacles related to the level of user satisfaction that have not been identified. This study uses the End User Computing Satisfaction (EUCS) Method, focusing on five satisfaction variables: content, accuracy, appearance, ease of use, and timeliness. The research population involved admin employees and users of the INLISLite system at the Denpasar City Library and Archives Office. The sample of 109 respondents was obtained through saturated sampling and purposive sampling. The Likert scale is used to measure user satisfaction with 24 statements for each indicator of each variable. The results of data analysis of the average user satisfaction level were 3.98 with satisfied categories in each variable, namely 4.03 for content, 3.92 for accuracy, 3.93 for format, 3.97 for timeliness, and 4.04 for ease of use. With each EUCS variable known, it has an influence and is significant on the end-user satisfaction of INLISLite. However, it is necessary to make improvements to the system with some improvements. System improvement recommendations are prepared based on open question suggestions for each variable in the EUCS. Continuous improvement and development is expected to improve the quality of library and archival services based on information and communication technology.]]>
<![CDATA[OPTIMIZATION PRODUCT RECOMMENDATION USING K-MEANS, AGGLOMERATIVE CLUSTERING AND FP-GROWTH ALGORITHM ]]>
<![CDATA[Huda, Ratu Najmil]]>;
<![CDATA[Fitriadi, Rifqi]]>;
<![CDATA[Wibowo, Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1901
<![CDATA[The growth of online business has been rising considerably in recent years. The growth is affected by technology advancement in Internet and smartphones and consumer behavior change for better online shopping experience. To anticipate this swift customer behavior, business owners need to have an excellent inventory management to be able to keep making profits. In data mining realm, the algorithm model that is known to be applied in this case is the association algorithm. This model will explicate customers’ purchasing patterns where is useful in calculating stock accurately. The aim of this research is to find an appropriate model in handling large data to obtain valid association rules that have minimum support value, confidence value, and high lift ratios. It is hoped that the results of this research can provide recommendations for online sellers to manage a large variety of goods and to keep making profits. Datasets that contain a large variety of goods are handled first by using a clustering algorithm to group similar items together. The dataset tested was divided into three groups, namely, dataset without clusters, k-means cluster, and agglomerative cluster. After forming three groups of datasets, FP-Growth was applied to each dataset. The result is that datasets with clusters, whether using k-means or agglomerative, have a minimum support value that is greater than datasets without clusters. Most association rules are obtained from the k-means cluster dataset. Based on the model applied in this research, the association itemset size only obtains one conclusion from one premise.]]>
<![CDATA[PREDICTION SYSTEM OF RICE CONSUMPTION NEEDS USING WEIGHTED MOVING AVERAGE METHOD ]]>
<![CDATA[Maulidifa, Renisa]]>;
<![CDATA[Puspa Miladin Nuraida Safitri A.]]>;
<![CDATA[Ririen Kusumawati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1905
<![CDATA[Most of Indonesia's population works in the agricultural sector. The main agricultural commodity is paddy which will be processed into rice. Despite being the fourth largest rice producer in the world, Indonesia continues to import rice. This is due to the rice deficit, declining rice field harvest areas, and the high consumption and demand for rice in the country. Malang Regency is one of the regions in Indonesia that faces challenges in fulfilling rice needs due to the increasing population and decreasing agricultural land due to land conversion. Therefore, this research aims to predict rice demand to ensure the availability of sufficient supply. This research implements the Weighted Moving Average (WMA) method to find the most optimal period and weight with the smallest MAPE value. The results show that WMA using a 3-month period and weights 0.1, 0.1, 0.8 is the best. From the test results, the rice demand obtained MAPE of 7.15% with the prediction results reaching 20,552.25 tons and the planting area obtained MAPE of 22.96% with the prediction results reaching 3842.70 ha for the next period. Further analysis was conducted to determine the efficiency of the available planting area whether it can sufficient the needs of rice. The results show that the expected rice production from the available planting area in Malang Regency can still sufficient the rice needs of the population. This research has also successfully implemented the method on a website-based system to facilitate data processing and prediction process with faster and more accurate results.]]>
<![CDATA[COMPARISON OF NAÏVE BAYES AND INFORMATION GAIN ALGORITHMS IN CYBERBULLYING SENTIMENT ANALYSIS ON TWITTER ]]>
<![CDATA[Dinda Septia Ningsih]]>;
<![CDATA[Suryono, Ryan Randy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1908
<![CDATA[In the current digital era, cyberbullying is very easy to do because access to various social media platforms is very easy to obtain. Generation Z is a generation born in the era of digital technology advancement, being one of the parties that plays a role in the increasing cases of cyberbullying. The twitter social media platform is one of the platforms that is often used as a place for cyberbullying in Indonesia. With the alarming impact, this research aims to analyze cyberbullying cases on twitter. By comparing Naïve Bayes and Information Gain algorithms, this research will provide accuracy results from tweet data containing cyberbullying content. The dataset used comes from twitter with the time span of collecting the dataset is from January 05, 2024 to January 25, 2024. The dataset is then processed to produce a clean dataset that is ready to be tested using both algorithms. In this study, testing the two algorithms using the K-fold Cross Validation technique resulted in variations in each test. In testing both algorithms, an accuracy level is obtained that indicates how successful the model is in making predictions. In simple terms, this accuracy assesses how effective the model is in predicting cyberbullying sentiment in datasets from Indonesian twitter. Testing the Naïve Bayes algorithm obtained an accuracy of 92.3%. Testing the Information Gain algorithm has an accuracy of 97.8%. From the results obtained, it can be concluded that the Information Gain algorithm gets higher accuracy than the Naïve Bayes algorithm for cyberbullying sentiment analysis on Indonesian twitter.]]>
<![CDATA[DEVELOPMENT OF AUGMENTED REALITY APPLICATION FOR GEOMETRY LEARNING USING THE MARKER BASED TRACKING METHOD ]]>
<![CDATA[Pratama, Pangeran Fadillah]]>;
<![CDATA[Hamzah, Muhammad Luthfi]]>;
<![CDATA[Idria Maita]]>;
<![CDATA[Megawati, Megawati]]>;
<![CDATA[Ahsyar, Tengku Khairil]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.1928
<![CDATA[The role of teachers in implementing innovative and creative learning models in the era of industrial revolution 4.0 is an important influence in attracting student’s attention to achieve learning goals. Student’s lack of interest and motivation to learn is a factor in the difficulty of understanding basic mathematical concepts, especially geometric material. Apart from that, it can be seen from the student’s enthusiasm for learning who easily get bored when studying using books alone. The aim of this research is to develop and apply an Android-based augmented reality (AR) application to increase student’s interest in learning and deliver more interactive material. This application uses a marker based tracking method which was developed using the Unity program. The results of application testing using a black box showed that all application features were used successfully without errors. The pre-test and post-test of 19 grade 6 students regarding understanding of geometry material before and after using AR obtained an increase from 60.53 to 86.84. The system usability scale (SUS) test was aimed at teachers and students by providing 10 statements to assess user satisfaction with the application which received a score of 77.84 in the acceptable category. Evaluation of application usability using 3 matrices, namely learnability, obtained a result of 94%, user efficiency in completing tasks was 0.19 goal /sec, and the error matrix obtained a value of 0.44.]]>
