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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962 Documents
<![CDATA[IMPLEMENTATION OF BAYES THEOREM ALGORITHM FOR WEB-BASED EXPERT SYSTEMS FOR DIAGNOSIS OF HUMAN SKIN DISEASES ]]>
<![CDATA[Agustin, Yoga Handoko]]>;
<![CDATA[Fitri Nuraeni]]>;
<![CDATA[Anisa Devisa Putri]]>
<![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.2024.5.1.1363
<![CDATA[The skin is an important organ in the human body which has various roles, such as functioning as a sense of touch, as a means of excretion through sweat glands, regulating body temperature, and as a place to store fat. If the care is not good, the skin can become infected, which is caused by the proliferation of bacteria, germs and viruses in the skin tissue. However, ironically, people often underestimate skin diseases because they are considered less dangerous and do not lead to death. In Garut Regency there is the Tarogong Community Health Center which is a community health center that provides health services, one of which is skin disease examination for the people of Garut. This health center has a practicing doctor, but the doctor is a general practitioner. So to provide services to people who experience skin diseases, they need help from experts. Based on these problems, this research is entitled Implementation of the Bayes Theorem Algorithm in a Web-Based Human Skin Disease Diagnosis Expert System. The Bayes theorem algorithm can determine a possibility that will occur in the future with information from the past and can later reach precise and accurate decisions and information. The methodology used in this research is the Expert System Development Life Cycle (ESDLC) which is a methodology for building or developing an expert system that is structured and directed in its work. The results of this research are in the form of an expert system application that can diagnose 14 types of skin diseases based on 26 symptoms that are often felt by the public and it was found that the results of the system diagnosis and expert diagnosis were 83.3% in agreement with 30 tests carried out with 25 appropriate data and 5 data. it is not in accordance with.]]>
<![CDATA[COMPARISON PERFORMANCE OF WORD2VEC, GLOVE, FASTTEXT USING SUPPORT VECTOR MACHINE METHOD FOR SENTIMENT ANALYSIS ]]>
<![CDATA[Anjani, Margaretha]]>;
<![CDATA[Irmanda, Helena Nurramdhani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1366
<![CDATA[Spotify is a digital audio service that provides music and podcasts. Reviews received by the application can affect users who will download the application. The unstructured characteristic of review text is a challenge in text processing. To produce a valid sentiment analysis, word embedding is required. The data set that is owned is divided by a ratio of 80:20 for training data and testing data. The method used for feature expansion is Word2Vec, GloVe, and FastText and the method used in classification is Support Vector Machine (SVM). The three word embedding methods were chosen because they can capture semantic, syntactic, and contextual meanings around words when compared to traditional engineering features such as Bag of Word. The best performance evaluation results show that the GloVe model produces the best performance compared to other word embeddings with an accuracy value of 85%, a precision value of 90%, a recall value of 79%, and an f1-score of 85%.]]>
<![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[IMPLEMENTATION OF RSA AND AES-128 SUPER ENCRYPTION ON QR-CODE BASED DIGITAL SIGNATURE SCHEMES FOR DOCUMENT LEGALIZATION ]]>
<![CDATA[Nuraeni, Fitri]]>;
<![CDATA[Kurniadi, Dede]]>;
<![CDATA[Rahayu, Diva Nuratnika]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1426
<![CDATA[Maintaining the confidentiality and integrity of electronic documents is essential in the modern digital age. In the contemporary digital world, digital signatures are essential for safeguarding and legalizing electronic documents. The current issue, however, goes beyond digital signatures and instead centers on enhancing security and data integrity. Therefore, RSA and AES-128 super-encryption is required in QR-code-based digital signature techniques for document legalization. This research stage entails constructing a super encryption algorithm, testing it experimentally for security and performance, and designing a digital signature system using RSA and AES-128 super encryption. The results of this research show that the use of RSA and AES super encryption has been proven to have better performance in data security, where the encryption and decryption process time is relatively close to the RSA encryption time, and the comparison of entropy values is better than RSA and AES-128. So, the combination of Super RSA and AES-128 encryption can increase the security level of electronic documents and reduce the risk of hacking. Moreover, the proposed QR-code-based digital signature scheme is also very efficient regarding file size and processing time.]]>
<![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[ENHANCING SENTIMENT ANALYSIS WITH CHATBOTS: A COMPARATIVE STUDY OF TEXT PRE-PROCESSING ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Kurniadi, Dede]]>;
<![CDATA[B. Balilo Jr , Benedicto]]>
<![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.2023.4.6.1448
<![CDATA[Text pre-processing plays a crucial role in the Sentiment Analysis process. Machine Learning models like Chat GPT-3.5 by OpenAI and Google Bard serve as alternative methods for text pre-processing. This study aims to evaluate the capabilities of both Chatbots in the text pre-processing stage while assessing their performance using a dataset obtained by crawling from source X. The study involves a comparison of Chat GPT-3.5 and Google Bard using Decision Tree and Naïve Bayes algorithms. The validation process employs K-Fold Cross Validation with a K value of 10. Additionally, three sampling methods, namely Linear, Shuffled, and Stratified Sampling, are utilized. The findings reveal that Chat GPT-3.5 performs best when using the Decision Tree algorithm with a K-Fold Cross value of 10, and employing Stratified Sampling, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. On the other hand, Google Bard's optimal performance is achieved with the Decision Tree algorithm, a K-Fold Cross value of 10, and Shuffled Sampling, resulting in an Accuracy of 74.00%, Precision of 72.73%, and Recall of 98.77%. The study concludes that Chat GPT-3.5 and Google Bard are viable alternatives for text pre-processing in Sentiment Analysis. Performance measurements indicate that Chat GPT-3.5 outperforms Google Bard, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. These results were validated by comparing them to human annotations, which achieved an accuracy score of 85.20%, Precision of 85.71%, and Recall of 99.03% when using the Decision Tree algorithm with a K-Fold Cross value of 10 and employing Stratified Sampling. This suggests that Chat GPT-3.5's text pre-processing performance is on par with human annotations.]]>
<![CDATA[APPLICATION OF THE AHP METHOD IN DETERMINING OUTSTANDING STUDENT BASED ON INDICATORS OF EDUCATION AND STUDENT ACTIVITIES AT FTI UKSW ]]>
<![CDATA[Munda', Mourin]]>;
<![CDATA[J. P, Sri Yulianto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.5.1455
<![CDATA[The decision making system is a computer-based system that aims to assist in the decision-making process by using existing data to solve semi-structured problems. This research aims to develop an efficient and objective approach for selecting outstanding students in the higher education environment of FTI UKSW. Until now, FTI UKSW has been using manual methods to determine outstanding student. The Analytical Hierarchy Process methods is employed as the primary framework in this research to measure and analyze relevant criteria and sub-criteria for the selection of outstanding student. In assessing outstanding students, several criteria are required, including Cumulative Grade Point Average, Academic Research, Skills/Achievements, English Proficiency, and Extracurricular Activities. The determination of outstanding student using the Analytical Hierarchy Process methods is designed in the form of a web application to simplify the decision-making process for the faculty. The results of the Analytical Hierarchy Process analysis produce relative priorities for each student based on educational indicators and extracurricular activities. Thus, this research provides a systematic framework for decision making in selecting outstanding students that is more objective.]]>
<![CDATA[COMPARING THE PERFORMANCE OF OSPF AND OSPF-MPLS ROUTING PROTOCOL IN FORWARDING TCP AND UDP PACKET ]]>
<![CDATA[Miswar, Nur]]>;
<![CDATA[Herman, Herman]]>;
<![CDATA[Riadi, Imam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.5.1456
<![CDATA[Routing is an important process in computer networks, which involves selecting a path for packet data. The routing mechanism is governed by protocols at layer three of the OSI model or layer two of the TCP/IP model. Routing can be static or dynamic, with dynamic routing algorithms automatically determining the best path in the network. One commonly used dynamic routing protocol is Open Shortest Path First (OSPF), which automatically selects the optimal route for data packets based on received network topology information. The need for more advanced and scalable routing solutions led to the development of Multi-Protocol Label Switching (MPLS), introducing the concept of label switching for faster and more efficient data forwarding. This research aims to analyze the performance of OSPF and OSPF MPLS routing scenarios regarding TCP and UDP services. Through evaluation of Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. This study simulates the network using GNS3 software, with TCP and UDP services transmitted for durations of 15, 30, and 60 seconds. The results showed performance for both scenarios with an average index value of 3.75, indicating "good" network performance according to TIPHON standards across all measurement sessions. Although MPLS OSPF showed slightly lower throughput compared to non-MPLS OSPF, both scenarios showed very low packet loss, low delay, and stable jitter. The evaluation also highlights that the use of OSPF MPLS for UDP services reduces the time delay. Both non-MPLS OSPF and MPLS OSPF effectively support TCP and UDP services, with MPLS OSPF showing advantages in certain QoS parameters, especially for UDP services.]]>
<![CDATA[CLASSIFICATION OF REGIONAL LANGUAGES USING METHODS GRADIENT BOOTS AND RANDOM FOREST ]]>
<![CDATA[Patasik, Eva Sapan]]>;
<![CDATA[Yulianto, Sri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.5.1459
<![CDATA[Indonesia is one of the countries that has the most regional languages in the world, ranking second most. The large number of regional languages that are owned makes it difficult for people between regions to recognize the origins of the regional language, so the author aims to conduct research by identifying a regional language. Identifying a language using data mining, one of the data mining techniques is classification. Classification is a technique used to find the value of data. Classification will build a model from samples of data into groups of the same type. There are two classification methods used in this research, namely gradient boots and random forest, where the two methods will be compared using regional language data from Java, Nias and Toraja. The results of calculating the accuracy values for the two methods used are quite good in classifying languages with results of an accuracy level of 0.8 or 80%, where the results of the gradient boots research have an accuracy value of 0.8850 or 88.5%, while the random forest method has an accuracy value. random forest is lower, namely 0.8794 or 87.94%, so in this study the gradient boots method is the best method.]]>
