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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1,048 Documents
<![CDATA[IMPLEMENTATION OF CLUSTERING ON TWEET UPLOADING SIDE EFFECTS OF COVID-19 POST VACCINATION USING K-MEANS ALGORITHM ]]>
<![CDATA[Santi, Santi]]>;
<![CDATA[Februariyanti, Herny]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.704
<![CDATA[The Covid-19 Vaccination Program has become pros and cons among Indonesian people including Twitter social media users. When the program was running, Twitter users started uploading tweets regarding the side effects that occurred, ranging from mild to severe, both scientifically proven. or not. Of all uploaded tweets, by applying text mining, only tweets containing the queries "vaccine effect" and "post vaccine" in the period from January to June 2022 and Indonesian language tweets will be used and based on these parameters a total of 4800 tweets have been collected, of the total These tweets will be further processed using the clustering method, the k-means algorithm and the silhouette coefficient. The results of implementing the silhouette coefficient show that the best cluster is in cluster 2 with a score of 0.6228720387313319 and the results of the clustering algorithm k-means for 4800 tweets obtained 3917 members in cluster 0, and 883 members in cluster 1 placement. The feature is that cluster 0 contains tweets that state program effects explicitly or explicitly, while cluster 1 states program effects that arise implicitly.]]>
<![CDATA[ASPECT EXTRACTION OF E-COMMERCE AND MARKETPLACE APPLICATIONS USING WORD2VEC AND WORDNET PATH ]]>
<![CDATA[Sari, Dhani Ratna]]>;
<![CDATA[Matsaany , Bayun]]>;
<![CDATA[Hamka, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.726
<![CDATA[Aspect extraction is an essential element in Aspect-Based Sentiment Analysis (ABSA). Errors in determining aspects of ABSA will result in errors in determining the sentiment of an opinion and the accuracy value of ABSA. This study aims to obtain elements of opinion sentences on using e-commerce applications and marketplaces in Indonesia. Corrections of the statement were sourced from social media Twitter with the keywords "e-commerce" and "marketplace" from August 2020 to January 2022, and a total of 54,244 comments were obtained. Determination of the words that are candidate aspects is selected using POS Tagging for classes of noun singular (NN), noun plural (NNS), proper noun singular (NNP), and proper noun plural (NNPS).]]>
<![CDATA[CLASSIFICATION OF TOMATO QUALITY BASED ON COLOR FEATURES AND SKIN CHARACTERISTICS USING IMAGE PROCESSING BASED ARTIFICIAL NEURAL NETWORK ]]>
<![CDATA[Agung, Andi Sadri]]>;
<![CDATA[SR, Amin Farid Dirgantara]]>;
<![CDATA[Hersyam, Muh Syachrul]]>;
<![CDATA[Kaswar, Andi Baso]]>;
<![CDATA[Andayani, Dyah Darma]]>
<![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.2023.4.5.730
<![CDATA[Tomato (Solanum Lycopersicum) is a plantation commodity in Indonesia with a production rate that tends to increase every year. With a high economic value, maintenance is important so that the quality is getting better. The problems that arise at this time are related to the determination of the quality of tomatoes which is still done manually and depends on humans so classification using technology is considered important to be developed. Previously there has been researching related to the classification of tomatoes. However, accuracy and computation time still need to be improved. Therefore, in this research, a method of classification of tomatoes was carried out using Artificial Neural Network (ANN) Backpropagation algorithm by utilizing color features and skin characteristics based on image processing. This research followed several stages, from acquiring 300 tomato images with 3 class levels to the classification process using ANN Backpropagation. Several training scenarios and tests were conducted to select the feature combined with the highest accuracy and fastest computation time. The combination of 3 best features used is RGB color feature with shape and texture features as skin characteristic parameters. Based on training results with 210 training images, an accuracy of 100% was obtained with a computation time of 2.58 seconds per image. While test results with 90 test images, accuracy reaches 95.5% with a computing time of 1.39 seconds per image. So it can be concluded that the method used has gone well in classifying tomato image quality based on color features and skin characteristics.]]>
<![CDATA[COMPARATIVE STUDY OF DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACK DETECTION IN COMPUTER NETWORKS ]]>
<![CDATA[Adam Zukhruf]]>;
<![CDATA[Bagus Fatkhurrozi]]>;
<![CDATA[Andriyatna Agung Kurniawan]]>
<![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.2023.4.5.756
<![CDATA[Distributed Denial of Service (DDoS) attack is an internet crime that aims to consume server resources so that the server becomes unusable. Suricata, Snort and Wireshark are useful software applications for detecting DDoS attacks. This study aims to compare the performance of the snort, suricata and wireshark applications in detecting Distributed Denial of Service attacks. The comparison parameters used are the total attacks that can be detected and memory usage. The type of attack used in testing is syn flood and ping of death. The research results obtained by Suricata became the most effective application in this study compared to snort and wireshark. Suricata excels in memory usage in the two types of attacks performed with the percentage of memory usage being 0.1891 GB (4.975%) during syn flood attacks and 0.00114 GB (0.03%) during ping of death attacks. Suricata also excels in the percentage of the total number of detected ping of death attacks, namely 86,472%.]]>
<![CDATA[DEVELOPMENT OF SCHEDULING SYSTEM WITH GENETIC ALGORITHM IN WEBSITE-BASED SMK NEGERI 1 SINE ]]>
<![CDATA[Saputra, Shafa Bani]]>;
<![CDATA[Pamungkas, Endang Wahyu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.784
<![CDATA[Scheduling is an information that has limited conditions that must be met. Preparation of the schedule will take quite a long time if it is done using conventional media such as writing on paper or books. Scheduling optimization is needed to provide effectiveness and efficiency so that the implementation of learning activities can run more optimally. The genetic algorithm approach method is used to get the optimum schedule. This algorithm produces the best combination for subject pairs and teaching teachers as a whole by determining the initial population and initializing the chromosomes, determining the fitness value, then carrying out crossover selection, and carrying out mutations to produce the best fitness value which will be used to determine the final value of scheduling. The results of the entire algorithm process are consistent with the original prediction data, and the same teacher is not scheduled to teach more than once at the same time. The results of the subject scheduling process using the genetic algorithm obtain a fairly good optimization in subject scheduling.]]>
<![CDATA[DESIGN AND BUILD VEHICLE PLATE DETECTION SYSTEM USING YOU ONLY LOOK ONCE METHOD BASED ON ANDROID ]]>
<![CDATA[Usen, Yolan Anjani]]>;
<![CDATA[Hayat, Cynthia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.791
<![CDATA[The method of collecting the vehicle data is conducted conventionally by gathering data from each region to be converted into single, raw information in the form of vehicle plates for all regions, to be processed on a computer and sent to the Central Bureau of Statistics. It is then transformed into a form of national data file that provides information on vehicle plates for the Indonesian people. This kind of data gathering method requires a lot of time and effort. Therefore, it is a concern for researchers to detect vehicle plates using image processing by utilizing the Android-based You Only Look Once method. The YOLOv4 technique is used because it processes image data directly with optimal performance in order to produce faster predictions. In its application, the researchers use Google Collaboratory to create models and Android Studio for android applications. At the same time, the parameters studied were precision, recall, F1 score, average IoU, and mAP. By using the "Vehicle Registration Plate" dataset, the ratio of which is 70% in training data and 30% in data validation, an accuracy of 77% is obtained with a detection time of 0.05 seconds, whereas the average accuracy value is 86.82%. Therefore, it can be concluded that this study has an optimized performance for detecting vehicle plates using the Android application.]]>
<![CDATA[SENTIMENT ANALYSIS OF POST-COVID-19 INFLATION BASED ON TWITTER USING THE K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS ]]>
<![CDATA[Ratih Puspitasari]]>;
<![CDATA[Findawati, Yulian]]>;
<![CDATA[Rosid, Mochamad Alfan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.801
<![CDATA[The COVID-19 pandemic caused a crisis in global economic growth. The impact of injuries due to the COVID-19 pandemic has also caused price increases and an increase in the inflation rate. Inflation is a price increase caused by a certain factor so that it has an impact on the prices of nearby goods which increase the circulation of money in society to increase. Many people expressed their various opinions or criticisms of the post-COVID-19 price increase policy on social media, one of which was via Twitter. Sentiment analysis was carried out to see how public sentiment is towards the price increase policy after the COVID-19 pandemic, and these sentiments are combined into multiclasses, namely positive, negative and neutral sentiments. So that this sentiment can later be used as material for evaluation regarding the post-COVID-19 price increase policy. This study aims to see and compare the accuracy of the two classification methods, namely K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) in the sentiment classification process. The data used was 5989 tweets with the keywords ""Stuffets Go Up Post-Pandemic", "Fuel Goes Up", "Inflation 2022", "Covid19 Inflation", "Inflation Post-Pandemic" with a data collection period from August to October 2022. The data obtained then enter the text preprocessing stage before later entering the classification stage. The results obtained after carrying out the classification using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) methods show that the Support Vector Machine (SVM) method has a higher accuracy of 79%, while the K-Nearest Neighbor (K -NN) has an accuracy of 54%.]]>
<![CDATA[UX (USER EXPERIENCE) EVALUATION OF THE OPENLEARNING SYSTEM AT UNIVERSITAS MUHAMMADIYAH SURAKARTA USING HEURISTIC EVALUATION AND USABILITY TESTING ]]>
<![CDATA[Imana, Afifah Ghaisani]]>;
<![CDATA[Nugroho , Yusuf Sulistyo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.824
<![CDATA[Since the COVID-19 pandemic in 2020, the learning process at the University of Muhammadiyah Surakarta (UMS) has been carried out online using the OpenLearning platform. The use of OpenLearning media has provided an easy learning process for both lecturers and students. Although there are many conveniences and benefits offered, usability problems still arise, for example, there is no distinction between courses that have ended and those that are still active, the "Edit Page Header" feature on student users, and misperceptions in the progress bar in the Course menu. In addition, previous research has evaluated the UMS OpenLearning platform using the System Usability Scale (SUS) technique and scored 61 (grade scale D) and indicates that the system is well received. To identify more detailed problems related to user experience (UX) for the OpenLearning system applied at UMS, it is necessary to evaluate the UX with the other approaches. In this study, the Heuristic Evaluation method based on 10 principles of Heuristic and Usability Testing based on 5 Usability criteria are implemented to evaluate. The results of this study are a system prototype according to the results of heuristic evaluation. The prototype was evaluated using SUS method on 380 respondents. The SUS calculation yields a value of 69, which indicates that the system is classified to the grade scale category D. This insignificant increase in the SUS score indicates that the UX improvements have not been optimally related to usability due to some factors such as the complexity of the system when used, inconsistencies, and the need for users to adapt to using the system. This finding can be considered by the system development team to fix the weaknesses so as to create an online learning platform with a better UX.]]>
<![CDATA[PCOS DISEASE CLASSIFICATION USING FEATURE SELECTION RFECV AND EDA WITH KNN ALGORITHM METHOD ]]>
<![CDATA[Pitaloka, Nadhira Triadha]]>;
<![CDATA[Kusnawi, Kusnawi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.4.831
<![CDATA[Polycystic ovary syndrome is an endocrine disorder of the ovaries that causes hormonal disturbances in women of reproductive age, where androgen secretion in the ovaries of women with Polycystic Ovary Syndrome (PCOS) is excessive compared to normal women. This usually occur in women with obesity which is characterized by irregular menstrual cycles, chronic anovulation, hyperandrogenism, and even infertility. Efforts are used to treat this disease in the form of hormone therapy, laparoscopic ovarian drilling, and in-vitro fertilization. However, these three therapies are focused on symptomatic therapy and are less effective in treating PCOS-related infertility. Detecting PCOS disease early is very necessary so that prevention and treatment can be carried out immediately. Therefore, a classification is carried out to detect PCOS disease by being able to analyze data that has a high degree of accuracy. The method used for the classification of PCOS disease is using the K Nearest Neighbor (KNN), method which previously carried out the feature selection process, namely the Exploratory Data Analysis (EDA), method which is used for the data analysis process by means of an analysis approach to data to find out the most accurate method and using the Recursive Feature Elimination and Cross-Validation (RFECV) selection method which ranks the features based on their level of importance to the prediction process. Further, the data classification process uses the K-Nearest Neighbors (KNN) algorithm. The results of the Exploratory Data Analysis (EDA) feature selection process produce 10 data attributes that are used and are continued by the Recursive Feature Elimination and Cross-Validation (RFECV) process by producing the 7 most important attributes used and finally the K-Nearest Neighbors (KNN) method has a high level high accuracy by producing an accuracy value of 93%, precision 82%, recall 100%, and F1 score 90%.]]>
<![CDATA[TRANSFER LEARNING TO PREDICT GENRE BASED ON ANIME POSTERS ]]>
<![CDATA[Kaka Kamaludin]]>;
<![CDATA[Woro Isti Rahayu]]>;
<![CDATA[Helmi Setywan, Muhammad Yusril]]>
<![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.2023.4.5.860
<![CDATA[Anime is an animated film with a distinctive graphic design originating from Japan, which is widely favored by various groups. anime itself has a genre like a movie in general, but there is a slight difference from ordinary films, anime has additional genres that are not in ordinary films, such as the Ecchi, Mahou Shoujou, Seinen, Shounen, and Josei genres. Since those genres only exist in anime, this research is devoted to predicting those anime genres. The prediction will use posters from the anime itself, with the help of image processing, namely the Convolutional Neural Network method and Transfer Learning. Transfer Learning will be implanted as a comparison of the performance of the existing architecture with the architecture that will be created, whether the architecture is able to process the dataset properly. The dataset to be used is a dataset of posters and csv documents containing images and details of the anime, the dataset contains anime data from 1980 to 2021 and contains 11651 anime poster data which has different resolution sizes. The ResNet50 model has the highest accuracy rate of 48% with a loss rate of 36%, while InceptionV3 produces 35% accuracy with 69% loss. At the time of testing ResNet50 gave the smallest genre percentage value of CustomModel and InceptionV3, while CustomModel gave the highest genre value. In addition to the value, all modes also predicted the genre well. Especially InceptionV3 is able to predict the music genre, because the music genre has a very small number of datasets, and this music genre is difficult to predict by the ResNet50 and CustomModel models.]]>
