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[CLASSIFICATION OF CAT SOUNDS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND LONG SHORT-TERM MEMORY (LSTM) METHODS ]]>
<![CDATA[Fadhilah Gusti Safinatunnajah]]>;
<![CDATA[Agi Prasetiadi]]>;
<![CDATA[Merlinda Wibowo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.373
<![CDATA[Cats become pets who are very close to humans, and they convey messages by producing identical sounds. Therefore, analysis of pet voices is important for a better relationship between cats and human. Animal communication through sound, especially in cats, depends on the situation or context in which the sound is made such as in a state of danger. Based on these problems, a classification method is needed to classify the similarity of characteristics in the resulting sound pattern. The classification methods used are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) which can remember information for a long time and are used for a long time period. This study aimed to determine feelings or moods based on the sound produced into 4 categories: The Purr, The Meow, The Mating Call, and The Howl. The result of this study is that the best architectural model is to use 4 CNN convolution layers measuring 8-8-8-8 and 2 LSTM layers measuring 8-8. The precision value in this architecture is 0.68, the recall value is 1.00, the accurary value is 0.5625 and the f1-score value is 0.77. The small value of the confusion matrix is caused by the lack of dataset duration in the training process, resulting in underfitting.]]>
<![CDATA[IMPLEMENTATION OF THE NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFICATION OF COMMUNITY SENTIMENT ABOUT DEPRESSION ON YOUTUBE ]]>
<![CDATA[Sri Mulyani]]>;
<![CDATA[Rice Novita]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.374
<![CDATA[Depression is a disease that knows no age, gender and social status. WHO states that more than 264 million people suffer from depression, people with depression will continue to grow if public knowledge about mental health is still low, especially in Indonesia. This can be known from the way the community responds to a case. This study aims to determine public sentiment towards people with depression by classifying comments using the Niave Bayes Classifier (NBC) algorithm and adding the Term Frequency-inverse Document Frequency (TF-IDF) method as a feature extraction method. Sentiment used as data is obtained from YouTube comments on several news media accounts such as tvOneNews, Kompas TV, Tribunnews, Official iNews, VIVACOID, CNN Indonesia and Tribun Jateng, so that 4783 data are obtained with training data of 3826 and 957 testing data. This sentiment was analyzed by giving three classes, namely positive, neutral and negative. The results of the sentiment analysis were dominated by positive sentiment of 93.31%, followed by negative comments of 6.68% while neutral sentiment was 0%, and the accuracy of the NBC Algorithm was 84.11%.]]>
<![CDATA[SOCIAL DISTANCING DETECTION FINDING OPTIMAL ANGLE WITH YOLO V3 DEEP LEARNING METHOD ]]>
<![CDATA[Mutiara Dwi Anggraini]]>;
<![CDATA[Kusrini Kusrini]]>;
<![CDATA[Hanif Al Fatta]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.390
<![CDATA[The COVID-19 pandemic has had a profound impact on all aspects of society. One of the implementations that the government has so far carried out is using masks and maintaining social distance. Over time, social distancing is difficult to control because people are now getting booster vaccines, but some have not. One way to overcome this problem is with a social distance detector system that detects the number of people and the distance of human objects from one another in an area. This study aims to apply in the office area, or the public. This research is one of the developments of a social distancing detector application that produces an optimal angle in using the application. So the program can detect the entirety of the object with optimal accuracy. Angle is very influential in taking the image to be processed in the system. This study uses the python language with the YOLOv3 library. This study got the best results,and the mean average precision in 90%:10% didapatkan dengan learning rate 0,001 dengan nilai mAP 54,11%, deteksi pada saaat penggujian sebesar 100%. Percobaan sudut terdapat 00.150.300, 450.600 dengan total 50 data video testing. Sudut optimal yang didapatkan pada penelitian ini adalah 00.150.300. Hal ini membuktikan bahwa sudut pengambilan video atau peletakan kamera sistem social distancing.]]>
<![CDATA[“IMPROVING GRADE 6 LEARNING OUTCOMES USING LEARNING MEDIA INFORMATION SYSTEM (CASE STUDY OF KRISTEN PENABUR WACANA ELEMENTARY SCHOOL PURWOREJO)" ]]>
<![CDATA[Victoria Angela Sugianto]]>;
<![CDATA[Lasmedi Afuan]]>;
<![CDATA[Nur Chasanah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.391
<![CDATA[The Covid-19 pandemic that has entered since March 2020, requires every sector to carry out all their activities online. One of them is in the field of education. SD Kristen Penabur Wacana is having difficulty providing additional classes or enrichment to grade 6 students. Due to the Covid-19 pandemic, students are learning online. Parents of grade 6 students admit that it is difficult to find additional material and teach it to their children. The school also does not have a learning system that can facilitate students in online learning. To overcome the existing problems, the researcher created an information system entitled “Sistem Informasi Media Pembelajaran Untuk Meningkatkan Hasil Pembelajaran Kelas 6 Sekolah Dasar (Studi Kasus SD Kristen Penabur Wacana Purworejo)”This system was created to make it easier for schools to provide additional classes or online enrichment. The development method used in this research is the Waterfall method. This system is built using the Codeigniter framework. Testing on this system shows that there are no failures in each module's functionality, so that this system is in accordance with user needs. With the information system built, it can make it easier for the school to provide additional classes or enrichment and to improve the learning outcomes of grade 6 students.]]>
<![CDATA[IMPLEMENTATION OF GAMIFICATION IN CAPITAL MARKETS COURSE PERBANAS INSTITUTE ]]>
<![CDATA[Januponsa Dio Firizqi]]>;
<![CDATA[Fangky Antoneus Sorongan]]>;
<![CDATA[Dwi Atmodjo Wismono Prapto]]>;
<![CDATA[Richardus Eko Indrajit]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.4.457
<![CDATA[Along with technological developments and the occurrence of a pandemic caused by the Covid-19 Virus, many activities are carried out digitally and remotely. This pandemic causes disruption and constraints in various industries, sections or sectors. Higher education is one of the sectors experiencing disruption during the Covid-19 pandemic, where teaching and learning processes that were previously held face-to-face in the classroom have turned into virtual spaces. This makes lecturers and students have to be ready to adapt and carry out the teaching and learning process by utilizing technology such as Learning Management Systems, Video Conferences, and Campus Academic Systems. Not infrequently the teaching and learning process becomes monotonous, boring and limited in conveying and receiving the material provided. With the development of information technology can produce a concept of teaching and learning with the help of technology which is commonly called Gamification. The concept of Gamification using Mechanics Dynamic Emotional (MDE) Framework adopts elements of the game concept into the teaching and learning process because it has Mechanical, Dynamics, and Emotional aspects so that learning conditions can be created that are more interesting, fun and enjoyable. The MDE framework will be integrated into e-learning using the Research and Development (R&D) method. The R & D method is a method that is able to improve a product or make a product better. The stages in this study were made into 6 steps, namely: problem identification, data collection, analysis, village, trial and implementation, evaluation. And it can be said that by implementing gamification in lectures in the capital market at e-learning Perbanas succeeded in increasing interest, interaction, and learning atmosphere.]]>
<![CDATA[IDENTIFICATION OF BIOMETRIC DEEPFAKES USING FEATURE LEARNING DEEP LEARNING ]]>
<![CDATA[Anita Sindar Sinaga]]>;
<![CDATA[Arjon Samuel Sitio]]>;
<![CDATA[Sumitra Dewi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2023.4.1.461
<![CDATA[Improved image quality on several frames extracted from video by manipulating image parameters by improving object edges and coloring segmentation to identify individual human biometric parts. Convolutional Neural Network (CNN) is designed to process two-dimensional data on images by classifying labeled data using the supervised learning method. The classification of fake or not fake images is done using the feature learning Deep Learning technique by forming a Machine Learning model. Video samples (testing and testing) are taken from YouTube randomly. Identifying the resemblance of one person's face to another's (real) face using deep learning. Identifying the resemblance of a person's face to another's face (real) on a genuine or fake label using CNN. Overall, the accuracy results models obtained the highest average accuracy on the face = 93.40%, mouth = 88.52%, eyes = 89.75 %. average accuracy = 90%.]]>
<![CDATA[EMPLOYEE PRESENCE APPLICATION USING SWAFOTO AND LOCATION BASED SERVICE AT THE FACULTY OF SCIENCE AND TECHNOLOGY AT UNIVERSITAS TERBUKA ]]>
<![CDATA[Dian Nurdiana]]>;
<![CDATA[Andri Suryadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.4.466
<![CDATA[The Covid-19 pandemic gave birth to a policy of limiting employees who work from office (WFO) and employees who work from home (WFH). At the Faculty of Science and Technology (FST) of the Universitas Terbuka, attendance for employees who do WFH uses Microsoft Form. This is considered impractical and raises several problems. This study will develop a presence application by utilizing selfies and location-based services based on Android. The purpose of this study is to assist in processing attendance data by knowing the location of the employee and authentication using the employee's face. The system can process data into reports that can help faculty know the activities and performance of employees. The software development methodology uses Agile Development. This model has stages including planning, design, develop, test, release, and planning stages. Each stage in the Agile Development model is carried out in an organized and structured manner according to agreed rules and solutions. Software testing uses Black Box Testing which will test the functions, inputs, and outputs of software or applications based on needs. There are 20 test scenarios performed for users and administrators. From testing using Black Box Testing, 100% of the functions run well, so it can be concluded that the application is suitable for use in the Faculty of Science and Technology of the Universitas Terbuka.]]>
<![CDATA[WALLET-BASED AUTHENTICATION ON COLLEGE INFORMATION SYSTEM ]]>
<![CDATA[Rickard Elsen]]>;
<![CDATA[Muhammad Rikza Nashrulloh]]>;
<![CDATA[Ade Sutedi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.473
<![CDATA[Since the widespread use of cryptocurrency, blockchain technology start to be adapted in various applications. Some businesses are already adopting blockchain technology because of its advantages such as data integrity and privacy. One of them is Web 3.0. Web 3.0 puts forward data decentralization so that users can choose what data will be sent to the server. User data is provided locally with the help of a crypto wallet and the server just receives wallet info. With this mechanism, user privacy can be maintained directly by the user himself. All data will be processed at the users' end first before being sent to the server. With the new mechanism of web 3.0 and the advantages of blockchain, we build an application to authenticate students' login activities and grant roles to them based on their wallets. In this paper, we use the prototyping model as the method to build the application. We managed to utilize students’ wallet addresses as credentials. And with the help of Web3 module, we managed to decentralize the authentication process. And as a result of the successful authentication process, students can access their data based on their roles.]]>
<![CDATA[E-CRM FOR SALES OF FASHION PRODUCTS AT JANNAH SIMPANG FOUR BOUTIQUE ]]>
<![CDATA[Diana Pertiwi]]>;
<![CDATA[Yessica Siagian]]>;
<![CDATA[Abdul Karim Syahputra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.4.474
<![CDATA[Sales is the purchase of an item from one party to another by getting money from that party, whether in the form of electronics, clothing, food and others. One of the businesses engaged in the business of selling clothing which includes clothes, pants, bags, shoes and others that are sold to customers is Jannah Boutique. the need for a media in the form of a system that integrates marketing, ordering and customer service into an online-based application that can manage the relationship between the company and its customers. The method used in this research is the concept of Electronic Customer Relationship Management (E-CRM) because it can be a company solution in company service management and can utilize customer databases and their complaints to be input and improvement of the company and can make it easier for customers to place orders (orders) and serve as as a promotional media for company products that can save the company's costs in promoting products to customers. This study resulted in E-CRM on fashion product sales at Jannah boutiques able to integrate existing functions within the company, namely marketing, product sales, information and can improve customer service. The conclusion obtained by E-CRM can improve store performance in service, sales and getting new customers.]]>
<![CDATA[AUTO SCALING DATABASE SERVICE WITH MICRO KUBERNETES CLUSTER ]]>
<![CDATA[Anita Rosdina Nasution]]>;
<![CDATA[Favian Dewanta]]>;
<![CDATA[Bagus Aditya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.4.484
<![CDATA[Data storage media, or what is often referred to as a database is something that is quite vital for technological developments. As the amount of data increases, it allows database services to experience downtime. For this reason, it is necessary to build an infrastructure that can replicate itself, so that it will avoid downtime. This infrastructure can be built using a container orchestration tool called Kubernetes which has high availability and autoscaler features, so it can replicate and guarantee service availability, to avoid downtime. This research builds a MongoDB NoSQL database service. This service is built using micro Kubernetes clusters from several different data centers. This service also implements a horizontal pod autoscaler feature that is capable of replicating pods, to increase high availability and avoid downtime. The autoscaling process will be tested by providing a load request for the service. Testing is done several times on each server. This study will compare the MongoDB service that was built monolithically with a micro Kubernetes cluster, and with HPA features and without HPA features by paying attention to several things. Based on Response Time, Response Code per Seconds, and CPU Usage, the results obtained are that the service built on a micro Kubernetes cluster with HPA features is the best, with a constant response time value below 100 ms, Response Code per Seconds reaches 500 threads per second. seconds, and CPU Usage in the range of 30 – 55%.]]>
