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[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[COMMUNICATION SECURITY IN THE MQTT PROTOCOL FOR MONITORING INTERNET OF THINGS DEVICES USING METHODS ELLIPTIC CURVE CRYPTOGRAPHY ]]>
<![CDATA[Axel Natanael Salim]]>;
<![CDATA[Tata Sutabri]]>;
<![CDATA[Edi Surya Negara]]>;
<![CDATA[M Izman Herdiansyah]]>
<![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.2024.5.2.1916
<![CDATA[The emergence of the IoT has become one of the most significant technology trends. The application of IoT is aimed at enhancing efficiency, comfort, and facilitating various human activities. One key aspect of IoT implementation is efficient communication between devices, with one of the most commonly used protocols being MQTT protocol. MQTT enables the transmission of data in real-time or based on specific events, although there are still several challenges that need to be addressed. One of the main challenges of MQTT is information security issues, prompting this research to examine effective solutions to enhance communication security in IoT applications that utilize MQTT protocol. One method of securing communication between IoT devices can involve using lightweight cryptographic communication security methods such as ECC method. ECC method is chosen because it utilizes shorter keys while still providing high security, making it more efficient when implemented on IoT devices. The results obtained indicate that data sent to MQTT Broker cannot be read and converted manually, ensuring much safer data transmission. Based on the test results, the tool can effectively read, process, and send data to MQTT Broker. QoS measurements on the system revealed that data encrypted and sent from the subscriber to MQTT Broker had an average delay time of 54.1 ms, throughput of 410.4 bps, zero packet loss, and jitter of 0.00 ms. Looking at the research findings, it can be concluded that this ECC method could serve as a solution to data communication security issues in the MQTT protocol.]]>
<![CDATA[SENTIMENT ANALYSIS OF CYBERBULLYING USING BIDIRECTIONAL LONG SHORT TERM MEMORY ALGORITHM ON TWITTER ]]>
<![CDATA[Safitri, Anisa Ika]]>;
<![CDATA[Bayu Sasongko, Theopilus]]>
<![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.2024.5.2.1922
<![CDATA[Cyberbullying on social media such as Twitter is becoming an increasing social problem in today's society. Cyberbullying has a negative influence on mental health, increasing the risk of anxiety, sadness, and even suicide. The purpose of this research is to develop a model to classify tweets that contain or do not contain cyberbullying by applying the BiLSTM technique to sentiment analysis on Twitter. In this research, Word2Vec is used to weight each word in a tweet. The initial stage in this research is data collection with a total dataset of 47,692 tweets generated by Kaggle, preprocessing which consists of data cleaning, removing duplicates, case folding, tokenizing, stopword removal and lemmatization, classification and evaluation. This research uses the Bidirectional Long Short-Term Memory (Bi-LSTM) method and identifies patterns associated with bullying on social media. Testing uses Confusion Matrix and the results on classification show accuracy of 82.29%, precision of 82,04%, recall of 81,95% and F1-Score 81,89%. This sentiment analysis technique is expected to be the first step to combat and avoid cyberbullying on the Twitter platform. From several tests of existing reference algorithms, the classification accuracy performed includes having good performance.]]>
<![CDATA[ANALYSIS OF GRABAG GUIDE APPLICATION ACCEPTANCE FOR INTRODUCTION TO TOURIST ATTRACTIONS USING THE TECHNOLOGY ACCEPTANCE MODEL (TAM) ]]>
<![CDATA[Setiya Putra, Yusuf Wahyu]]>;
<![CDATA[Machmudi, Moch Ali]]>;
<![CDATA[Naim, Abdul Ghani]]>
<![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.2024.5.2.1925
<![CDATA[This research aims to analyze application user acceptance of the tourism introduction Android application in Grabag District, Magelang Regency using three (3) variables contained in the TAM (Technology Acceptance Model) model, namely Attitude towards Use, Perception of Ease of Use and Perception of Usefulness. This research needs to be carried out to resolve the problem of application acceptance which has an impact on the level of tourist visits, so that later application development and improvements can be carried out according to needs. The respondents used were 81 users consisting of sub-district officers, business or tourism owners and tourists. To obtain data whose validity and reliability have been tested and then analyzed using multiple linear regression techniques, a data collection method using a questionnaire was used. The results of the analysis of the 1st regression equation show that the Perception of Usefulness variable (X1) has a significant influence on the Attitude towards Use variable (Y). The Perception of User Ease variable (X2) has a significant influence on the Attitude towards Use variable (Y). The results of the analysis of the 2nd regression equation show that variables X1 and X2 together have a significant influence on Attitude to Use (Y).]]>
<![CDATA[LEAF DISEASE DETECTION IN TOMATO PLANTS USING XCEPTION MODEL IN CONVOLUTIONAL NEURAL NETWORK METHOD ]]>
<![CDATA[Arifin, Nurhikma]]>;
<![CDATA[Maratuttahirah]]>;
<![CDATA[Juprianus Rusman]]>;
<![CDATA[Muhammad Furqan Rasyid]]>
<![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.2024.5.2.1926
<![CDATA[This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each category having 100 images. In the model training phase using the fit method in TensorFlow, 17 epochs were performed to teach the model to recognize patterns in tomato leaf disease images in the training dataset. The model testing results on 30 tomato leaf images showed an accuracy rate of 85.84%. This result indicates a positive indication that the developed CNN model performs well in detecting and classifying tomato leaf conditions. Thus, this research can contribute to improving the understanding and management of leaf diseases in tomato plants to support more productive and sustainable agriculture.]]>
<![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.]]>
<![CDATA[TEXT MINING WITH LATENT DIRICHLET ALLOCATION FOR ANALYZING PUBLIC COMMENTS ON THE M-PASSPORT APPLICATION ]]>
<![CDATA[Hapsari, Theresia Shinta]]>;
<![CDATA[Nataliani, Yessica]]>
<![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.2024.5.4.1929
<![CDATA[The M-Passport application is a service application developed by the Directorate General of Immigration of Indonesia to assist the public in applying for new passports and replacing passports online. However, in its implementation, this application has not been able to give satisfaction to its users. It is proven by the low rating of the application and the numerous negative comments on the Google Play Store. One way to identify the application's shortcomings is by analyzing user comments. In analyzing the abundance of comment data, this study utilizes the text mining method with Latent Dirichlet Allocation (LDA) topic modeling. The analysis with this method aims to find topics frequently discussed in comments so that the government can identify the shortcomings of the M-Passport application. The results of comment analysis with LDA topic modeling produced seven topics, from which three topics with the highest coherence values were selected. These three topics are then interpreted to obtain information about the public's concerns regarding the M-Passport application. The results of this interpretation include users frequently failing to log in or register to the M-Passport application, users feeling that the M-Passport application does not assist them in passport management due to constraints in the online queue feature, and some users still finding it difficult to use the M-Passport application.]]>
<![CDATA[IMPLEMENTATION OF STREAMLINE REALTIME STOCK USING AUTO-SCALING THROUGH GOOGLE CLOUD PUB/SUB AT PT XYZ ]]>
<![CDATA[Prabawa, Joseph Heykel]]>;
<![CDATA[Nugroho, Adi]]>
<![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.2024.5.4.1930
<![CDATA[Shortening the updating and inputting of accurate and real-time stock data is crucial for smooth retail business operations at PT XYZ. The existing system had low availability, lacked scalability, and incurred high costs in managing inventory in real time. Implementing real-time stock streamline with automatic scaling and Google Cloud Pub/Sub can help achieve this goal. This system utilizes Google Cloud Pub/Sub as a message delivery platform to distribute stock information from sender to receiver in real-time. Auto-scaling is used to automatically increase or decrease the number of servers processing stock data based on demand. The system is designed using Python and integrated through libraries with the Google Cloud Platform. The results of this research prove that the system is capable of providing optimal performance and scalability with high availability, good security, and cost savings.]]>
