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[BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING RESVNET ARCHITECTURE ]]>
<![CDATA[Ramadhani, Syafira Dian]]>;
<![CDATA[Erwin, Erwin]]>;
<![CDATA[Desiani, Anita]]>;
<![CDATA[Bella Agustina, Sinta]]>
<![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.2024.5.4.2637
<![CDATA[The U-Net architecture is often used in medical blood vessel segmentation due to its ability to produce good segmentation. However, U-Net has high complexity due to the presence of the bridge part, which increases the parameters and training time. To overcome this, this research modifies U-Net by removing the bridge part, resulting in V-Net architecture. V-Net architecture faces challenges in capturing deep and complex features. This research proposes modifying V-Net with ResNet architecture in the encoder part, resulting in ResVNet architecture. ResNet, with residual connections, enables the training of very deep networks with more stability and effectiveness in capturing complex features. At the encoder, ResNet is used for more effective training of deep networks and capturing complex features. While at the decoder, U-Net is used to preserve the high resolution and spatial information of the image in segmentation. This study aims to determine the performance evaluation results of the ResVNet architecture. The evaluation measures used are accuracy, sensitivity, precision and Jaccard score. Tests were conducted on the DRIVE and STARE datasets. The measurement results of blood vessel segmentation using ResVNet on the DRIVE dataset resulted in accuracy 96.57%, sensitivity 82.28%, precision 79.57%, and Jaccard score 67.61%. On the STARE dataset, the accuracy results are 96.71%, sensitivity 79.44%, precission 79.44%, and Jaccard score 65.05%. The sensitivity results on the STARE dataset as well as the precision and Jaccard score values on the two datasets produced are still low, in the future this research will make improvements to the ResVNet architecture used.]]>
<![CDATA[SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT VECTOR MACHINE ALGORITHM ]]>
<![CDATA[Firdaus, Thoriq Janati]]>;
<![CDATA[Indra, Jamaludin]]>;
<![CDATA[Lestari, Santi Arum Puspita]]>;
<![CDATA[Hikmayanti, Hanny]]>
<![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.2024.5.4.2673
<![CDATA[Rapid technological developments have opened up new opportunities for public services by utilizing digital application innovations. One example is the West Java Samsat Mobile (SAMBARA) designed by the West Java Provincial Revenue Agency (BAPENDA). The SAMBARA application is expected to accelerate annual vehicle tax payment obligations, but several reviews on the Playstore show user dissatisfaction with SAMBARA's performance. This study aims to conduct a sentiment analysis of SAMBARA application reviews using the Support Vector Machine algorithm. SAMBARA user review data on Google Playstore was collected using the python programming language google play scraper library on google colabolatory resulting in 1620 data on January 2, 2024. The data pre-processing stage involves various steps such as data cleaning, lowercase conversion, tokenization, stemming, stop words removal, normalization, and the use of the TF-IDF method. The data is then labeled positive and negative, positive for reviews with scores of 4 and 5 and negative labels for reviews with scores of 1 to 3. The Support Vector Machine (SVM) algorithm is used for classification, a well-known method for accurate classification. Model evaluation was conducted using a confusion matrix to calculate the precision, recall, and F1-Score values. The evaluation results provide an overview of the performance of the classification algorithm in grouping user reviews into positive and negative categories. The evaluation results show that the SVM algorithm provides quite good performance with an accuracy value of 88.75%, precision 87.51%, recall 81.25%, and F1-Score 83.71% which can be the basis for improving the quality of service of the SAMBARA application. Because the Sambara application has a negative sentiment of 73.4%, it can be concluded that it still gets a bad rating in terms of use.]]>
<![CDATA[IMPLEMENTATION OF AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD FOR PT XL AXIATA TBK STOCK PRICE PREDICTION WITH WEBSITE-BASED DASHBOARD VISUALIZATION ]]>
<![CDATA[Alawiyah, Tuti]]>;
<![CDATA[Permadi, Ipung]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Maryanto, Eddy]]>;
<![CDATA[Rahayu, Swahesti Puspita]]>
<![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.2024.5.4.2700
<![CDATA[The financial market is a dynamic and uncertain sector, with stocks being one of the most commonly used investment instruments. PT XL Axiata Tbk, a telecommunications company listed on the Indonesia Stock Exchange as a blue chip stock, attracts the attention of many investors due to its financial stability and consistent performance. Technical analysis, particularly the ARIMA (Autoregressive Integrated Moving Average) method is used to predict prices. This research focuses on the use of the ARIMA method in predicting the closing price of PT XL Axiata Tbk shares and the implementation of visualization of prediction results through a web-based dashboard. The results of the analysis obtained the best model for stock prediction is ARIMA (2,1,2) with RMSE and MAPE are 50.743 and 0.01653, respectively. The closing price prediction results for 10 consecutive days are 2,190; 2,194; 2,193; 2,196; 2,194; 2,197; 2,195; 2,197; 2,195; and 2,197. Visualization for the results of this prediction is based on a website with the Streamlit framework that presents the results of stock prediction analysis. The existence of a website-based dashboard visualization can help readers find out the prediction results easily and interactively.]]>
<![CDATA[CORRELATION ANALYSIS OF SENTIMENT OF 2024 ELECTION RESULTS AND STOCK MOVEMENTS OF POLITICAL ACTORS IN INDONESIA ]]>
<![CDATA[Sari, Enjelita]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Permadi, Ipung]]>;
<![CDATA[Maryanto, Eddy]]>;
<![CDATA[Rahayu, Swahesti Puspita]]>
<![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.2024.5.4.2701
<![CDATA[General elections (elections) are one of the crucial moments in the political life of a country, where the public democratically elects leaders and their deputies to manage the government. Public sentiment towards the results of elections significantly impacts the political stability and economic conditions of a country. This research aims to analyze the relationship between public sentiment towards the 2024 General Elections in Indonesia and changes in the stock prices of political actors using technological approaches and data analysis. The Long Short-Term Memory (LSTM) method is used to classify sentiment based on Twitter data collected with Harvest Tweet. Evaluation of the LSTM model shows an accuracy rate of 90%, precision of 93.6%, and recall of 92.7%. The correlation analysis using the Spearman coefficient indicates a significant negative relationship with a coefficient of 0.402 and a p-value of 0.046. Implementation of an interactive dashboard using Streamlit facilitates visualization of the data used in this study. Recommendations include increasing the amount of training data for sentiment models, exploring alternative correlation methods for deeper analysis, and refining the interface and data integration on the dashboard to enhance user experience and analysis accuracy. This research is expected to contribute to understanding the dynamics of public sentiment and its impact on the stock market in the context of Indonesian politics.]]>
<![CDATA[SECURE AUDIO FILES USING VIGENERE CIPHER AND PLAYFAIR CIPHER ]]>
<![CDATA[Sumadi, Muhammad Taufiq]]>;
<![CDATA[Zahir S, Achmad Nur]]>;
<![CDATA[Faldi, Faldi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.1624
<![CDATA[This study aims to maintain the confidentiality of audio files sent using a combination of the Playfair cipher and Vigenere cipher methods. In this research, the object of research is an audio file with the extension wave or *.wav. This research requires several stages, including Audio Data Analysis, Determination of System Architecture, Implementation, Testing, and Results Analysis. The results of this study indicate that in the Vigenere Cipher 256 Encryption in audio wave files, the audio messages conveyed sound unclear or have no meaning. From the 6 trial datasets based on analysis of MAE and PSNR, the average value of the encryption process at PSNR was 28.345, and MAE was 97.0625. The average value of the decryption process on PSNR and MAE is 0.0, indicating that the decryption process is successful. The speed of the encryption and decryption process is affected by the audio file's size, which means that the larger the file size, the longer the encryption and decryption time.]]>
<![CDATA[QUALITY OF SERVICE DIPLOMA RECORDING SYSTEM USING SMART CONTRACTS AND NFT POLYGON NETWORK ON LAYER-2 ETHEREUM BLOCKCHAIN ]]>
<![CDATA[Judhie Putra, Rizky Rachman]]>;
<![CDATA[Nursalman, Muhamad]]>;
<![CDATA[Kautsar, Fawwaz]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.1647
<![CDATA[A diploma is a document or certificate given to someone who has completed formal education. Diplomas are generally used as a benchmark for someone to get a job and identity in the eyes of the social environment. Many people think that a diploma is something meaningful or essential, so diplomas are often faked which violates legal norms and violates someone's Intellectual Property Rights (IPR). To anticipate counterfeiting, in Indonesia, there is currently a National Diploma Numbering (PIN) system and an Online Diploma Verification System (SIVIL), but unfortunately, the diploma database storage is still centralized which still allows illegal hacking to occur. On this basis, this research was created to able to provide a safer and more reliable diploma recording system solution, by utilizing Blockchain technology it is possible that every diploma issued can also be turned into a digital asset in the form of an NFT diploma, which is easy to track without having to face traditional bureaucratic obstacles. The NFT diploma functions as a representation of ownership, academic credentials, or identity as a sign of a student's educational history. This research aims to determine the performance of the Blockchain storage system on the Polygon network using smart contracts and IPFS. Apart from that, this research will compare the performance with previous research that used Polygon's layer-1, namely Ethereum. In smart contract cost testing, it was found that each Polygon transaction fee only requires 2.26% of the Ethereum transaction fee. Meanwhile, Quality of Service testing resulted in a throughput of 48.6-49.6 Kbps, packet loss of 0%, and latency of 42.07-44.13 m/s. The results show the potential for better cost efficiency and performance on the Polygon network compared to Ethereum.]]>
<![CDATA[ENHANCING COLLABORATION DATA MANAGEMENT THROUGH DATA WAREHOUSE DESIGN: MEETING BAN-PT ACCREDITATION AND KERMA REPORTING REQUIREMENTS IN HIGHER EDUCATION ]]>
<![CDATA[Wahid, Arif Mu'amar]]>;
<![CDATA[Afuan, Lasmedi]]>;
<![CDATA[Utomo, Fandy Setyo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.1747
<![CDATA[In higher education institutions, effective management of collaboration data is crucial for academic reporting and strategic planning. This study addresses the challenges of managing diverse data types and the necessity for streamlined data management to meet BAN-PT accreditation and Kerma reporting requirements. It aims to design and implement a data warehouse utilizing the star schema for improved accessibility and decision-making. Highlighting the development process, special emphasis is placed on the Extract, Transform, Load (ETL) process with Pentaho to assure data integrity and quality. The methodology involves a systematic approach to constructing the data warehouse, aimed at resolving identified challenges through efficient data organization and quality management. Results demonstrate significant enhancements in data accessibility, reporting efficiency, and quality, leading to reduced administrative efforts and improved decision-making. The research also considers the wider implications of such data management systems in academic administration, suggesting the potential of data warehouses in higher education as benchmarks for similar institutional challenges. Future research directions are recommended for optimizing data warehouse designs and adapting to evolving academic standards, underlining the critical role of advanced data management in meeting stringent accreditation and reporting needs, thus providing a model for technology-driven solutions in educational data management.]]>
<![CDATA[IMPLEMENTATION OF LSB AND PLAYFAIR METHODS TO SECURE TEXT FILES INTO WAV AUDIO FILES ]]>
<![CDATA[Al Akbar, Arief]]>;
<![CDATA[Sumadi, Muhammad Taufiq]]>;
<![CDATA[Faldi, Faldi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.1793
<![CDATA[In the rapidly evolving digital communication era, the demand for information security is escalating. Three main security techniques are required: cryptography, watermarking, and steganography. Despite cryptography and watermarking having detectability weaknesses, steganography emerges as a more reliable choice as it can conceal messages across various media without arousing suspicion. This article discusses the utilization of steganography, particularly the Least Significant Bit (LSB) technique, for embedding messages within audio wave files as the medium. In this research, the author combines steganography with encryption using the Playfair Cipher algorithm to enhance overall data confidentiality. Implementation results demonstrate that the combination of LSB and Playfair methods effectively conceals messages without compromising audio quality. Evaluation of stego quality using PSNR indicates that audio quality remains high after embedding secret messages, with PSNR exceeding 40 dB. Despite successful message extraction during decoding, the message content remains protected and requires decryption to be read. In conclusion, the use of steganography in audio wave files with a combination of Playfair Cipher encryption and LSB methods proves to be an effective approach in preserving privacy and data confidentiality during transmission.]]>
<![CDATA[COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST IN SENTIMENT ANALYSIS OF DISDUKCAPIL APPLICATION REVIEWS ]]>
<![CDATA[Junianto, Haris]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Kusuma, Bagus Adhi]]>;
<![CDATA[Saputra, Dhanar Intan Surya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.1802
<![CDATA[Civil registration administration institutions such as Disdukcapil have an important role in carrying out government functions, in supporting the smooth running of administrative services the Government presents the Disdukcapil Mobile Application platform which aims to provide efficient and fast services to the community regarding various population administration needs. Sentiment analysis of user reviews on the Play Store for the Disdukcapil application is needed to understand user perceptions and needs, as well as to improve service quality and application development. In this study, researchers conducted sentiment analysis using 2 algorithms, namely: Logistic Regression and Random Forest, which after comparing by testing the two algorithms with test data of 18810 user review data from PlayStore, obtained the performance results of each algorithm as follows: 90% accuracy, 91% precision, 89% recall, and f1 90% for the performance results of the Logistic Regression algorithm, while for the performance results of the Random Forest algorithm accuracy 89%, precision 92%, recall 86% and f1-score 89%. From these results the Logical Regression algorithm has better performance than the Random Forest algorithm.]]>
<![CDATA[EMPLOYEE VOLUNTARY ATTRITION PREDICTION AT PT.XYZ: ENSEMBLE MACHINE LEARNING APPROACH WITH SOFT VOTING CLASSIFIER ]]>
<![CDATA[Bey Lirna, Cagiva Chaedar]]>;
<![CDATA[Trimono, Trimono]]>;
<![CDATA[Damaliana, Aviolla Terza]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2007
<![CDATA[This research addresses the complexity of employee attrition challenges at PT.XYZ. The main objective is to develop a predictive system for potential voluntary employee attrition by focusing on an in-depth analysis of the factors contributing to attrition at PT.XYZ. The research utilizes data containing information on the job history of PT.XYZ employees from 2018 to 2023. The method employed in the research is a soft voting ensemble classifier model, incorporating SVM, decision tree, and logistic regression, supported by relevant literature. Analysis and exploration of historical data of PT.XYZ employees are conducted to identify key factors influencing employees' decisions to leave the company. Careful data preprocessing is carried out to ensure dataset quality before applying it to the soft voting classifier model. The results of the soft voting classifier modeling used in this research achieve excellent accuracy in both training and testing datasets with respective accuracy percentages of 99% and 98%. Based on the final results of applying the soft voting classifier model, it is expected to provide deep insights and solutions to enhance employee retention at PT.XYZ.]]>
