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[TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE ]]>
<![CDATA[Indra Yunanto]]>;
<![CDATA[Sri Yulianto]]>
<![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.2022.3.4.292
<![CDATA[The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%.]]>
<![CDATA[COMPARISON OF PREDICTION ANALYSIS OF GOFOOD SERVICE USERS USING THE KNN & NAIVE BAYES ALGORITHM WITH RAPIDMINER SOFTWARE ]]>
<![CDATA[Agista Nindy Yuliarina]]>;
<![CDATA[Hendry Hendry]]>
<![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.2022.3.4.294
<![CDATA[GoFood is a service provider that has a very important role in human life, especially in this growing era. Currently, many service providers are competing to meet the needs of users, including GoFood. However, not all service providers can meet and know the needs needed by users, because they focus on the services offered and only the quality of services provided. Therefore, survey analysis is needed to obtain customer satisfaction data that will be used to satisfy GoFood service users. The classification method uses the KNN and Naive Bayes algorithms, which are good algorithms for testing 1,000 records of GoFood user data that have been obtained previously. The test results using Cross Validation and T-Test show that the KNN algorithm is the best algorithm with 98.80% Accuracy and 100% Recall, while Naive Bayes obtains 94.10% Accuracy and 94.43% Recall.]]>
<![CDATA[DATA MINING TECHNIQUE USING NAÏVE BAYES ALGORITHM TO PREDICT SHOPEE CONSUMER SATISFACTION AMONG MILLENNIAL GENERATION ]]>
<![CDATA[Margaretha Intan Pratiwi Hant]]>;
<![CDATA[Hendry Hendry]]>
<![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.2022.3.4.295
<![CDATA[Shopee is one of the largest e-commerce platforms currently being used by Millennials. The use of Shopee itself makes it very easy for consumers to process transactions. Shopee itself is committed to maintaining and improving customer satisfaction so they don't switch to other competitors. However, it is undeniable that there are still many cases that can harm consumers when using the platform. With the cases that occur, it is very possible that there will be a big influence on the level of consumer satisfaction on the platform. Consumers will feel satisfied when the product or service used can meet consumer expectations. This study was made with the aim of predicting the level of consumer satisfaction of Shopee Indonesia among the Millennial Generation. This study applies data mining using the Naive Bayes Algorithm. The Naive Bayes algorithm itself is a simple probability classification that can calculate all possibilities by combining a number of combinations and the frequency of a value from the database obtained. The attributes used in conducting this research include Name, Gender, Age, Price, Performance and Efficiency, Fulfillment, Reliability, Control and Security, Responsiveness, Compensation, Contact, and Description of Satisfaction Value. In this study, the results obtained from several input attributes that create a causal relationship when classifying satisfied and dissatisfied consumers. The results obtained can provide benefits for the Shopee company in increasing customer satisfaction. After carrying out the testing process, it can be concluded that the Naive Bayes Algorithm is an algorithm that is suitable for use in the classification process for measuring Shopee Indonesia's consumer satisfaction level among the Millennial Generation, with an accuracy rate of 89.65%.]]>
<![CDATA[DATA CLUSTERING ON TEACHING AND LEARNING EVALUATION APPLICATION OVER PANDEMIC ERA ]]>
<![CDATA[Arie Vatresia]]>;
<![CDATA[Yosan Fredianto]]>;
<![CDATA[Asahar Johar T]]>
<![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.2022.3.5.296
<![CDATA[SIEPEL is an evaluation application for teaching and learning process in University of Bengkulu. It is mandatory for every student to fill the questionnaire before they can see the marking value for each subject each semester. This survey was designed to meet the requirements and expectations of students as educational service for customers. This data is very important to improve the quality of teaching and learning process for further policy and decision maker. However, the analysis of the data remains an open question as the size and the distribution of the data is become some issues to process the analysis. Here, we showed the new approach to analysis the data using K-Means Clustering to see the better distribution and understanding over the evaluation data. This paper used elbow method to find the best number of clusters to be implemented on the algorithm approach which results in four clusters of satisfaction values (unsatisfied, less satisfied, satisfied, and very satisfied). The result of this analysis was published based on website system to show the visualization of analysis. Furthermore, this research showed that the average value of evaluation result for 4 semester was very satisfied 6.50%; satisfied 43.89%; less satisfied 44.26%; and not satisfied 5.36%. The value of vary satisfied students was dropped from 20.47% to 0.12% by 2 years and the value for less satisfied was increased from 27.64% to 66.32%. This term was happened because of the pandemic era and the change on the process of learning and teaching on University of Bengkulu.]]>
<![CDATA[THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM ]]>
<![CDATA[Asri Mulyani]]>;
<![CDATA[Dede Kurniadi]]>;
<![CDATA[Muhammad Rikza Nashrulloh]]>;
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Meta Regita]]>
<![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.2022.3.4.297
<![CDATA[The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.]]>
<![CDATA[ASSET MANAGEMENT SYSTEM DESIGN OF VILLAGE BASED ON GEOGRAPHIC INFORMATION SYSTEM ]]>
<![CDATA[Heri Suhendar]]>;
<![CDATA[Joko Iskandar]]>;
<![CDATA[Dede Kurniadi]]>;
<![CDATA[Yosep Septiana]]>
<![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.2022.3.4.299
<![CDATA[Management of an asset by the government is a process that starts from planning to asset inventorying that have been pre-existing or obtained from legitimate assistance so that they can managed appropriately and beneficially for the community. For the government, especially in village regions, management of assets is very important, so that both government apparatus and village community get complete, accurate and real-time information about the assets owned by the village government so that the information can be used for activities of village government and communities optimally. The goal of this research is to design and build an asset management system based on geographic information system (GIS) for government in the village. The GIS-based asset management design system uses a waterfall-model approach with five stages, namely: 1) Analysis, 2) Design, 3) Implementation, 4) Integration Testing, and 5) Maintenance. This asset management application is built with web-based technology using the Leaflet framework that supports Web Map Service (WMS) layers, GeoJSON data, vectors and tile layers, while the database in this application uses MySQL. The results of this GIS-based asset management system design research can be used to store, collect, repair, process, control and monitoring assets so that asset management for activities that benefit the community can be optimally improved. For the maintenance and utilization of asset management applications, training is carried out for operators and supervisors, as well as system support personnel.]]>
<![CDATA[PREDICTION OF BABY BIRTH RATE USING NAÏVE BAYES CLASSIFICATION ALGORITHM IN RANDAU VILLAGE ]]>
<![CDATA[Magda Kitty Hartono]]>;
<![CDATA[Hendry Hendry]]>
<![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.2022.3.4.302
<![CDATA[The birth rate is one of the factors increasing the rate of population growth. Birth or fertility can affect the population, getting more lower of birth rate in an area, the higher the life expectancy in that area. The number of births in Randau Jekak Village is increasing every year. The Naïve Bayes algorithm can be used to predict the future births rate because this algorithm is a simple algorithm and uses a lot of data as information in collecting data groups, and with data mining techniques it can see the predictive pattern of each variable and test. The testing data and the training data are expected to help the Village Institution or Office in Randau Jekak to suppressing the rate of population growth which increases every year. The results of this study can be concluded that the Naïve Bayes Algorithm is suitable for predicting the birth rate of babies in Randau Jekak Village with the classification technique, the birth rate in Randau Jekak Village in 2021 is High. The results of this data will be very useful for the Randau Jekak Village office in suppressing the rate of population growth in the coming year.]]>
<![CDATA[COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION ]]>
<![CDATA[Takdir Zulhaq Dessiaming]]>;
<![CDATA[Siska Anraeni]]>;
<![CDATA[Suwito Pomalingo]]>
<![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.2022.3.5.310
<![CDATA[Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.]]>
<![CDATA[NAÏVE BAYES ALGORITHM CLASSIFICATION IN SENTIMENT ANALYSIS COVID-19 WIKIPEDIA ]]>
<![CDATA[Jessica Margaret Br Sembiring]]>;
<![CDATA[Hendry h]]>
<![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.2022.3.4.311
<![CDATA[In recent years during the pandemic Wikipedia created more than 5,200 new pages regarding COVID-19 cases, with an accumulation of more than 400 million pages by mid-June 2020. Wikipedia is one of the most popular websites of our time. In this case Wikipedia always integrates new and fast research. To get an opinion from wikipedia text, sentiment analysis is needed. The analysis was conducted using a classification containing public sentiment regarding the issue of COVID-19 in Indonesia. The classification method used in this study is naive bayes classifier (NBC). Naïve Bayes Classifier is a popular method of solving classification problems. This classification method is often used in sentiment analysis in both precision and data computing. This wikipedia classification is obtained from each label, namely positive, negative and neutral classes. The results of tests conducted in the classification of naive bayes get a high accuracy of 81%.]]>
<![CDATA[INFORMATION SYSTEM DESIGN COMPLETENESS OF FILLING OUT DISCHARGE SUMMARY OF INPATIENTS ]]>
<![CDATA[Fransiska Ayu Veren Nyoman Sebastianus]]>;
<![CDATA[Edi Suharto]]>
<![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.2022.3.4.312
<![CDATA[The discharge summary or also known as a medical resume is a sheet that contains a detailed explanation or summary of important information about the patient, this sheet is part of the medical record.The summary of discharge of patients who are hospitalized is one of the criteria used to assess the quality of hospital health care because the summary of discharge is a very important record, in which there is information about the patient's diagnosis while the patient is hospitalized. There are also medical and non-medical interventions that have been carried out by health workers to patients. The discharge summary must be filled out correctly and completely. There were problems found in hospital x, such as there was still a discharge summary that had not been filled out completely so it had to be returned to the patient's room, also the process of checking the completeness of the discharge summary and making reports were still done manually, which was typed in Microsoft excel. The purpose of this research is to create a specific information system for process of analyzing the completeness of filling in inpatient discharge summary. The method applied is qualitative research methods and descriptive and data collection techniques with direct observation at hospital x and interviews with medical record officers on duty, and the development of the system used is SDLC with the waterfall method through the stages of analysis, design, coding, and testing. The design of this program uses visual basic programming on microsoft visual studio 2010 by making reports using crystal reports. Based on the results of blackbox testing, this program gets good results. All menus in this program can run properly and exactly according to their functions.]]>
