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[ANALYSIS OF PRODUCT STOCK INVENTORY FORECASTING USING WEIGHTED MOVING AVERAGE METHOD ]]>
<![CDATA[Anita Rahayu]]>;
<![CDATA[Arny Lattu]]>;
<![CDATA[Mupaat Mupaat]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.421
<![CDATA[A company is said to be successful if it has fulfilled the demands of consumers in the future. cv. Aadiba company is engaged in sales services as a reseller by selling men's outdoor shoes. Problems with the company Cv. Aadiba, which is unable to meet consumer demand because they often experience a shortage of product stock, so far, planning for purchasing product stock is estimated manually based on previous experience and therefore this study aims to predict product stock inventory in order to minimize product stock purchases errors in the future period. and make the implementation of the forecasting system. The method used in the study is the Weighted Moving Average (WMA) method which can estimate the amount of stock that must be purchased for the future period. The data used for this research is sales data from January 2021 to April 2022, the moving average of the previous 3 months. Forecasting results using the Weighted Moving Average (WMA) method produce future sales of 612.5 and the error value of MAD is 104.448718, MSE 28380.712 and the MAPE value is 0.179160773% or 0.18% The smallest error value is considered the most appropriate used for forecasting, then the MAPE value is considered appropriate in forecasting because it has the smallest error value and the implementation of a forecasting system that can facilitate the Cv. Aadiba in terms of forecasting in the future period.]]>
<![CDATA[SENTIMENT ANALYSIS USING K-NEAREST NEIGHBOR BASED ON PARTICLE SWARM OPTIMIZATION ACCORDING TO SUNSCREEN’S REVIEWS ]]>
<![CDATA[Anita Nur Syifa Rahayu]]>;
<![CDATA[Teguh Iman Hermanto]]>;
<![CDATA[Imam Ma'ruf Nugroho]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.425
<![CDATA[High UV exposure and tropical climate are afftected by the equator that passing through Indonesia. In this case, early aging will haunted not only by skincare lovers but also the rest of Indonesians including male and female. By so, we need to protect our skin using sun protector like sunscreen. Sunscreen application awareness through reviews by many different user probably are the most effective way to get to know the suitable sunscreen. By scrolling through the reviews surely will be time wasting. As of, sentiment analysis is the solution to classifying between negative and positive sentiments from the reviews. This research uses K-Nearest Neighbor (K-NN) algorithm as classification method because this method way more easy and efficient to use by its self learning, thus K-NN can learned its own data through its neighbor. Particle Swarm Optimization is used to increasing the accuration. Evaluation method using Confusion Matrix and the results are accuracy, precision and recall. Classification result using only k-NN and optimized with PSO are getting increase. Brand ‘Azarine’ rank the first accuration value with 91,89% using k-NN and 92,80% using k-NN PSO.]]>
<![CDATA[GROUPING THE PREVALENCE OF DISEASE CASES BY AGE IN BANDUNG CITY HOSPITALS USING K-MEANS ]]>
<![CDATA[Irfan Soliani]]>;
<![CDATA[Safitri Juanita]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.430
<![CDATA[In 2019, the World Health Organization (WHO) stated that the top 10 types of diseases accounted for 55% of the 55.4 million deaths in the world. Meanwhile, in Indonesia, the province of West Java has the largest population, with the capital city of Bandung. Based on the health profile of the Bandung City Hospital, there were the ten highest diseases based on 18,147 cases. However, the data has not been processed into helpful information for the health department, especially the city of Bandung, to help determine disease cases by age group. So that the contribution of this study is to classify the prevalence of disease cases by age in Bandung City Hospital; this study aims to help the Bandung City Health Office take preventive, treatment and counselling actions against diseases that have a prevalence of disease cases based on age. This study uses the CRISP-DM methodology, with the K-Means clustering method and the testing method using the elbow method and the Davies-Bouldin Index (DBI). Data processing using rapid miner software and python programming. This study concludes that the optimal cluster value is K=2. The value of cluster 0 consists of the type of disease with the lowest case, and cluster 1 consists of the kind of disease with the highest case. Cluster 1 is the elderly and adult age group, while the age group in cluster 0 is the infant age group, the toddler age group, and the child age group.]]>
<![CDATA[IMPLEMENTATION OF FORECASTING EXPONENTIAL SMOOTHING IN THE NUMBER OF NEW STUDENT PREDICTION INFORMATION SYSTEM AT SMK AL MA'SHUM ]]>
<![CDATA[Suci Sulistia]]>;
<![CDATA[Muhammad Amin]]>;
<![CDATA[Santoso]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.431
<![CDATA[SMK Al Ma'shum annually conducts socialization and promotions in junior high schools. Prediction of the number of prospective new students is one of the most important things in the evaluation process or high cognitive at SMK Al Ma'shum. This serves to determine the priority of how many prospective students will be accepted, where the number of students sometimes tends to decrease and increase. This makes the school must try to estimate the number of new students from each of these majors next year will increase or decrease. If this is not done by the school, it will have an impact on the operational costs of the school. The goal is to predict the number of prospective new students who will be accepted at the Al Ma'shum Vocational School next year using the forecasting exponential smoothing method. The prediction system for new students at the Al Ma'shum Vocational School uses the PHP and MySQL programming languages to help the forecasting process be fast and accurate. The prediction results for 2022 are accounting majors as many as 19.23 new students with MAPE 20.52%, computer and network engineering majors as many as 102.73 new students with MAPE 9.12% and motorcycle engineering majors as many as 76 new students with MAPE 25.53 %. So it can be concluded that the forecasting exponential smoothing method can help predict new students at SMK Al Ma'shum.]]>
<![CDATA[FORECASTING PRICES OF FERTILIZER RAW MATERIALS USING LONG SHORT TERM MEMORY ]]>
<![CDATA[Eliansion Ivan]]>;
<![CDATA[Hindriyanto Dwi Purnomo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.433
<![CDATA[This study uses long short term memory (LSTM) modeling to predict time series data on the price of fertilizer raw materials, namely prilled urea, granular urea, ammonium sulphate((NH4)2SO4), ammonia (NH3), diammonium phosphate((NH4)2HPO4 ), phosphoric acid (H3PO4), phosphate rock (P2O5), NPK 16-16-16, potash, sulfur, and sulfuric acid (H2SO4). Predictions are made based on data that existed in the past using the long short term memory method, which is a derivative of the recurrent neural network. Carry out the evaluation process by looking at the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model that has been created. The results obtained are quite good, as seen from the root mean square error (RMSE) and mean absolute percentage error (MAPE) which are close to 0 and not too high. Sulfur raw material got the smallest root mean square error (RMSE) with a score of 0.053 and diammonium phosphate raw material got the smallest mean absolute percentage error (MAPE) evaluation value with 2.3%, while the largest value was for the root mean square error (RMSE) of raw materials. Phosphoric acid fertilizer raw material with a value of 22,979 and the largest mean absolute percentage error (MAPE) comes from sulfuric acid fertilizer raw material with a value of 9.180%.]]>
<![CDATA[THE THE EFFECT OF ABDURRAB UNIVERSITY LIBRARY WEBSITE QUALITY ON USER SATISFACTION USING MULTIPLE LINEAR REGRESSION AND IMPORTANCE PERFORMANCE ANALYSIS ]]>
<![CDATA[Dasri Surya Hamdani]]>;
<![CDATA[Muhammad Jazman]]>;
<![CDATA[Muhammad Luthfi Hamzah]]>;
<![CDATA[Anofrizen Anofrizen]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.454
<![CDATA[Abdurrab University library website is an effort to improve services in providing information in the form of physical book data and digital collections to its users. This study aims to analyze the influence of the quality of the Abdurrab University library website on user satisfaction based on end user perceptions. The WebQual 4.0 method was modified by the researcher with the addition of the variables user interface quality and the quality of reliability to compile this research instrument. The distribution of questionnaires was carried out online and offline to 96 respondents obtained from the results of sample calculations using the slovin formula from a total population of 2552 people. The quality of Abdurrab University library website based on multiple linear regression analysis partially variables Usability Quality and The Quality of Reliability affect user satisfaction while simultaneously all WebQual 4.0 variable modifications affect user satisfaction. The influence of independent variables on dependent variables received a value of 43.6% and 56.4% was influenced by independent variables that were not used in this study, so the quality of the website was classified as poor. The quality of Abdurrab University library website is based on Importance Performance Analysis (IPA) for conformity level analysis of 85.29% and gap analysis of -0.55, with a conformity level analysis value of 85.29% of websites is good. The conclusion of this study is that the quality of the Abdurrab University library website affects user satisfaction, so this research can be used as a reference for improvement by the Abdurrab University library.]]>
<![CDATA[USING FORWARD CHAINING METHODS TO DIAGNOSE CHOLESTEROL DISEASE USING THE WEB ]]>
<![CDATA[Venny Octavia]]>;
<![CDATA[Jhonson Efendi Hutagalung jhonson]]>;
<![CDATA[Cecep Maulana Cecep]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.464
<![CDATA[Cholesterol disease can attack humans, both young and adult, in this case it is necessary to do a quick diagnosis to provide basic knowledge about cholesterol disease to sufferers. Advances in expert systems can overcome this problem, namely by designing a web-based computer system that is integrated with databases and programming languages such as PHP-MySQL so that it can help cholesterol patients to diagnose the disease. The expert system application in its decision making uses a forward chaining inference engine where the goal driven data will start a search on the initial node to the goal node until it gets results. The results of the implementation of the system, namely the system provides questions in the form of symptoms that must be answered by the patient based on the symptoms experienced by the patient and the results of the process the system will provide information on what type of cholesterol disease he is experiencing in order to get a solution with treatment and handling. System that works with a knowledge base search that is able to provide decisions by utilizing an expert knowledge base.]]>
<![CDATA[THE INFLUENCE OF DIGITAL MARKETING AND LIFESTYLE ON THE DECISIONS OF E-COMMERCE (SHOPEE) USERS IN INDUSTRY 4.0 ]]>
<![CDATA[Rido Dwi Kurniawan Kurniawan]]>;
<![CDATA[Richardus Eko Indrajit]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.468
<![CDATA[The industrial revolution is a fundamental change in the way of life and human work processes, where advances in information technology can integrate the life of the digital world. The right digital marketing and still paying attention to the lifestyle of every community is a success in achieving the desired marketing target, including in the world of the trade industry where various innovations continue to emerge. This study aims to determine the effect of Digital Marketing and Lifestyle on the Decisions of E-Commerce Users (Shopee) in Industry 4.0. This research was conducted with a quantitative research design where the data source is secondary data in the form of E-Commerce user data. Data were collected through questionnaires and analyzed by multiple linear regression with classical assumptions, namely Normality Test, Multicollinearity Test, Heteroscedasticity Test. The capital feasibility test is the coefficient of determination, F test and t test. Based on the results of the classical assumption test, it shows that Digital Marketing and Lifestyle have a positive and significant effect on the dependent variable on the decisions of E-Commerce users at Shopee. By using a multiple regression analysis system, the results show that Digital Marketing and Lifestyle have a partial effect on the decisions of E-Commerce users with the Digital Marketing variable value tcount 1,988 > ttable 1.660 and tcount Lifestyle tcount 2.479 > ttable 1.660. The results showed that the Digital Marketing and Lifestyle variables simultaneously had a positive effect on the decisions of E-Commerce users by 1.3% and 98.7% with a 95% confidence level explained by other variables outside the study.]]>
<![CDATA[COMPARISON OF FEATURE SELECTION TO PERFORMANCE IMPROVEMENT OF K-NEAREST NEIGHBOR ALGORITHM IN DATA CLASSIFICATION ]]>
<![CDATA[Iswanto Iswanto]]>;
<![CDATA[Tulus Tulus]]>;
<![CDATA[Poltak Poltak]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.471
<![CDATA[One of the most widely used data classification methods is the K-Nearest Neighbor (K-NN) algorithm. Classification of data in this method is carried out based on the calculation of the closest distance to the training data as much as the value of K from its neighbors. Then the new data class is determined using the most votes system from the number of K nearest neighbors. However, the performance of this method is still lower than other data classification methods. The cause is the use of the most voting system in determining new data classes and the influence of features less relevant to the dataset. This study compares several feature selection methods in the data set to see their effects on the performance of the K-NN algorithm in data classification. The feature selection methods in this research are Information gain, Gain ratio, and Gini index. The method was tested on the Water Quality dataset from the Kaggle Repository to see the most optimal feature selection method. The test results on the dataset show that the use of the feature selection method affects to increase the performance of the K-NN algorithm. The average increase in the accuracy value obtained from the value of K=1 to K=15 is the Information Gain increased by 1.17%, Gain ratio increased by 0.69%, and the Gini index increased by 1.19%. The highest accuracy value in the classification of the Water Quality dataset is 89.66% at K=13 with the Information Gain feature selection method.]]>
<![CDATA[ALGORITHM COMPARISON AND FEATURE SELECTION FOR CLASSIFICATION OF BROILER CHICKEN HARVEST ]]>
<![CDATA[Christian Cahyaningtyas]]>;
<![CDATA[Danny Manongga]]>;
<![CDATA[Irwan Sembiring]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.6.493
<![CDATA[Broiler chickens are the result of superior breeds that produce a lot of meat. In practice, however, many breeders experience crop failure, which has a serious impact on the economy and can also affect farmer quality, resulting in sanctions. The value of the performance index produced at harvest indicates the success rate of harvesting broiler chickens. Broiler crop yield data can be used to help classify broiler crop yield data using an approach method. The CRISP-DM (Cross Industry Standard Process for Data Mining) method was used in this study's data mining technique. This study compares 3 classification algorithms to determine the best algorithm and 3 feature selection methods to determine the best method for improving algorithm performance. According to the findings of this study, the Random Forest algorithm is the best algorithm for classifying harvest data, with an accuracy rate of 89.14 percent. The best way to improve the algorithm's performance is to use the Backward Elimination method, which can increase the accuracy by 7.53 percent. As a result, the Random Forest + Backward Elimination algorithm yields an accuracy value of 96.67 percent. According to this study, the factors that influence crop yield increase are FCR, number of harvests, and body weight.]]>
