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<![CDATA[IMPLEMENTATION TECHNOLOGY ACCEPTANCE MODEL (TAM) ON ACCEPTANCE OF THE ZOOM APPLICATION IN ONLINE LEARNING ]]>
<![CDATA[Ahmad Faisal]]>;
<![CDATA[Frisma Handayanna]]>;
<![CDATA[Indah Purnamasari]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.53
<![CDATA[Abstract The application of online learning in various educational institutions in the Covid-19 Pandemic has had an impact on behavioral attitudes where many educators and students have also complained about the limited technology facilities, operations, and internet networks in some areas or quotas to access online learning. Followed by the popularity of the Zoom application in supporting education, the authors researched the application of the Technology Acceptance Model (TAM) to the acceptance of the Zoom application in online learning to determine the effect of using applications in online learning with 4 variables accompanied by multiple linear regression hypothesis testing, F-test and T-test. using SPSS. The test results in Perceived Usefulness, Perceived Ease of Use, and Behavioral Intention to Use affect Actual System Usage with significant and positive results of 20.21. Behavioral Intention to Use is more dominant than other variables with a value of 5.31, while the lowest is Perceived Ease of Use with a value of (-0.50).]]>
<![CDATA[INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLYING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS ]]>
<![CDATA[Kristiawan Nugroho]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.54
<![CDATA[Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study.]]>
<![CDATA[A STUDY OF SIRIP DESA AT CITERAS, LEBAK, BANTEN AN ARCHIEVAL INFORMATION SYSTEM ]]>
<![CDATA[Ria Astriratma]]>;
<![CDATA[Helena Nurramdhani Irmanda]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.55
<![CDATA[This study aims to create an archiving information system that can be used to store data held by government officials in Citeras Village as needed. The research stage was preceded by collecting data archives / files contained in the Citeras Village Village. Then the data is processed to be processed in the system. The data obtained is modeled according to system requirements. Then implemented into a web-based system. The output of this research is the availability of an archiving information system as a data storage tool in accordance with the needs of the web-based Citeras Village government device using the SDLC (System Development Life Cycle) Waterfall method. Database design using MySQL and the programming language used is PHP. The result of this research is the availability of a web-based archiving information system for population data in Citeras Village which can then be used by Citeras Village officials, Rangkasbitung District, Lebak Regency, Banten.]]>
<![CDATA[WATER INTAKE APPLET BASED ON HUMAN EXCREMENT ]]>
<![CDATA[Nadine Swastika]]>;
<![CDATA[Winda Hasuki]]>;
<![CDATA[Sava Savero]]>;
<![CDATA[Putri Gabriella Satya]]>;
<![CDATA[Arli Aditya Parikesit]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.56
<![CDATA[To function properly, the human body requires adequate hydration as 70% of the human body was being built up by water. It acts as a solvent for lots of biochemical reactions to keep our physiological function work optimally throughout the day. Dehydration could cause a disturbance in both the gastrointestinal and kidney systems of human beings, and it could be noticed in the color of both urine and feces. The objective of this paper is to present an idea on how to make a water intake app based on the color indicator of human excrement surveillance. It might be a solution to intensify people's ability to become self-conscious when drinking less water by surveying the excreted substance. The deployed method is to measure one indicator which is between urine and feces detector on how much water should the user drink by observing the color of both their urine and feces. However, both excrement indicators are needed to detect users' drinking amount. If one of these indicators shows a bad result, it could lead to water intoxication or hydration. The application has been successfully created using Python to give feedback for the user's water intake based on the condition of their excreted substance. The Water Intake application has successfully shown a clear indicator for dehydration. It could be inferred that this water intake software could detect the dehydration phenomenon with human excrement as the main indicator.]]>
<![CDATA[IMPLEMENTATION OF GENETIC ALGORITHM IN THE CURRENT SCHEDULING SYSTEM : ]]>
<![CDATA[Pateh Ulum]]>;
<![CDATA[Desti Fitriati]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.57
<![CDATA[Scheduling courses is a routine job in academic activities at a college. In its implementation, the scheduling process is not easy to do because many factors need to be considered, several factors that are considered, such as the willingness of lecturers to teach, the availability of classrooms. Besides that, it is also necessary to pay attention to the number of classes in each subject. Course scheduling is a combination of courses, days, time, lecture space, and consideration of lecturers' willingness to teach. To solve the course scheduling problem, a system that can handle the scheduling process is needed. The method that can be used to solve this problem is to use the Genetic Algorithm approach. The genetic algorithm is a scheduling algorithm that can combine lecture time and space automatically by applying a natural or gene selection system. Based on the research that has been done, the genetic algorithm can solve scheduling problems quickly, which only takes 15 seconds for 78 classes and uses as many as 16 chromosomes. Also, the fitness value of all chromosomes is 0, this means that the scheduling results obtained are optimal.]]>
<![CDATA[RESTAURANT DENSITY PREDICTION SYSTEM USING FEED FORWARD NEURAL NETWORK ]]>
<![CDATA[Muhammad Kurnia Sandi]]>;
<![CDATA[Anggunmeka Luhur Prasasti]]>;
<![CDATA[Marisa W. Paryasto]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.58
<![CDATA[In this day and age, information about something is so important. The level of trust of modern society depends on the testing of information. Tested and accurate information will have a good impact on the community. One of the important but often missed information is information about the density of a restaurant. Information about restaurant density is important to know because it can affect the actions of someone who will visit the restaurant. This information is also useful to provide information in advance so that diners avoid full restaurants to avoid the spread of the Covid-19 virus, among other things. With limited operating hours as well as the number of restaurant visitors, information about the density of a restaurant becomes much needed. The lack of information on restaurant density is a major problem in the community. The needs of the community, made this study aims to predict the density of a restaurant an hour later. Based on survey data and existing literature data, with simulation methods and also system analysis built using feedforward neural network artificial intelligence architecture and then trained with Backpropagation algorithms produced an accuracy of 97.8% with literature data.]]>
<![CDATA[THE BLACK BOX TESTING AND LOC METHOD APPROACH IN TESTING AND STREAMLINING THE PATIENT REGISTRATION PROGRAM ]]>
<![CDATA[Joosten Joosten]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.59
<![CDATA[Good software can be used if there is proper testing. The testing phase is quite important because the software needs to be tested before it is used by end users. In making software for animal hospitals there is no validation and verification so testing is needed. This study used information on the registration section of veterinary hospital patients and was tested by three Black Box Testing methods, namely Equivalence Class Partitioning (ECP), Boundary Value Analysis (BVA), and Decision Table plus the LOC approach. The test results of the three methods are that the percentage of invalid ECPs is greater than the valid ones, so the input value limit needs to be changed again. Then for BVA testing, the percentage of valid is higher than invalid. In the decision table, a shortening rule is made between operating services and other services so that it produces inpatient status and down payment automatically without choose it again and is tested again by the decision table by matching the estimation results of the two services.]]>
<![CDATA[SMART SYSTEM FOR AUTOMATIC CROP AND RECOGNITION PLAT NUMBER ]]>
<![CDATA[Desti Fitriati]]>;
<![CDATA[Nira Ravika Pasha]]>;
<![CDATA[Bambang Hariyanto]]>;
<![CDATA[Amir Murtako]]>;
<![CDATA[Sri Rezeki Candra Nursari]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.60
<![CDATA[Based on data from the Central Statistics Agency in 2018, it was written that the number of motorbikes for the Indonesian region was 120.10 million or 82% and for cars 26.75 million or around 18% of the total population. With the increasing population of motorized vehicle users, it will result in an increase in problems that occur in traffic violations and also the technology security system in the parking system. Most of the existing parking systems still require parking attendants. In addition, the existing system only discusses the opening and closing of bars and providing information on parking lots. Although the existing system already uses artificial intelligence to read plate numbers, the officers are still matching it. Of course this is not effective and efficient because the use of artificial intelligence is not purely done by the system. To overcome this, the solution given in this study is to create a parking system that can read plate numbers automatically and store vehicle entry data directly into the database. The system created can also open and close the door latch automatically. The template matching image processing technique was chosen to solve this problem. Based on the experimental results, the system can recognize plate numbers with an accuracy of 83%. For further research, it is necessary to introduce vehicle ownership and provide parking information so that the parking system becomes more perfect.]]>
<![CDATA[IMPLEMENTATION OF INVENTORY INFORMATION SYSTEM DESIGN USING ECONOMIC ORDER QUANTITY METHOD ]]>
<![CDATA[Frieyadie Frieyadie]]>;
<![CDATA[Tyas Setiyorini]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
Publisher : <![CDATA[Kresnamedia Publisher ]]>
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DOI: 10.34288/jri.v3i2.61
<![CDATA[The research problems faced are among others the cost of ordering goods which always changes every time there is an order. Poor product order data collection and less than optimal handling of product orders can harm the company. To solve the problem of controlling inventory management, the Economic Order Quantity (EOQ) method is used, which is proven to be effective in overcoming these problems. Contribution is generated by building an inventory management information system so that the problems faced are not repeated. The purpose of this study is to make the cost of ordering goods more stable and more optimal in handling product orders]]>
<![CDATA[THE COMPARISON OF VIKOR AND MAUT METHODS IN THE SELECTION OF USED CARS ]]>
<![CDATA[Nurul Rahmadani]]>;
<![CDATA[Risnawati Risnawati]]>
<![CDATA[ Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition ]]>
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<![CDATA[Abstract A used car is a car that has been used by other people. Choosing a used car according to the needs of the buyer is very much a consideration. Used car buyers, of course, make their choices based on several criteria. The criteria for choosing a used car include transmission, price, passenger capacity, luggage capacity, year of manufacture, color, and engine capacity. These criteria are the buyer's consideration in choosing a used car because it is not easy for those who do not understand the criteria for choosing a used car. This research aims to compare the selection of used cars with the VIKOR method (Vise Kriterijumska Optimizajica I Kompromisno Resenje) and MAUT (Multi-Attribute Utility Theory). The VIKOR method is a ranking method using a multicriteria ranking index based on a certain measure of closeness to the ideal solution. Meanwhile, the MAUT method is a multi-attribute method that usually combines measurements of different risks and benefits. The research method used is descriptive research with a quantitative approach. The results of this study can be seen that using the VIKOR and MAUT methods obtained the same results, namely A5 as the selected used car.]]>
