INFOKUM
The INFOKUM a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Software Engineering. Image Processing Datamining Artificial Neural Networks
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<![CDATA[COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS ]]>
<![CDATA[Sastra Wandi Nduru]]>;
<![CDATA[Ronsen Purba]]>;
<![CDATA[Andri]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instance]]>
<![CDATA[EXPERT SYSTEM APPLICATION TO DIAGNOSE ESCHERICHIA COLI (E-COLI) BACTERIA IN REFILLED DRINKING WATER USING THE CERTAINTY FACTOR METHOD ]]>
<![CDATA[Rizky Fauziah]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Sometimes many people do not know whether the water is suitable for consumption or not. The reason is because the water that is consumed daily without going through the cooking or boiling process first.Many of the depots that have sprung up today do not include a letter from the local health office stating that the drinking water sold is fit for consumption. Expensive costs and very difficult affairs make the owners of drinking water depots ignore the most important things that actually must be owned. One of the things that can threaten health through drinking water is the presence of Escherichia Coli (E-Coli) bacteria. To find out whether the drinking water that is consumed contains E-Coli bacteria is not easy, because its size is very small and invisible to the eye. One of the consequences that can be caused by E-Coli bacteria is abdominal pain, vomiting, diarrhea, high blood pressure, and even kidney disorders. Certainty Factor (certainty factor) expresses belief in an event or fact based on evidence or expert judgment. Certainty Factor uses a value to assume the degree of confidence of an expert on a data. Many studies get references to do further research with different problems. Where the certainty factor method solves a problem with the concept of belief and disbelief. So that it can be seen whether the certainty factor method can also be used in solving other problems. Where the certainty factor method solves a problem with the concept of belief and disbelief. So that it can be seen whether the certainty factor method can also be used in solving other problems. Where the certainty factor method solves a problem with the concept of belief and disbelief. So that it can be seen whether the certainty factor method can also be used in solving other problems.]]>
<![CDATA[COMBINATION OF LOGISTIC REGRESSION AND SVM ALGORITHM WITH HYBRID PSO AND GA BASED SELECTION FEATURE IN CORONARY HEART DISEASE CLASSIFICATION ]]>
<![CDATA[Sutrisno Situmorang]]>;
<![CDATA[Pahala Sirait]]>;
<![CDATA[Andri]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[The world's high death rate from heart disease requires early prevention by medical doctors to diagnose heart disease early. The machine learning approach makes it possible to predict the risk of developing heart disease by examining certain values at a low cost. This study will contribute to the development of a combination of Logistic Regression and SVM models that integrate SVM and Logistic Regression algorithms by implementing selection features using hybrid PSO and GA methods. The combination concept of Logistic Regression SVM (LRSVM) applied to this study is to reduce the risk of SVM output errors by interpreting and modifying the output of SVM classifiers by the results of Logistic Regression analysis. The test results showed that LRSVM with pso-GA hybrid-based selection feature achieved better performance for coronary heart disease classification with 99.66% accuracy compared to classification accuracy with SVM algorithm without selection feature]]>
<![CDATA[KNN METHOD ON CREDIT RISK CLASSIFICATION WITH BINARY PARTICLE SWARM OPTIMIZATION BASED FEATURE SELECTION ]]>
<![CDATA[Harmoko Lubis]]>;
<![CDATA[Pahala Sirait]]>;
<![CDATA[Arwin Halim]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Today, classification performance has become increasingly important for credit risk assessment for loss control and revenue maximization. Therefore, a classification method is required that can accurately and efficiently measure the credit risk level of prospective borrowers as the key to the credit approval process. This study contributes to the development of feature selection methods with SI algorithms that use binary representation, namely feature selection using PSO algorithms with binary representation or Binary Particle Swarm Optimization (BPSO) applied to credit risk classification, with classification evaluation using kNN classification method. The application of feature selection is done to eliminate excessive features, thus reducing the number of features, improving the accuracy of the model, and reducing running time. The test results showed that KNN's best accuracy of 76.40%, can be improved by bpso-based selection feature with better accuracy of 88.70%, with an accuracy improvement of 13.35%. This test showed that bpso-based selection feature technique successfully improved the accuracy of KNN classification on credit risk classification.]]>
<![CDATA[FACE IMAGE RETRIEVAL SYSTEM USING COMBINATION METHOD OF SELF ORGANIZING MAP AND NORMALIZED CROSS CORRELATION ]]>
<![CDATA[Amir Saleh]]>;
<![CDATA[Diky Suryandy]]>;
<![CDATA[Jesron Nainggolan]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Content based image retrieval (CBIR) is one method in computer vision that is widely applied in various fields of life. In this study, two algorithms will be combined, namely self organizing map (SOM) and normalized cross correlation (NCC) to test the method in the face image retrieval system. The SOM algorithm is used to perform learning on the system created and the NCC method is used to calculate the proximity value between the input image and the image contained in the database to be displayed as the result of image retrieval. The test results in the proposed research show good results with an accuracy rate of face image retrieval of 93.62%. This percentage is higher than using the usual SOM method with an accuracy rate of face image retrieval of 91.62%.]]>
<![CDATA[CONTROL SMARTHOME DISTANCE CONTROL BASED FUZZY LOGIC ]]>
<![CDATA[Despaleri Perangin-Angin]]>;
<![CDATA[Gusman Jaya Ndruru]]>;
<![CDATA[Thomson Purba]]>;
<![CDATA[Amos Marulitua Butar Butar]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[SmartHome (smart home) is a part of IoT where all everyday objects or furniture that are familiar with people's lives are “smarted” due to the integration of technology in the form of a versatile microcontroller. In terms of Smart Home, IoT will be present in the form of items that are often found in ordinary people's homes. This research was conducted to develop a system that can assist in performing operations on electronic equipment used daily at home with a smartphone as its control. This research includes the stages of the prototype and system development method that will be developed using the Arduino Uno ATMEGA328 microcontroller hardware, NodeMCU (ESP8266), Bluetooth as the main control system connected to the internet network using a wireless router and as software for control of the system designed using Android-based application, namely the MIT Application Inventor open source application. In conducting experiments to show that this control system is able to work properly as expected so that users can turn off and on electronic devices such as lights, fans and others.]]>
<![CDATA[HEALTHY SMART DOOR BASED ON BODY TEMPERATURE USING ARDUINO UNO AND FUZZY LOGIC ]]>
<![CDATA[Fadhillah Azmi]]>;
<![CDATA[Gopas Pasaribu]]>;
<![CDATA[Rizki Imanuel]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Since the end of 2019, the spread of Corona Virus Disease (COVID-19) has always shown an increase from time to time, this is due to the rise of physical contact, both directly between humans and through contact with equipment or public facilities. Automated public facilities and early detection of humans who have the potential to spread disease are effective ways to prevent physical contact between the spreader and other humans. Body temperature is one indicator that shows how the human body is and its ability to generate or reduce heat in the body. Based on the information obtained, the normal human temperature is in the range of 36.5-37.20C, whereas if it is above that temperature a person can be said to have a fever, where fever is a symptom of COVID-19. However, the human body temperature is also relatively fluctuating depending on activities and environmental conditions. For this reason, a method that makes it easier to analyze body temperature based on grouping is used, namely the fuzzy logic method which is implemented into the Arduino Uno microcontroller as an automatic control tool]]>
<![CDATA[DECISION SUPPORT SYSTEM FOR DETERMINATION OF VOCATIONAL SCHOOL DEPARTMENT GKPS-3 PEMATANG SIANTAR USING WEB-BASED WEIGHT PRODUCT METHOD ]]>
<![CDATA[Lane Ertika Matanari]]>;
<![CDATA[Arifin Tua Purba]]>;
<![CDATA[Calen Calen]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Following applicable standards throughout Indonesia, prospective vocational students who will register for school will experience major selection (majors). For this reason, prospective students are expected to be able to assess their interests, talents, and abilities so that they do not choose the wrong department to take. The selection of majors for prospective vocational students is the beginning of future career choices, but many prospective students choose majors because of the influence of friends. The role of parents is very important. The direction available at SMK GKPS 3 includes accounting, office administration, and software engineering. The assignment will be adjusted to the student's academic abilities and interests. The purpose of these majors is so that students can be directed in receiving lessons that are following the abilities and talents possessed by students. To assist in the process of determining the direction, a decision support system (SPK) was designed with the Weight Product (WP) method. The WP method is done by using multiplication to relate the attribute rating, where the rating of each attribute must first be ranked with the attribute's weight. Based on the data sample studied, the DSS is designed to operate properly and correctly and is 100% accurate because the results of the comparative evaluation results of manual analysis and the overall analysis of the DSS are appropriate.]]>
<![CDATA[FEASIBILITY DECISION SUPPORT SYSTEM FOR RECIPIENTS OF BPNT (NON CASH FOOD ASSISTANCE) USING SAW METHOD AT BANE PEMATANGSIANTAR KELURAHAN WEB-BASED ]]>
<![CDATA[Eduard Sihombing]]>;
<![CDATA[Viktor Siregar]]>;
<![CDATA[Novendra Damanik]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[Non-Cash Food Assistance (BPNT) is a program organized by the government in tackling poverty and food needs. The BPNT program aims to reduce expenses and provide more balanced nutrition to Beneficiary Families (KPM). However, in the implementation of the BPNT program, there are still technical problems, including the determination of the wrong target for BPNT recipients. To make it easier for relevant officials to determine prospective BPNT recipients, the authors create a decision support system using the Simple Additive Weighting method where this system produces a ranking as a recommendation for residents who are entitled to receive BPNT through the calculation of the weighting criteria of each alternative. It is hoped that this application can provide recommendations for prospective BPNT recipients that are more targeted.]]>
<![CDATA[WEB DESIGN BASED ONLINE MANAGEMENT SYSTEM AT 3 MEDAN JUNIOR HIGH SCHOOL ]]>
<![CDATA[Siti Aisyah]]>;
<![CDATA[Widia Sari Zebua]]>;
<![CDATA[Rupiah Boru Tambunan]]>;
<![CDATA[Lina Sari Damanik]]>;
<![CDATA[Rut Julianti Hutagalung]]>;
<![CDATA[Matthew Thompson]]>
<![CDATA[ INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence ]]>
Publisher : <![CDATA[Sean Institute ]]>
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<![CDATA[This research is entitled Design of Web Based Online Management System at Nasrani 3 Medan Junior High School. The purpose of this research is to design a support system in order to assist prospective students in the reegistration process for Nasrani 3 Medan Junior High School. The method used in this research is a descriptive research method and the waterfall method in the system development methodology used. The result of this research indicate the following conclusions; 1. The system at Nasrani 3 Medan Junior High School is still done manually so that the system built can accelerate academic data processing. 2. The system built can be carried out efficiently on academic performance so that the time required is not too long. 3. All students data, teachers, tuition fees, and student grades are stored in the designed system.]]>
