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INFOKUM
Published by SEAN INSTITUTE
ISSN : 23029706     EISSN : 27224635     DOI : -
Core Subject : Science,
Computer Science & IT
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 72 Documents
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Search results for , issue "Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence" : 72 Documents clear
COMBINATION OF K-MEANS CLUSTERING AND K-NEAREST NEIGHBOR ON ECOMMERCE CUSTOMER SPENDING RATE PREDICTION Boni Octaviana
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

K-Nearest Neighbor is a classification method that classifies new data into specific classes based on the proximity of characteristics to k members of existing classes. K-Nearest Neighbor relies heavily on training data. In actual circumstances such as the ecommerce customer spending rate dataset, there is no class label for each data. So that to be able to obtain datatraining required additional methods need to be added before the prediction process can be done. This research attempts to use K-Means Clustering to group datasets into multiple clusters which then each cluster will be given a class label according to the centroid characteristics of those clusters. The combination of KNN and K-Means Clustering methods in customer's spending rate predictions gives a fairly good result, where the accuracy of the prediction obtained is 89.6%.
Application of Multi-Objecttive Optimazation on the basis of Ratio Analysis in Determining Monthly Study Agenda onMasjid Al-Muhajirin Rumah Pondok Mansion Saidi Ramadan Siregar; Pristiwanto Pristiwanto
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Pondok Mansion House (RPM) which is located in the Namorambe sub-district, Deli Serdang Regency is one of the government subsidized housing held by PT Rapy Ray. The construction of the mosque which has been completed provides good news for the Muslim residents of the housing because they can carry out routine prayers and various other Islamic holiday activities. On Saturday, June 19, 2021, a mosque committee was formed before the establishment of the Mosque Prosperity Agency. Then after the mosque committee was formed. The mosque committee held a meeting to find the name of the mosque and to complete the management of the mosque which was held on July 7, 2021. To make the mosque agenda, it was discussed again by inviting members of the management which coincided on August 3, 2021. Then the results of the meeting were agreed upon by the mosque management and the problem that occurs is the emergence of disagreements or disagreements on one of the mosque's agenda schedules resulting in small talks in the housing complex area which can result in disorganization between mosque administrators and individuals who do not agree with the agenda. It is necessary to make a policy in preparing a schedule with a scientific system based on mathematical calculations so that the results can provide explanations and can be accepted with grace. Of course the well-known system in this case is the Decision Support System (DSS) in which this system provides suggestions, input, as well as contributions to organizational actors, associations whose nature is to choose the best among several available options. Then the results will provide solutions in the form of approaches with alternative systems, ratings and several components related to DSS. The value of the approach or result that will be given later reaches 80% to 95%..
Testing C4.5 Algorithm Using Rapid Miner Applications In Determining Customer Satisfaction Levels Fithrie Soufitri; Ellanda Purwawijaya; Eka Hayana Hasibuan; Roy Nuary Singarimbun
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Data mining is a series of processes to extract added value in the form of information that has not been known manually from a database. The resulting information is obtained by extracting and recognizing important or interesting patterns from the data contained in the database. One part of the service of an agency is the service to customers, customers or those related to certain services. The quality of service is assessed by what has been done and how to treat from those who serve, some provisions used to ensure service optimization are Confiden, Integrity, Pride and Passion with the main purpose or output is Customer Satisfaction. The process of Forming a Pattern of Satisfaction Level by Utilizing the C4.5 Algorithm Penelitain process is carried out by data collection, testing with applications, exposure of pattern results or Knowledge. Pola yang terbentuk after the extraction is 1.Integrity = low: quite satisfied {very satisfied=0, quite satisfied=3}Integrity = tall, Passion = low: quite satisfied {very satisfied =0, quite satisfied =2}, Passion = tall: very satisfied {very satisfied =11, quite satisfied =0}.
IMPLEMENTATION OF TF-IDF AND COSINE SIMILARITY ALGORITHMS FOR CLASSIFICATION OF DOCUMENTS BASED ON ABSTRACT SCIENTIFIC JOURNALS Paska Marto Hasugian; Jonson Manurung; Logaraz Logaraz; Uzitha Ram
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Research on one of the higher education dharmas is carried out by each lecturer and is a challenge for lecturers who pay attention to produce new and useful findings. Research results will be published in journals both nationally and internationally and one of the websites published by Ristekbirn is Sinta which includes all research works in Indonesia. The problem in this research is the accumulation of data that is getting bigger and it needs to be analyzed by utilizing text mining by searching for the resources contained in the abstract document and presenting part of the information. The purpose of this study is to classify the suitability of another document so that knowledge is found. and placement in groups according to existing topics. The process of these problems is by classifying documents based on abstracts from the publication of scientific papers. Solving these problems involves two mutually supporting algorithms, namely TD-IDF with Cosine Similarity with different tasks. TF-IDF ensures the weight of each document that can be read and read with Cosine Similarity. This research uses text mining as part of the search for related patterns and documents that have been tested. For the process of calculating the test data, 1 document and 15 documents were used as training data. With the calculation of TD-IDF the weight of each document from Q, D2 to D15 is 10,946, 28,050,27,176, 39,043, 36,535, 30,696, 25,612, 12,581, 42,335, 29,661, 33,867, 31,706, 22,654, 15,450, 59,832, 42,127, The similarity of the data is tested by determining the value of k = 4 which results in similarity to the Expert System and Cryptography, while with the selection of K = 5 with the highest similarity to the expert system..
Linear Regression Analysis To Predict The Length Of Thesis Completion Fristi Riandari; Hengki Tamando Sihotang
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Students who carry out knowledge in the undergraduate program will certainly be faced with the preparation of a thesis at the end of their study period. However, every year students still find it takes longer than the time specified in completing their thesis. This is caused by several things, such as students who are working, working hours that do not support the implementation of thesis preparation, students who already have families and other factors. This of course makes universities have to prepare special strategies in order to reduce the number of students who cannot complete their thesis on time in the future, one of which is with a decision support. This can be done by utilizing university big data. Prediction of the length of time for completion of college student thesis can be done by utilizing data mining and a simple linear regression approach. Using 1 independent variable, namely the average inhibiting factor (Working Status, Working Hours, Work Sip, Guidance Media, Status) (X1) and the number of days of thesis completion being the dependent variable (Y). After looking for the regression value of b and constant a, then the simple linear regression equation model is: Y = 280.450 + 1.650 X.
BIT CHECK IN ERROR DETECTION ON TEXT DATA TRANSMISSION USING HAMMING CODE ALGORITHM Pilipus Tarigan
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

When data or information is transmitted via wireless or via cable channels, errors may occur while the data is transmitted. One of the efforts made is to apply error control coding. Hamming code is an example of an existing error control coding technique. Hamming code performance is distinguished by the number of parity bits it has. Ontelecommunications allows everyone to communicate with each other quickly over long distances though. Data that is transmitted or sent in the form of text data can fail (error). Errors cause changes in the contents of the data transferred to the recipient (Receiver) to change or fail. One way to detect simple errors is to use Hamming Code with single error correction. In the detection, this algorithm uses the EX-OR (Exclusive–OR) operation in the error detection process.In testing the data sent is not the same as the result received, the bit has experienced an error, and the system will correct the position where the bit has an error.
PERFORMANCE ASSESSMENT DECISION SUPPORT SYSTEM FOR MEDICOM'S BEST EMPLOYEE DETERMINATION WITH FUZZY SUGENO METHOD Jontinus Manullang; Sartika Dewi Purba
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Performance appraisal is important for every employee and useful for the company to determine the next policy action, At Medicom performance appraisal is used to process promotions, performance evaluations, and determining employee achievements, while the variables used are technical ability, conceptual ability and interpersonal relations, with application fuzzy logic in spk with the Sugeno method will make it easier for foundations to determine the best employees, the results show this decision support system can help foundations get decent employees to be the best employees
THE CONCEPT OF APPLICATION OF MACHINE LEARNING IN THE ENVIRONMENT INTERNET OF THINGS Sulindawaty Sulindawaty; Jijon R Sagala; Penda Sudarto Hasugian
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Machine Learning is an application of computers and mathematical algorithms adopted by means of learning that comes from data and produces predictions in the future. The learning process in question is an attempt to acquire intelligence through two stages, including training and testing. The Internet of Things is a network that can connect anything in the supply chain, including people, machines and systems, where efficient supply chain management is guaranteed. This is done through visualizing any object/thing in the supply chain by monitoring, tracking and providing a third dimension to organizational data, that if analyzed can improve all supply chain processes. In the IoT environment, Machine Learning is very suitable to be applied which can provide many benefits including Resolving Data Inefficiency Problems, Automating Business Processes, Visualizing Supply Chain Management (Supply Chain), Risk Management and Maximizing Profits. By implementing IoT and Machine Learning, of course, it can fulfill business opportunities, namely: process optimization, speed optimization, adaptability optimization and reliability optimization
THE ANIMATION OF ANTICIPATION SIMULATION OF FLOOD DISASTER BASED ON INFOGRAPHIC (CASE STUDY OF WATERSHED IN MEDAN JOHOR REGION, MEDAN CITY) Junus Sinuraya; Hikmah Adwin Adam
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Flood disaster is a natural phenomenon caused by the natural process and uncontrolled human activities in exploiting nature. The natural process depends on rainfall conditions, groundwater systems (geohydrology), geological structure, rock types, geomorphology, and topography. Meanwhile, human activities mean behaviors in exploiting nature for human welfare, that tend to damage the environment, particularly at a watershed, with high intensity, less control, and oversteps of the spatial planning rules. It has been known that flood disaster brings a big loss, for example, the physical loss is estimated more than a billion rupiah and it has not included tangible losses (or invaluable losses), such as plague, the time loss of social activity, and so forth. It is expected to minimize the loss by flood anticipation from government and society who are prone to getting the flood impact, for example, the settlement at the watershed. This research aims to design and make an infographic of flood anticipation simulation based on animation and multimedia for giving education to the society who are often getting the flood impact, like the settlement at the watershed, so that the physical and intangible losses can be minimized
CASE BASED REASONING FOR HANDLING FINAL STUDENT GRADUATION PROBLEMS AT STMIK PELITA NUSANTARA Petti Indrayati Sijabat; Endra AP Marpaung
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

Handling the graduation of problematic final students at STMIK Pelita Nusantara is very much needed during this pandemic considering the problems that were passed in 2020 and many students did not graduate on time due to many problems such as academic problems, study programs and finances. The number of problematic final student problems is caused by quite a number of cases. Examples are students who do not attend lectures according to the curriculum schedule that should be followed, including non-active students for several semesters, students who fail to exceed the minimum standards set by the study program, students do not pay tuition administration fees and students do not know or are not active to get good information academically. and students do not complete the thesis within the stipulated time. The problem is that many students do not graduate on time due to many problems such as academic problems, study programs and finances. This problem is like a student who does not attend lectures for 1 year but can still continue his lectures which happened in the final semester. From the research conducted, it is expected to produce a decision support system that is able to become a medium of information for students and study programs to disseminate information on academic sanctions for students who commit violations

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