International Journal of Quantitative Research and Modeling
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
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<![CDATA[Fuzzy Decision Tree to Predict Student Success in Their Studies ]]>
<![CDATA[Heri Bambang Santoso]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.59
<![CDATA[The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.]]>
<![CDATA[Data Cleansing Strategies on Data Sets Become Data Science ]]>
<![CDATA[Sardjono Sardjono]]>;
<![CDATA[R. Yadi Rakhman Alamsyah]]>;
<![CDATA[Marwondo Marwondo]]>;
<![CDATA[Elia Setiana]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.71
<![CDATA[The digital era very grows up with the increasing using of smartphone and many organization or companies was implemented of a system to support their business. That is who will increase the volume of usage and dissemination of data, neither through open nor closed internet networks. Because there is the need to process large data and how to get it from different store resource, so requirement strategy to process the data according to the rule of good, effective and efficient in activity data cleansing until the data set can be use as mature and very useful information for their business purpose. By using the R languaged who can process large data and has data complexity for the data loaded from different storage resource can be done as well as. To using R languaged maximally, so we have to a basic skill that needed to process the data set which will be used to be data scient for organizations or companies by good data cleansing techniques. In this research on Data Cleansing Strategies on data set owned by organizations,will describe the correct step by step to obtaining data that very useful to be uses as data science for organization so by the data that generated after the data cleansing process is very meaningful and useful for making decisions, other than that this research give basic overview and guide to the beginner all data scientists by doing data cleansing in the way stages and also provides a way to analyze from the result of execution some functions used.]]>
<![CDATA[Design Multi Input Automatic Identifier System Class B for Indonesian Fishery ]]>
<![CDATA[A Sumarudin]]>;
<![CDATA[Willy Permana Putra]]>;
<![CDATA[Ahmad Rifai]]>;
<![CDATA[Agfianto Eko Putra]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.19
<![CDATA[The need for technology that can help Indonesian fishermen is needed to improve fishing yields. Based on BPS data Indonesian fishermen dropped from 1.7 million to 64 thousand in 2013. This is due to several factors, including unfavorable professions for fishermen. This is insufficient fishing equipment for fishing. One of them is the need for navigation tools that are easily understood by traditional fishermen and in accordance with the needs in fishing.we propose the design of Class B's Automatic Identifier System (AIS) to help fishermen navigate and find fish points. In this system we also propose determining the point of fish based on information from fishermen who are collectively collected by the fishermen. With this design it is hoped that the fish point can be shared with other fishermen. The result this design, it is expected to be able to assist fishermen in navigating using AIS so that fisherman security with AIS system with multiple sensor can be improved find the point of fish obtained from sending data from several fishermen and collect data weather for safety.]]>
<![CDATA[On Generalization of Fibonacci, Lucas and Mulatu Numbers ]]>
<![CDATA[Agung Prabowo]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.65
<![CDATA[Fibonacci numbers, Lucas numbers and Mulatu numbers are built in the same method. The three numbers differ in the first term, while the second term is entirely the same. The next terms are the sum of two successive terms. In this article, generalizations of Fibonacci, Lucas and Mulatu (GFLM) numbers are built which are generalizations of the three types of numbers. The Binet formula is then built for the GFLM numbers, and determines the golden ratio, silver ratio and Bronze ratio of the GFLM numbers. This article also presents generalizations of these three types of ratios, called Metallic ratios. In the last part we state the Metallic ratio in the form of continued fraction and nested radicals.]]>
<![CDATA[Usability Testing on Android-based KMS for Pregnant Women using the USE Questionnaire ]]>
<![CDATA[Halimah Tus Sadiah]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.61
<![CDATA[An Android-based medicines knowledge management system (KMS) application has been built as a result of a research about the usage of medicines on pregnant women. Usability testing is needed to be used to measure the success rate of the implementation of this mobile application. In this study, the Usefulness, Satisfaction and Ease of Use (USE) Questionnaire method is used, with quantitative and qualitative analysis. Based on the test results, it shows that the KMS application for pregnant women has a high usability result, with each component score, namely Usefulness reaches 86%, Ease of Use of 86%, and Satisfaction of 84%. On average, 85% indicates that the Android-based medicines KMS application for pregnant women has high quality attributes in ease of use. In addition, this application already has criteria to meet the needs of users, especially pregnant women, to easily find information and knowledge about medicines that are safe to consume during pregnancy to relieve pain or pregnancy complaints.]]>
<![CDATA[Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection ]]>
<![CDATA[Budiman Budiman]]>;
<![CDATA[Reni Nursyanti]]>;
<![CDATA[R Yadi Rakhman Alamsyah]]>;
<![CDATA[Imannudin Akbar]]>
<![CDATA[ International Journal of Quantitative Research and Modeling Vol 1, No 3 (2020) ]]>
Publisher : <![CDATA[Research Collaboration Community (RCC) ]]>
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DOI: 10.46336/ijqrm.v1i3.72
<![CDATA[Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.]]>
