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Journal : EIGEN MATHEMATICS JOURNAL

Penerapan aritmatika modulo untuk menguji validitas dan mengembangkan nomor ISBN (International Standard Book Number) Lukman Ibrahim; Syamsul Bahri; Irwansyah -
Eigen Mathematics Journal In Press Desember 2018
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.902 KB) | DOI: 10.29303/emj.v2i2.18

Abstract

International Standard Book Number or ISBN is a code that contains information about the title, the publisher, the different types of materials for making the book, and publisher group from a book. The ISBN code of a book along with its development need to be checked for validity, because the more books are published, the more chance the book will be copied so that it has a double ISBN number. This research show that the use of modulo arithmetic in arranging ISBN for a book, especially ISBN-10 and ISBN-13. In this research too discussed about validation ISBN-10 and ISBN-13 using modulo arithmetic and expanded by developing an ISBN-n, for a natural number n greater than 10. Validation will be carried out in two stages, namely manually using modulo arithmetic calculation and then computing, by compiling java-based application to validate an ISBN. The development of ISBN-n for n ∊ ℕ and n ≥ 11 use the advantages of ISBN-10 and ISBN-13 and (Memorandum of Understanding/MoU) ISBN agency. Case studies in the Department of Library and Archives of West Nusa Tenggara Province on the ISBN validity of additional collection books for the 2015-2016 period showed that the ISBN validity of these books is 96%.
Perbandingan Metode Classification and Regression Trees (CART) dengan Naïve Bayes Classification (NBC) dalam Klasifikasi Status Gizi Balita di Kelurahan Pagesangan Barat Nurul - Insan; Mustika Hadijati; Irwansyah Irwansyah
Eigen Mathematics Journal Vol. 3 No. 1 Juni 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i1.68

Abstract

This study aims to compare the Classification and Regression Trees (CART) and Naïve Bayes Classification (NBC) methods in classifying the nutritional status of toddlers in West Pagesangan by looking at their accuracy and also knowing the variables that influence the classification of toddler nutritional status. The data used in this study were toddlers who come to the posyandu in May 2019, with predictor variables used namely gender, ages, weight, mother’s employment status, mother’s education level, number of children and parents income. The result showed that Naïve Bayes Classification (NBC) is better in classifying the nutritional status of toddlers in West Pagesangan than Classification and Regression Trees (CART). This can be seen from the accuracy values obtained with three comparisons of training data and testing data. In the comparison of 90% of training data: 10% of testing data, obtained an accuracy value of 90% for NBC and 85% for CART, in the comparison of 80% of training data: 20% of testing data, obtained an accuracy value 0f 82.5% for NBC and 80% for CART, while in comparison 70% traing data : 30% testing data, obtained an accuracy value 72% for NBC and 70%for CART. This study also showed that significant variables the classification of nutritional status of toddlers in West Pagesangan village are age, gender, weight and parents income.
Pipeline Network Optimization using Hybrid Algorithm between Simulated Annealing and Genetic Algorithms Parizal Hidayatullah; Irwansyah Irwansyah; Qurratul Aini; Bulqis Nebula Syechah
Eigen Mathematics Journal Vol. 4 No. 2 Desember 2021
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v4i2.100

Abstract

The pipeline network is one of the most complex optimization problems consisting of several elements: reservoirs, pipes, valves, etc. The pipeline network is designed to deliver water to consumers by considering the demand and adequate pressure on the water pipe network. The main problem in designing reliable pipelines is the cost. The amount of cost that most influences the design of pipelines is the diameter of the pipe used. Therefore, this study aims to combine (hybrid) simulated annealing algorithm with genetic algorithm to optimize water pipe networks. The simulated annealing algorithm is the main algorithm in finding the optimal cost.Meanwhile, the genetic algorithm will assist in the pipeline update process using the roulette wheel selection. Simulation data is used to test the hybrid algorithm performance compared to the standard simulated annealing algorithm. The results show that the simulated annealing hybrid algorithm is able to get a more optimal cost in designing a water pipe network compared to the standard simulated annealing algorithm. Keywords: Optimization, Epanet 2.0, Simulated Annealing, and Genetic Algorithm
Jaringan Syaraf Tiruan untuk Memprediksi Kadar Polutan Ozon di Kota Mataram Nurul Hikmah; Syamsul Bahri; Irwansyah Irwansyah
Eigen Mathematics Journal Vol. 5 No. 2 Desember 2022
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v5i2.129

Abstract

Ozone tropospher (O3) is one of the pollutants in the environment of Mataram City, Lombok, NTB, Indonesia. Based on the data obtained from the Agency of Environment and Forestry of West Nusa Tenggara Province, ozone pollutant concentrations in Mataram City have changed unpredictably. One time pollutant concentrations increase and then decrease, but then quickly increase again significantly. Therefore, the concentrations of ozone pollutant must be monitored because its presence at certain levels can cause various negative effects human health and the environment. Changes in ozone pollutant concentrations can be identified by carrying out a method of predicting ozone pollutant levels so that a decision can be taken to prevent the negative impact of the pollutant. In this research, a backpropagation artificial neural network is used to find the model prediction of the concentration of ozone in Mataram City. The input variables that are used in this network are air temperature (x_1 ), wind direction (x_2 ), wind speed (x_3 ), humidity (x_4 ), solar radiation (x_5 ), concentration of NO2 (x_6 ), the concentration of SO2 (x_7 ) and the concentration of O3 a day before (x_8 ) for the period of 6 July 2018 to 31 May 2019. The method in this study was to conduct trial and error on 60 different combinations of network architectures and parameters. Then all the network architectures performance will be compared based on the RMSE, MAPE and R2 indicators. Based on this research, the best neural network model to predict the concentration of ozone pollutant in Mataram City is the network with architecture 8-20-1, with logsig-purelin activation function and trainlm learning function. The performance of the training model is RMSE=0.011, MAPE = 1,043 % and R^2=0,9566. Meanwhile, the performance of the testing model is RMSE=0.001, MAPE = 0.749 % and R^2=0.497