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INDONESIA
EIGEN MATHEMATICS JOURNAL
Published by Universitas Mataram
ISSN : 26153599     EISSN : 26153270     DOI : -
Core Subject : Education,
Mathematics
Eigen Mathematics Journal mempublikasikan artikel yang berkontribusi pada informasi baru atau pengetahuan baru terkait Matematika, Statistika, dan Aplikasinya. Selain itu, jurnal ini juga mempublikasikan artikel berbentuk survey dalam rangka memperkenalkan perkembangan terbaru dan memotivasi penelitian selanjutnya dalam bidang matematika, statistika, dan aplikasinya.
Arjuna Subject : -
Articles 8 Documents
 1 
Search results for , issue "VOL. 3 NO. 2 DESEMBER 2020" : 8 Documents clear
Peramalan Penjualan Kendaraan Mobil Segmen B2B dengan Metode Regresi Linear Berganda, Jaringan Saraf Tiruan dan Jaringan Saraf Tiruan – Algoritma Genetika Muhammad Agung Nugraha; Farizal Farizal; Djoko Sihono Gabriel
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.80

Abstract

This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.
Peramalan Jumlah Siswa Baru Madrasah Aliyah (MA) Manhalul Ma’arif Darek-Lombok Tengah Lisa Harsyiah; Nurul Fitriyani; Salwa Salwa
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.88

Abstract

This study aimed to forecast the new student number at Madrasah Aliyah (MA) Manhalul Ma'arif Darek. The data used in this study was the annual time series data of new students who enrolled in the school, from the 1998/1999 academic year to 2016/2017. Based on the data obtained, it shows that the number of new students who enroll in Madrasah Aliyah (MA) Manhalul Ma'arif Darek tends to fluctuate. This fluctuating pattern is a problem faced by Madrasah Aliyah (MA) Manhalul Ma'arif Darek in determining strategic and policy steps related to planning the provision of school facilities / infrastructure. Therefore we need a forecasting method in accordance with the data pattern. The forecasting method used is the Fuzzy Time Series Cheng method. This method uses fuzzy principles as the basis of the forecasting process. The forecasting process results obtained the Mean Square Error (MSE) value of 101.5009 and the Mean Absolute Percentage Error (MAPE) value of 18.49%. The results showed that the Fuzzy Time Series Cheng method performed well in predicting the number of new students at Madrasah Aliyah (MA) Manhalul Ma'arif Darek.
Penerapan Metode Hungarian dalam Penugasan Dosen Pengampu Mata Kuliah Program Studi Matematika FMIPA Universitas Mataram Muhammad Khairurradziqin; Ahmad Tedi Ruslan; Dzakiyatul Mardliyah; Fahmi Handika; Mamika Ujianita Romdhini
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.63

Abstract

The tight schedule of lecture activities requires accuracy so that it always runs smoothly. Lecturer assignments play an important role to ensure the smooth lecture activities. Problems that often occur in the assignment of these lecturers need to be avoided. In an effort to reduce the risk of problems that occur in the assignment of lecturers, it is necessary to make a structured system with the right method. Hungarian method can be said very appropriate for this assignment problem because each course will only be charged to one lecturer. Another advantage of using the hungarian method in this lecturer assignment model is also because it uses the preferences of prospective lecturers as subjects of measurement. Each lecturer will take courses according to their best preferences with the expectation that the lecturer will have more mastery in the courses that he will teach.
Algoritma Needleman-Wunsch dalam Menentukan Tingkat Kemiripan Urutan DNA Rusa Timor (Cervus timorensis) dan Rusa Merah (Cervus elaphus) Hibban Kholiq; Mamika Ujianita Romdhini; Marliadi Susanto
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.65

Abstract

Sequence alignment is a basic method in sequence analysis. This method is used to determine the similaritiy level of DNA sequences. The Needleman-Wunsch algorithm is an algorithm that can be used to solve the problem of sequence alignment. This research shows that the relation T (i, j) used in the Needleman-Wunsch algorithm is a function where T: (ℕ0 ℕ0) → ℤ. The function T (i, j) is a recursive function. Moreover, DNA sequence data used are DNA sequences from the Timor Deer, which are the identities of the provinces of West Nusa Tenggara and Red Deer, which are typical deer from the European continent as a comparison. The DNA sequence data was obtained from BLAST (Basic Local Alignment Search Tool). Based on the alignment, the most optimal alignment is obtained by forming 666 base pairs sequences with 322 matches, 230 missmatches and 114 gaps, meaning that the two DNA sequences have a 48% similarity (322/666).
Regresi Nonparametrik Kernel Gaussian pada Pemodelan Angka Kelahiran Kasar di Provinsi Nusa Tenggara Barat Deni Pratiwi; Lalu Abd Azis Mursy; Muhammad Rizaldi; Nurul Fitriyani
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.78

Abstract

This study aims to model Crude Birth Rates (CBR) in West Nusa Tenggara Province. The nonparametric regression method was used in this research by considering data distribution patterns that do not show a linear relationship between variables. In this case, the kernel nonparametric regression using the Gaussian function and the Nadaraya-Watson estimator. The results showed optimal bandwidths of 0.55542837, 1.29042927, 0.94706041, and 0.92278896 with a value of minimum Generalized Cross-Validation (GCV) of 0.000000000432613511, which was minimized by the simulated annealing algorithm. The resulting model's accuracy can be seen from the coefficient of determination (R2) of 99.23% and the Mean Absolute Percentage Error (MAPE) of 0.007049%.
Penerapan Model Vector Autoregressive Integrate Moving Average dalam Peramalan Laju Inflasi dan Suku Bunga di Indonesia Jusmawati Jusmawati; Mustika Hadijati; Nurul Fitriyani
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.62

Abstract

The inflation and interest rates in Indonesia have a significant impact on the country's economic development. Indonesian inflation and interest rates data are multivariate time series data that show activity over a certain period of time. Vector Autoregressive Integrated Moving Average (VARIMA) is a method for analyzing multivariate time series data. This method is a simultaneous equation modeling that has several endogenous variables simultaneously. This study aimed to model the inflation and interest rates data, from January 2009 to December 2016 and predict inflation and interest rates by using VARIMA method. The model obtained was the VARIMA(0,2,2) model, with estimated parameters using the maximum likelihood method. The choice of the VARIMA(0,2,2) model was based on the smallest AIC value of -4,2891, with a MAPE value for the inflation and interest rates forecasting were 6,04% and 1,84%, respectively, which indicates a very good forecast results.
Optimasi Penyaluran Bahan Bakar Minyak di Wilayah Maluku Indonesia Rini Dian Sari; Farizal Farizal; Djoko Sihono Gabriel
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.81

Abstract

Fossil fuel (BBM) is a vital commodity and has a strategic value for people's lives. On the demand side, the need for BBM tends to increase along with the increasing energy demand for people's lives. Therefore, the distribution system for BBM has to be optimized in order to fulfill the people’s demand. The aim of this paper is to optimize vehicle route for BBM distribution in Maluku so that it has minimum cost, distance, and time. The optimization method in this paper is Mix Integer Linear Programming (MILP). Decision variables in this paper are chosen from the most significant variables for BBM distribution in Maluku.
Pengembangan Motif Batik Sasambo dengan Sistem Lindenmayer Subaiah Marlina; Qurratul Aini; I Wayan Sudiarta
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.77

Abstract

Batik is an Indonesian intangible cultural heritage that needs to be preserved. Indonesia has a variety of batik patterns such as Sasambo (Sasak, Samawa, and Mbojo) batik patterns from West Nusa Tenggara. This study aims to develop Sasambo batik patterns by using the Lindenmayer System (L-System). The Sasambo batik patterns developed in this study are of two types: Lumbung (rice barn) and Kangkung (water spinach) patterns. By applying the L-System, there are three different ways that can be done, namely (1) making the Sasambo batik patterns as axioms, (2) making the batik patterns as generators, and (3) combining the Sasambo patterns with fractals. Based on the new patterns produced using the L-System, the selection of generators should have the initial and final segments which are both in a single line position, so that the original patterns unchanged.

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