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All Journal Journal of the Indonesian Mathematical Society BAREKENG: Jurnal Ilmu Matematika dan Terapan Bisma: Jurnal Bisnis dan Manajemen JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Rachel Wulan Nirmalasari Wijaya
Prodi Matematika, Fakultas Sains Dan Matematika, Universitas Kristen Satya Wacana Salatiga
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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PERBANDINGAN PENGARUH PROMOTION MIX TERHADAP KEPUTUSAN PENGGUNAAN DIGITAL WALLET PADA E-MARKETPLACE TOKOPEDIA DAN SHOPEE I Gede Wisnu Satria Chandra Putra; Rachel Wulan Nirmalasari Wijaya; Devina Tasya Noverin
BISMA: Jurnal Bisnis dan Manajemen Vol 16 No 1 (2022)
Publisher : Jurusan Manajemen Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bisma.v16i1.23972

Abstract

This research was conducted to find effective strategies that influence decisions on using digital wallets on e-marketplaces. In this case, the authors compare the influence of the promotion mix factor carried out by the Tokopedia and Shopee marketplaces to see which promotional strategies are effective for driving decisions on using digital wallets. This research was conducted by using statistical testing methods on the data from the questionnaire collected from 155 respondents. As a result, it was found that both Tokopedia and Shopee both had a promotion mix that had a significant effect on the use of digital wallet services. It's just that when compared, Shopee's promotion mix is ​​more effective than Tokopedia’s because they excel in two aspects, namely sales promotion and direct marketing. Meanwhile, Tokopedia only excels in the advertising aspect. Keywords: decision of use, digital wallet, e-marketplace, financial technology, promotion mix.
Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing Lutfi Sivana Ihzaniah; Adi Setiawan; Rachel Wulan N. Wijaya
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.18948

Abstract

This research was made in order to see which method  performance is better between the KNN (K-Nearest Neighbor) regression method and the multiple linear regression method on Boston Housing data. The method performace referred here is MAE, RMSE, MAPE, and R2. The KNN method is a method to predict something based on the closest training examples of an object. Meanwhile, multiple linear regression is a forecasting technique involving more than one independent variable. The comparison of the two methods is based on the results of the Mean Absolute Percent Error (MAPE). In this research the definitions of distance used are Euclidean distance and Minkowski distance. The K value in the KNN method defines the number of nearest neighbors to be examined to determine the value of a dependent variable, in this research we use K values from 1 to 10 for each test data and definition of distance. In this research, the percentage of test data used was 20%, 30%, and 40% for both methods. The best MAPE value obtained by the KNN regression method was 12,89% at K = 3 for Euclidean distance and 13,22% at K = 3 for Minkowski distance. Meanwhile the best MAPE value for the multiple linear regression method is 17,17%. The best method between the two methods is the KNN regression method as seen from the MAPE value of the KNN regression method which is smaller than the MAPE value of the multiple linear regression method.
Total Edge Irregularity Strength of the Cartesian Product of Bipartite Graphs and Paths Rachel Wulan Nirmalasari Wijaya; Joe Ryan; Thomas Kalinowski
Journal of the Indonesian Mathematical Society VOLUME 29 NUMBER 2 (JULY 2023)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.29.2.1321.156-165

Abstract

For a simple graph G = (V (G), E(G)), a total labeling ∂ is called an edge irregular total k-labeling of G if ∂ : V (G) ∪ E(G) → {1, 2, . . . , k} such that for any two different edges uv and u'v' in E(G), we have wt∂(uv) not equal to wt∂(u'v') where wt∂(uv) = ∂(u) + ∂(v) + ∂(uv). The minimum k for which G has an edge irregulartotal k-labeling is called the total edge irregularity strength, denoted by tes(G). It is known that ceil((|E(G)|+2)/3) is a lower bound for the total edge irregularity strength of a graph G. In this paper we prove that if G is a bipartite graph for which this bound is tight then this is also true for Cartesian product of G with any path.
COMPARISON OF ANN METHOD AND LOGISTIC REGRESSION METHOD ON SINGLE NUCLEOTIDE POLYMORPHISM GENETIC DATA Setiawan, Adi; Wijaya, Rachel Wulan Nirmalasari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.456 KB) | DOI: 10.30598/barekengvol17iss1pp0197-0210

Abstract

This study aims to determine the goodness of classification using the ANN method on Asthma genetic data in the R program package, namely SNPassoc. SNP genetic data was transformed using codominant genetic traits, namely for genetic data AA, AC, CC were given a score of 0, 0.5 and 1, respectively, while CC, CT and TT were scored 0, 0.5 and 1, respectively. The scoring is based on the smallest alphabetical order given a low score. The average accuracy, precision, recall and F1 score were determined using the neural network method if the genetic code was used with variations in the proportion of test data 10%, 20%, 30% and 40% and repeated B = 1000 times. The results obtained were compared with the logistic regression method. If 20% test data is used and the ANN method is used, the accuracy, precision, recall and F1 scores are 0.7756, 0.7844, 0.9844 and 0.8728, respectively. When all information from various countries is used in the Asthma genetic data, the logistic regression method gives higher average accuracy, precision and F1 scores than the ANN method, but the average recall is the opposite. When a separate analysis is performed for each country, the logistic regression method gives higher accuracy, precision, recall and F1 scores in the ANN method compared to the logistic regression method.
Co-Authors Adi Setiawan Devina Tasya Noverin Hapzi Ali I Gede Wisnu Satria Chandra Putra Joe Ryan Lutfi Sivana Ihzaniah Thomas Kalinowski