Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Computing Science and Applied Mathematics Al-Muqayyad
Rustam Efendi
Universitas Islam Riau
Computer Science & IT Economics, Econometrics & Finance Education Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
On the Reciprocal Sums of Generalized Fibonacci-Like Sequence Musraini M.; Rustam Efendi; Endang Lily; Noor El Goldameir; Verrel Rievaldo Wijaya
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 9, No 1 (2023)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v9i1.7895

Abstract

The Fibonacci and Lucas sequences have been generalized in many ways, some by preserving the initial conditions, and others by preserving the recurrence relation. One of them is defined by the relation B_n = B_{n−1} + B_{n−2}, n >= 2 with the initial condition B_0 = 2s, B_1 = s + 1 where s in Z. In this paper, we consider the reciprocal sums of B_n and B^2_n, with an established result that also involve Bn.
Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen Anne Mudya Yolanda; Arisman Adnan; Rustam Efendi; Haposan Sirait; Irfansyah Irfansyah; Okta Bella Syuhada; Rahmad Ramadhan Laska; Riko Febrian
AL-Muqayyad Vol. 5 No. 2 (2022): Al-Muqayyad
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/jam.v5i2.766

Abstract

Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%
Co-Authors Anne Mudya Yolanda Arisman Adnan Endang Lily Haposan Sirait Irfansyah Irfansyah Musraini M Noor El Goldameir Okta Bella Syuhada Rahmad Ramadhan Laska Riko Febrian Verrel Rievaldo Wijaya