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All Journal BIMASTER BAREKENG: Jurnal Ilmu Matematika dan Terapan
Fikadila, Lisa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL Huda, Nur'ainul Miftahul; Fran, Fransiskus; Yundari, Yundari; Fikadila, Lisa; Safitri, Fauziah
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 (663.366 KB) | DOI: 10.30598/barekengvol17iss1pp0263-0274

Abstract

The Generalized Space-Time Autoregressive (GSTAR) model is able to utilize modeling of both space and time simultaneously. The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. In this research, a modified weight matrix is presented using the Minimum Spanning Tree approach of graph theory. Prim's algorithm is utilized for calculation here. Not only does the modified weight matrix depend distance, but also highlights the correlation. It makes the modified weight matrix unique. Before starting Prim's algorithm, the correlation is first utilized as an input in forming the initial graph. Following that, find the graph with the least of MST weight. Afterwards, the graph is described utilizing weight matrix, which is applied to the normalization process. Following this, the GSTAR(1;1) modelling process is carried out, beginning with estimating the parameters and then forecasting. The case study is Covid-19 cases that occurred on Java Island between July 2020 (when early Covid-19 entered Indonesia) and the beginning of January 2021. The aim of the research is to model the Covid-19 cases using modified weights and to predict the following five times. The outcome is a GSTAR(1;1) model with modified weights can captures both temporal and spatial patterns. The accuracy of the model is achieved for both the training data and the testing data by the MAPE computations, which yielded of 11.40% and 21.57%, respectively. Predictions are also obtained for each province in the next five times.
PENENTUAN BANYAKNYA POHON PERENTANG MENGGUNAKAN TEOREMA POHON MATRIKS Fikadila, Lisa; Kusumastuti, Nilamsari; Pasaribu, Meliana
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 2 (2024): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v13i2.77239

Abstract

Setiap graf terhubung G pasti memuat pohon perentang T, yaitu subgraf dari G yang berupa pohon dan memuat semua titik G. Banyaknya pohon perentang dari graf G adalah berhingga. Dalam penelitian ini dibahas terkait penentuan banyaknya pohon perentang dari graf G dengan menggunakan teorema pohon matriks. Suatu graf bisa direpresentasikan menjadi bentuk matriks, seperti matriks derajat (D), matriks ketetanggaan (A), dan matriks Laplacian (L). Tujuan dari penelitian ini ialah untuk menganalisis matriks Laplacian (L) dan membuktikan teorema pohon matriks. Matriks L adalah selisih antara matriks D dan A dengan matriks D dan A ialah matriks hasil representasi dari graf G. Matriks L ini dapat digunakan pada teorema pohon matriks untuk mencari banyaknya pohon perentang dari graf G, yaitu dengan mencari nilai sebarang kofaktor dari matriks L. Pada penelitian ini dapat disimpulkan bahwa teorema pohon matriks bisa digunakan untuk mencari banyaknya pohon perentang dari graf G dengan graf G merupakan graf sederhana terhubung dan graf tak berarah, sehingga banyaknya pohon perentang dari graf G ialah sama dengan nilai sebarang kofaktor dari matriks L.  Kata Kunci : representasi graf, matriks Laplacian, kofaktor.
Co-Authors Fran, Fransiskus Huda, Nur'ainul Miftahul Meliana Pasaribu Nilamsari Kusumastuti Safitri, Fauziah Yundari, Yundari