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All Journal Inferensi Mathematics and Applications (MAp) Journal
Qonita Qurrota A'yun
Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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INVERS MOORE-PENROSE SEBAGAI REPRESENTASI MATRIKS PROYEKSI ORTHOGONAL Sri Wigantono; Moh. Nurul Huda; Qonita Qurrota A'yun; Hardina Sandariria; Dimas Raditya Sahputra; Tuhfatul Janan
MAp (Mathematics and Applications) Journal Vol 5, No 1 (2023)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v5i1.6187

Abstract

The inverse of matrix is one of the important properties of matrix. This properies, especially singular matrix, has been developed by Moore and continued by Penrose. Then, this inverse called Moore-Penrose inverse. The Moore-Penrose invers criteria can represent a projection on a vector space V along W with V and W are orthogonal to each other or can written with W=V^⊥ which is called orthogonal projection matrix on V. This research will present lemmas and theorems related to the Moore-Penrose invers construction of the multiplication matrix. Then, a square matrix is an orthogonal projection matrix on a vector space V if and only if it satisfies two conditions, that are idempotent and symmetric. These two properties are satisfied by matrices I-A^+ A and I-AA^+ which respectively are orthogonal projection matrices on Ker(A) and Ker(A^' ). As a result, the Moore-Penrose inverse A^+ can be constructed from a square matrix A which is an multiplication of several matrices and fulfills certain properties.
Aplikasi Model ARIMAX dengan Efek Variasi Kalender untuk Peramalan Trend Pencarian Kata Kunci “Zalora” pada Data Google Trends Andrea Tri Rian Dani; Sri Wahyuningsih; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sri Wigantono; Hardina Sandariria; Qonita Qurrota A'yun; Muhammad Aldani Zen
Inferensi Vol 6, No 2 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i2.15793

Abstract

ARIMAX is a method in time series analysis that is used to model an event by adding exogenous variables as additional information. Currently, the ARIMAX model can be applied to time series data that has calendar variation effects. In short, calendar variations occur due to changes in the composition of the calendar. The purpose of this study is to apply the ARIMAX model with the effects of calendar variations to forecast search trends for the keyword "Zalora". Data were collected starting from January 2018 to November 2022 in the form of a weekly series. Based on the results of the analysis, the ARIMAX model is obtained with calendar variation effects with ARIMA residuals (1,1,1). Forecasting accuracy using the Mean Absolute Percentage Error (MAPE) of 10.47%. Forecasting results for the next 24 periods tend to fluctuate and it is estimated that in April 2023 there will be an increase in search trends for the keyword "Zalora".
Co-Authors Dani, Andrea Tri Rian Dimas Raditya Sahputra Fachrian Bimantoro Putra Hardina Sandariria Meirinda Fauziyah Moh. Nurul Huda Muhammad Aldani Zen Sri Wahyuningsih Sri Wigantono Tuhfatul Janan