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Muhammad Aldani Zen
Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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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".
Aplikasi Pengelompokan Data Runtun Waktu dengan Algoritma K-Medoids Muhammad Aldani Zen; Sri Wahyuningsih; Andrea Tri Rian Dani
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.v6i2.15864

Abstract

The development of information technology will always be accompanied by the storage and accumulation of massive quantities of digital information. Cluster analysis is one of many data processing problems that require the selection of an appropriate algorithm when dealing with large data sets. Cluster analysis is a collection of techniques for dividing a set of observation objects into clusters. Cluster analysis is applicable to time series data, the processing of which differs slightly from that of cross-section data. Clustering time series is a technique for processing multivariable time series data. K-Medoids is the clustering algorithm used for time series clustering. The objective of this study is to obtain optimal K-values in determining the number of clusters based on silhouette coefficients and grouping outcomes using the K-Medoids algorithm. In this study, the dynamic time-warping distance is utilized as the similarity metric. This study provides cooking oil price data for 34 Indonesian provinces from October 2017 to October 2022. The optimal K value is determined for two clusters based on the results of the analysis, with 19 provinces joining cluster 1, where the cluster with cooking oil prices was below cluster 2 and 15 provinces joining cluster 2 which is the cluster with the highest cooking oil prices.
Co-Authors Dani, Andrea Tri Rian Fachrian Bimantoro Putra Hardina Sandariria Meirinda Fauziyah Qonita Qurrota A'yun Sri Wahyuningsih Sri Wigantono