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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Mandalika Mathematics and Educations Journal Inferensi
Meirinda Fauziyah
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Indonesia
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".
K-Means Algorithm for Grouping Provinces in Indonesia Based on Macroeconomic and Criminality Indicators Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sifriyani Sifriyani; Suyitno Suyitno; M Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.12-21

Abstract

Cluster analysis is a method in multivariate analysis to group n observations into K groups (K ≤ n) based on their characteristics. One of the well-known algorithms in cluster analysis is K-Means. K-Means uses the non-hierarchical principle where at the initial initiation, it is necessary to determine the number of groups in advance. The K-Means algorithm can be applied to classify provinces in Indonesia based on macroeconomic indicators (percentage of poor people, open unemployment rate, and Gini ratio) and crime rate (Crime rate). The ultimate goal of this research is of course to get optimal grouping results. The similarity measure used is Euclidean Distance. The number of groups tested K=2,3,4,…,10 and the optimal number of groups with the highest Silhouette value was selected. Based on the results of the analysis, the optimal number of clusters is four. These four clusters have characteristics that distinguish one cluster from another.
Identification of Spatial Autocorrelation in Cases of Complaints About Illegal Online Loans in Indonesia Meirinda Fauziyah; Devita Dwi Putri; Andrea Tri Rian Dani; Hardina Sandariria; Nazmi Soraya
Mandalika Mathematics and Educations Journal Vol 7 No 4 (2025): Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i4.10572

Abstract

The development of digital financial services has made it easier for people to access technology-based loans, but this has also been accompanied by an increase in illegal online lending practices, which have generated numerous complaints from consumers in various regions. This situation indicates the potential for uneven distribution of cases across regions. This study aims to identify interregional spatial relationships in data on the number of complaints about illegal online loans across provinces in Indonesia. This study uses data on the number of complaints about illegal online loans from January to June 2025. The analytical methods used include descriptive statistics, the Moran Index to measure global spatial autocorrelation, and the Local Indicator of Spatial Autocorrelation (LISA) to detect local clusters. The results show positive spatial autocorrelation, where provinces with a high number of complaints tend to be close to each other, particularly areas with high levels of urbanization and digital activity such as DKI Jakarta, West Java, Central Java, and Banten. Thus, the distribution of complaints about illegal online loans is not random but is influenced by geographic proximity and regional socioeconomic characteristics.
Co-Authors Andrea Tri Rian Dani Dani, Andrea Tri Rian Devita Dwi Putri Fachrian Bimantoro Putra Fachrian Bimantoro Putra Hardina Sandariria M. Fathurahman Muhammad Aldani Zen Nazmi Soraya Qonita Qurrota A'yun Sifriyani Sifriyani Sri Wahyuningsih Sri Wigantono Suyitno Suyitno