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All Journal Prosiding Seminar Nasional Official Statistics Journal of Data Insights
Angelina, Lea
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Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences

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Forecasting Honda Car Retail Sales Using the Seasonal Autoregressive Integrated Moving Average Method: Peramalan Penjualan Retail Mobil Honda Menggunakan Metode Seasonal Autoregressive Integrated Moving Average Angelina, Lea; Permata, Alia; Arsusma, Jesicha; Masichah, Firochul; Al Haris, M.; Fathoni Amri, Ihsan
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.416

Abstract

This article discusses the forecasting of Honda car retail sales using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The study aims to forecast Honda car retail sales for the upcoming year. Various SARIMA models have been tested to determine the best model, and the results show that the SARIMA (1,1,0)(1,1,1)¹² model provides the lowest Mean Absolute Percentage Error (MAPE) among all tested models, which is 17,74%. Therefore, this model was chosen for forecasting sales over the next 12 months. The forecast results are expected to assist management in making optimal decisions regarding stock and marketing, as well as significantly enhancing operational efficiency and customer satisfaction in the future.
Klasterisasi Indikator Kesehatan Ibu dan Anak di Indonesia Menggunakan Hierarchical Clustering Agglomerative Angelina, Lea; Putri, Dinda Meyda; Ana, Nisfatun Nurul; Syafira, Elsa Izza; Chumairoh, Kamilah Citra; Syaharani, Nabbila Dyah; Fauzi, Fatkhurokhman
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2401

Abstract

Maternal and child health is a top priority in national development, given that high maternal and infant mortality rates remain a significant challenge in Indonesia. Disparities in health indicators between regions indicate that existing inequalities remain insufficiently addressed. This study aims to group 38 provinces in Indonesia based on 14 maternal and child health indicators for 2024 to identify patterns of disparity. The method used is Agglomerative Hierarchical Clustering with multicollinearity tests (VIF) and KMO for data validation. The complete linkage method was selected for its optimal performance, yielding an agglomerative coefficient of 0.742 and the highest silhouette value of 0.2099 at K = 6. The results formed six clusters reflecting similarities in regional characteristics. Several provinces in Papua clustered separately due to their low health indicator achievements. These findings emphasize the need for region-specific intervention policies to address disparities and promote equitable improvements in maternal and child health.
Co-Authors Ana, Nisfatun Nurul Arsusma, Jesicha Chumairoh, Kamilah Citra Fauzi, Fatkhurokhman Ihsan Fathoni Amri M. Al Haris Masichah, Firochul Permata, Alia Putri, Dinda Meyda Syafira, Elsa Izza Syaharani, Nabbila Dyah