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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika UNP Journal of Statistics and Data Science d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Statistics and Its Applications
Zamahsary Martha
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Forecasting Export Values in West Sumatra Using Backpropagation Neural Network Rahmawati, Desi; Martha, Zamahsary
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12199

Abstract

Export value is an important indicator in supporting regional economic growth. However, its movement tends to be volatile and non-linear, making it difficult to forecast using conventional statistical methods such as ARIMA. This study aims to forecast the export value of West Sumatra Province using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The data used consist of monthly export values from January 2006 to October 2025 obtained from Badan Pusat Statistik (BPS) of West Sumatra Province. The data were normalized and modified using the rolling window method, then divided into training and testing datasets. Several network architectures were evaluated through a trial-and-error process with variations in the number of neurons in the hidden layer. The best model was achieved with the BPNN(12,12,1) architecture, yielding a Mean Square Error (MSE) of 0.0236 and a Mean Absolute Percentage Error (MAPE) of 25.31%. The results indicate that the model is capable of capturing non-linear patterns and reasonably following the trend of the actual data. The selected model was then used to perform short-term forecasting of export values for the period from November 2025 to March 2026. The findings demonstrate that the Backpropagation Neural Network algorithm is effective for forecasting export values in West Sumatra Province. This study contributes theoretically by enriching the application of artificial intelligence in regional economic forecasting and practically by supporting data-driven policy formulation for export strategies in West Sumatra.
Comparison of K-Means and Ward Methods in Clustering Indonesian Provinces Based on Household Basic Service Access Mulya, Nurul; Fajri Juli Rahman Nur Zendrato; Muhammad Arief Rivano; Zamahsary Martha; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/449

Abstract

Disparities in household basic service access across provinces in Indonesia remain a key issue in regional development. Basic services such as access to improved drinking water, proper sanitation, electricity, and adequate housing are essential indicators of household welfare, making regional classification necessary to identify similarities and disparities among provinces. This study aims to cluster Indonesian provinces based on household basic service access indicators and to compare the performance of the K-Means method and Hierarchical Clustering using the Ward approach. The analysis was conducted using numerical data with Euclidean distance as a measure of similarity. The optimal number of clusters was determined using the Silhouette plot and further validated using the Silhouette Coefficient. The results indicate that both K-Means and Ward methods produce two optimal clusters representing provinces with relatively high and relatively low levels of household basic service access. Centroid analysis reveals clear differences between clusters across all indicators, particularly in electricity access and sanitation. Furthermore, the evaluation of clustering quality shows that the Ward method yields a higher Silhouette Coefficient than the K-Means method, indicating more compact clusters and better separation between clusters. Therefore, the Ward method is considered more effective in mapping patterns of household basic service access across provinces. The findings of this study can support regional planning by providing a clearer understanding of disparities in household basic service access in Indonesia.
Co-Authors 01, Riska Admi Salma Admi Salma Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani april leniati Arssita Nur Muharromah Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Carina, Fadhillah Meisya Desi Rahmawati Dina Fitria Dina Fitria, Dina Dinul Haq, Asra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Fajri Juli Rahman Nur Zendrato Febiola Putri, Febi Fitri, Fadhilah fitri, silfia wisa Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Lubis, Riskiani Maharani, Listia Maulidya Hernanda Muhammad Arief Rivano Muharromah, Arssita Nur Mukhti, Tessy Octavia Mukthi, Tessy Octavia Mulya, Nurul Nadha Ovella Syaqhasdy Nanda P, Muhamad Rayhan Natasya Dwi Ovalingga, natasyalinggaa Nazifatul Azizah Nini Erdiani Nonong Amalita Novi, Yoli Marda Oktaviani, Bernadita Permana, Dony Putra, Dio Afdal Rahma, Dzakyyah Rahmad Wanizal Pastha Rahmanesta, Frandito Raihan Athaya Wudd Rizki Amalia, Annisa Salma, Admi Suherman, Suherman Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yonggi, Yonggi Septa Pramadia Zilrahmi, Zilrahmi