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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNP Journal of Statistics and Data Science
Tessy Octavia Mukhti
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Social Sciences

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Journal : UNP Journal of Statistics and Data Science

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Forecasting Smallholder Oil Palm Yield in Riau Province through the SARIMA Approach Septrina Kiki Arisandi; Dony Permana; 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-iss1/436

Abstract

Oil palm stands as one of Indonesia’s major agricultural sectors that plays a vital role in regional economic growth, particularly within Riau Province. However, its production often fluctuates due to seasonal and environmental factors, making accurate forecasting essential for planning and policy formulation. This study aims to forecast smallholder oil palm production in Riau Province through the Seasonal Autoregressive Integrated Moving Average (SARIMA) Approach. The data consist of monthly oil palm production from January 2006 to December 2023 obtained from the Central Bureau of Statistics (BPS) of Riau Province. The modeling process includes identifying the model structure, estimating parameters, performing diagnostic checks, and evaluating forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The best model selected was SARIMA (2,0,0)(0,1,1)[12] with an AIC value of 4980.12 and a MAPE of 11.27%, indicating a good level of accuracy. The model effectively captured both seasonal and long-term trend patterns in production. The forecast results suggest that peak production typically occurs in August–September, while the lowest occurs in February–March. The study concludes that the SARIMA model provides a robust statistical framework for predicting oil palm production and can be applied as a decision-support tool in agricultural and economic planning for the province
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 Adinda Putri Aditya, Muhammad Fadhil Aditya Admi Salma Afifah Nabilah AL Rezki Ivansyah Andini Diva Luthfiyah Anggara, Rudi Annisa Ramadhani Ardhi, Sonia Arssita Nur Muharromah Atus Amadi Putra Azizah Apriyerni Baehaqi Berliana Nofriadi Bimbim Oktaviandi Bunga Miftahul Barokah Bunga Nafandra defal aditya defran Dewi Febiyanti Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Ervi Dayana Putri Fadhilah Fitri Fajri Juli Rahman Nur Zendrato fajriyanti nur, Putri Fauzan Arrahman Fenni Kurnia Mutiya Fikra, Hidayatul Fitri, Fadhilah Fitri, Fitri Hayati Hariati Ainun Nisa Herlena Purnama Sari Ihsan Dermawan Inna Auliya kartika, anice Martia Rosada Muhammad Arief Rivano Mulya, Nurul Nikma Hasanah Nonong Amalita Novi, Yoli Marda Nurul Mulya Syahwa Olga Afrilly Putri Putri Yeni, Dicha Rahmatul Annisa Rahmika Alya Rivani, Putri RR. Ella Evrita Hestiandari Sari Agustin Sepniza Nasywa Septrina Kiki Arisandi Siregar, Fauzan Al-Hamdani Siti Nurhaliza Sri Wahyuni Syafriandi Syafriandi Wafiq Alya Aufa Yenni Kurniawati Yoli Marda Novi Yonggi, Yonggi Septa Pramadia Zamahsary Martha Zilrahmi, Zilrahmi