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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 Imajiner: Jurnal Matematika dan Pendidikan Matematika 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 Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Implementation of Fuzzy C-Means Algorithm for Clustering Provinces in Indonesia Based on Micro and Small Industry Ratio in Village Areas Rahmanesta, Frandito; Martha, Zamahsary; Vionanda, Dodi; Zilrahmi, Zilrahmi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p178-190

Abstract

Post-economic crisis, the micro and small industries contribute the most labor compared to other industries. Regional development sourced from small micro industries is a strategic force in developing a country because the development of small micro industries leads to realizing equitable welfare to reduce income inequality. Development in village areas is an important factor for regional development, reducing inequality between regions, and alleviating poverty. However, based on the 2018 PODES survey, there are regional imbalances in Indonesia in the small micro industry which is centralized on Java Island. Therefore, clustering and characteristics of the province were carried out based on the PODES survey of the small micro industry sector. This research uses the Fuzzy C-Means algorithm to cluster 34 provinces in Indonesia based on the ratio of small micro industries in village areas in 2021, to see how the development of small micro industries in village areas in each province in Indonesia. Fuzzy C-Means is one of the data clustering techniques that uses a fuzzy clustering model, where cluster formation is based on a membership degree value that varies between 0 and 1. The Fuzzy C-Means algorithm generates 4 clusters, cluster 1 and 2 represents provinces with high and very high micro and small industry development in village areas and cluster 3 and 4 represents provinces with medium and low micro and small industry development in village areas. The Fuzzy C-Means algorithm produces a good cluster structure with a silhouette coefficient value of 0,6406.
Robust Spatial Autoregressive (Robust SAR) Modeling in the Case of Poverty Percentage in West Java Novi, Yoli Marda; Tessy Octavia Mukhti; Zamahsary Martha
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.61818

Abstract

Poverty is a complex problem influenced by various economic and social factors, such as the open unemployment rate, the minimum wage, population density, and the school participation rate. This study aims to model the poverty rate in West Java Province by considering spatial effects and the existence of outliers through the application of Spatial Autoregressive (SAR) and Robust Spatial Autoregressive (Robust SAR) models. Based on the Lagrange Multiplier test, the SAR model is declared suitable for use. However, the presence of outliers in the data necessitated the use of a robust approach to obtain more accurate results. The analysis showed that the Robust SAR model had a coefficient of determination of 81.53%, higher than that of the SAR model at 77.48%, making it a better model for explaining variations in poverty levels. Of the four independent variables, only School Participation Rate had a significant effect in both models, where an increase in School Participation Rate contributed to a decrease in the poverty rate. This finding confirms the importance of investment in education as a strategic effort to reduce welfare inequality between regions in West Java.
Application of Area Sampling Frame for Digitizing Household Data in Talawi Mudiak to Support Sustainable Development Goals Syafriandi, Syafriandi; Fitria, Dina; Amalita, Nonong; Kurniawati, Yenni; Permana, Dony; Fitri, Fadhilah; Martha, Zamahsary; Mukhti, Tessy Octavia
Pelita Eksakta Vol 8 No 2 (2025): Pelita Eksakta, Vol. 8, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss2/293

Abstract

Desa Talawi Mudiak menghadapi tantangan dalam pengelolaan data kependudukan. Meskipun mereka telah menyusun RPJMD 2022-2027 yang mengacu pada SDG's, pendataan yang dilakukan masih terbatas pada aspek kependudukan dan demografi. Padahal, pemutkhiran data harus mencakup 17 pilar SDg's agar dapat digunakan sebagai dasar dalam perencanaan pembangunan desa. Selain itu, keterbatasan akses internet dan kurangnya pemanfaatan teknologi informasi juga menjadi kendala pengembangan sistem informasi desa yang lebih komprehensif. Program Studi S1 Statistika hadir dalam menjembatani pencapaian beberapa pilar itu melalui pemutakhiran data hingga dilitalisasinya. Kegiatan diawali dengan pengumpulan data awal, perhitungan kerangka sampling, pelaksanaan survei, dan pemrosesan data pasca survei hingga diperoleh suatu kesimpulan yang dapat digunakan untuk pembangunan desa. Kegiatan melibatkan banyak pihak, mulai dari dosen program studi, perangkat desa, mahasiswa, dan masyarakat. Hasil yang diperoleh berupa data yang mutakhir dan sebuah buku berisikan kondisi Desa Talawi Mudiak tahun 2025.
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.
Vector Autoregressive Exogenous Modelling to Forecast Rice Prices Based on Inflation and Rice Production in West Sumatra Province Khairisa Putri, Nadya; Kurniawati, Yenni; Vionanda, Dodi; Martha, Zamahsary
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 14 No 1 (2026): VOLUME 14 No 1, 2026
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v14i1.66522

Abstract

Rice prices in West Sumatra Province tend to be high despite high production levels, making forecasting essential to support food security. This study aims to forecast rice prices in traditional and modern markets using a Vector Autoregressive with Exogenous Variables (VARX) model, incorporating inflation and rice production as exogenous variables. The data used consists of monthly secondary data covering the period from January 2019 to December 2024, sourced from PIHPS and the West Sumatra Provincial Statistics Agency. The analysis includes the Augmented Dickey-Fuller stationarity test, determination of the optimal lag based on the Akaike Information Criterion, parameter estimation using Ordinary Least Squares, as well as tests of stability, parameter significance, and residual diagnostics. Forecast performance is evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the VARX (3,3) model is the best, with an MAPE of 1.95\% for traditional markets and 1.56\% for modern markets, indicating very high forecasting accuracy. This study demonstrates that incorporating external factors into the VARX model improves rice price forecasting accuracy, providing a basis for the government to formulate policies to maintain food price stability in West Sumatra Province.
Penerapan Agglomerative Hierarchical Clustering Untuk Pengelompokan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Potensi Sambaran Petir Rahmadani, Rahmadani; Martha, Zamahsary
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 8, No 3 (2026): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v8i3.27440

Abstract

Lightning strike are an atmospheric phenomenon that pose significant risk to human safety and infrastucture, partuculary in topical regions sudh as Indonesia, inculding West Sumatra Province. Variations in geographical and environmental conditions contribute to differences in lightning strike potential across districts/municipalities. This study aims to classify districts/municipalities in West Sumatera Province based on their lightning strike potential in order to identify areas requiring higher mitigation priority. The classification was conducted using Agglomerative Hierarchical Clustering with Ward’s method based on three main variables including lightning density, rainfall, and population density. The result indicate that West Sumatera Province can be grouped into 4 cluster of lightning strike potential: low, moderate, high, and very high. The low-potential cluster consists of 6 districts/municipalities, the moderate-potential cluster consists 6 districts/municipalities, the gigh-potential cluster consisits 6 districts/municipalities, and the very high potential cluster contains only 1 municipality. These findings provide a systematic spatial overview of lightning strike potential levels and may serve as a basis for determining regional risk mitigation proirities and infrastructure protection strategies.
Comparison of Estimate Method of Moment and Least Trimmed Squares in Models Robust Regression Tri Wahyuni Nurmulyati; Dony Permana; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): 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/vol2-iss2/176

Abstract

The poverty line is the minimum income that a person must earn to be considered to have a decent standard of living in a particular area. In 2022, the poverty line in West Sumatra Province was higher than the poverty line in Indonesia as a whole. An analysis was conducted to identify the factors influencing the poverty line in West Sumatra Province. However, the observational data on the poverty line and its influencing factors contained outlier. Therefore, robust regression analysis was performed to address the data containing outlier, comparing two estimates: MM estimation and LTS estimation. By examining the value, the best estimate was found to be MM estimation, with significant factors being average net wages/salaries, TPT, APM, and AMH. If the average net wages/salaries, TPT, APM, and AMH increase, the poverty line in West Sumatra will rise. With an of 0.9582, the model can explain 95.82% of the variation in the poverty line, while the remaining variation is explained by other factors not included in the model.
Pengelompokan Potensi Kebakarn Hutan/Lahan di Indonesia Berdasarkan Sebaran Titik Panas Mengunakan Metode CLARANS silfia wisa fitri; Zamahsary Martha; Yenni Kurniawati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/182

Abstract

Kebakaran hutan/lahan merupakan bencana yang sering terjadi di beberapa negara di dunia. Peristiwa ini mendapat perhatian lebih dari pemerintah karena menimbulkan banyak kerugian seperti ekonomi, ekologi dan sosial. Indonesia merupakan negara dengan tingkat bencana kebakaran hutan/lahan yang tinggi, hal ini menjadikan Indonesia sebagai negara penyumbang pencemaran terbesar ketiga di dunia. Sehingga diperlukan upaya penanggulangan sejak dini, salah satu upaya yang dapat dilakukan adalah dengan memanfaatkan data titik api dengan melakukan klasifikasi wilayah yang berpotensi terjadinya kebakaran hutan/lahan. Kebakaran hutan/lahan ditandai dengan terdeteksinya data titik api oleh satelit yang terindikasi sebagai titik api. Pada penelitian ini parameter yang digunakan adalah lintang, bujur, kecerahan, keyakinan dan FRP (fire power radiative) dengan menerapkan metode CLARANS. CLARANS merupakan varian dari algoritma k-medoid dan juga merupakan pengembangan dari algoritma sebelumnya, seperti PAM dan CLARA untuk menangani jumlah data yang lebih besar dan tahan terhadap outlier. Hasil penelitian ini menunjukkan bahwa penggunaan metode CLARANS dapat digunakan untuk proses clustering data hotspot dengan hasil koefisien siluet sebesar 0,896 pada penggunaan 2 cluster dengan jumlah data sebanyak 12,287. Hasil cluster menunjukkan bahwa cluster 1 termasuk dalam potensi tinggi dengan kecerahan rata-rata 340K dengan kepercayaan rata-rata 95% dan cluster 2 termasuk dalam potensi sedang dengan kecerahan rata-rata 327 K.
K-Medoids Cluster Analysis for Grouping Provinces in Indonesia Based on Agricultural Households ST2023 Riska 01; Zamahsary Martha; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/193

Abstract

Agriculture plays a crucial role in Indonesia's national development, providing essential resources such as raw materials, household income, and contributing significantly to Gross Domestik Product (GDP). According to the 2023 Agricultural Census (ST2023), there has been an increase in the number of Agricultural Household Enterprises (RTUP) across various agricultural subsectors. However, the welfare of agricultural entrepreneurs remains low, with 48.68% of poor household heads working in this sector. Therefore, an analysis is needed to understand the patterns and characteristics of RTUPs in each province. This study aims to cluster the provinces in Indonesia based on the number of Agricultural Household Enterprises (RTUP) using K-Medoids cluster analysis. K-Medoids, an extension of K-Means, was chosen for its ability to handle outliers by using medoids as cluster centers instead of means. The research utilized data from the 2023 Agricultural Census, covering 38 provinces and eight variables representing different agricultural subsectors. The optimal number of clusters was determined using the Elbow method, resulting in four distinct clusters. The findings revealed that Cluster 1 consists of 12 provinces with moderate RTUP numbers, Cluster 2 includes 23 provinces with low RTUP numbers, Cluster 3 comprises one province with high RTUP numbers, and Cluster 4 contains two provinces with very high RTUP numbers. The cluster validation using the Davies-Bouldin Index (DBI) yielded a value of 0.722, indicating that the clustering results are optimal.
Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia Yonggi Septa Pramadia Yonggi; Zamahsary Martha; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/194

Abstract

One indicator to determine the economic stability of a country can be seen from the inflation rate of a country. Inflation is an economic symptom in the form of a general increase in prices or a tendency to increase the prices of goods and services in general and continuously. In an effort to anticipate the impact of inflation in the future, an analysis is needed to find out how the development of the inflation rate is by forecasting. Extreme Learning Machine (ELM) is a feed-forward artificial neural network (ANN) algorithm with one hidden layer called Single Hidden Layer Neural Networks (SLFNs). Based on the research, forecasting the inflation rate in Indonesia using the Extreme Learning Machine algorithm obtained the best architecture  (12,48,1) with a MAPE value of 11%. These results show good forecasting because the resulting MAPE is relatively low.
Co-Authors Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani Annisa Rizki Amalia april leniati Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Bunga Nafandra Desi Rahmawati Dina Fitria Dina Fitria Dina Fitria, Dina Dio Afdal Putra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Dzakyyah Rahma Fadhilah Fitri Fadhilah Fitri Fadhillah Meisya Carina Fajri Juli Rahman Nur Zendrato Febi Febiola Putri Fitri, Fadhilah Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Khairisa Putri, Nadya Lubis, Riskiani Maulidya Hernanda Muhamad Rayhan Nanda P Muhammad Arief Rivano Muharromah, Arssita Nur Mukthi, Tessy Octavia Nadha Ovella Syaqhasdy natasyalinggaa Natasya Dwi Ovalingga Nazifatul Azizah Nindi Syahfitrri Nini Erdiani Nonong Amalita Novi, Yoli Marda Nurul Mulya Oktaviani, Bernadita Olga Afrilly Putri Permana, Dony Rahmad Wanizal Pastha Rahmadani Rahmadani Rahmanesta, Frandito Raihan Athaya Wudd Riska 01 Salma, Admi silfia wisa fitri Suherman, Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yoli Marda Novi Yonggi Septa Pramadia Yonggi Zilrahmi, Zilrahmi