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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) 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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Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024 Rahmad Wanizal Pastha; Zilrahmi; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): 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/vol3-iss3/411

Abstract

This paper aims to identify the determinants affecting the Human Development Index (HDI) in Lampung Province, Indonesia, during the periode 2022-2024 using panel data regression. Lampung consistenly ranks among the provinces with the lowest HDI scores in Sumatera, indicating developmental disparties across regions. The research employs secondary data from 15 districts/cities and includes variables such as life expectancy, expected years of schoolingm mean years of schooling, and expenditure per capita. Panel data regression models fixed effect, random effect, and common effect were evaluated using chow, hausman, and lagrang multiplier tests to select the most approriate model. The random effect model was chosen, supported by a high R-Squared value of 92,71% indicating strong explanatory power. The analysis found that life expectancy and mean years of schooling significantly influence HDI, while expected years of schooling and expenditure per capita were not statistically significant in this model. The analysis shows that ensuring equal opportunities in health and education significantly contributies to better human development. Future research is recomended to incorporate qualitative approaches and more recent variables to enrich the analysis.
Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation Wahda Aulia Assara; Zamahsary Martha; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): 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/vol3-iss3/412

Abstract

Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.
Penerapan Vector Error Correction Model dalam Menganalisis Dampak Faktor Makroekonomi terhadap Inflasi di Indonesia Anjelisni, Nining; Amalita, Nonong; Kurniawati, Yenni; Martha, Zamahsary
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.654

Abstract

Penelitian ini bertujuan menganalisis dampak faktor makroekonomi terhadap inflasi di Indonesia pada periode Januari 2020–Maret 2025 dengan menggunakan pendekatan matematis melalui metode Vector Error Correction Model (VECM). Data diperoleh dari situs resmi Badan Pusat Statistik (BPS) dan Bank Indonesia (BI), yang meliputi variabel inflasi, jumlah uang beredar, BI Rate, kurs, ekspor, dan impor. Hasil analisis menunjukkan terdapat empat hubungan kointegrasi signifikan, dengan pengaruh positif dari jumlah uang beredar, kurs, dan ekspor terhadap inflasi, serta pengaruh negatif dari BI Rate dan impor. Dalam jangka pendek, ekspor (lag 1) secara statistik signifikan memengaruhi inflasi, sedangkan variabel lainnya belum signifikan. Model VECM yang dibangun terbukti stabil dan valid melalui berbagai uji kelayakan, serta menunjukkan akurasi tinggi dalam peramalan dengan nilai MAPE sebesar 9,23%. Prediksi inflasi untuk enam bulan ke depan memperlihatkan tren kenaikan bertahap, sehingga diperlukan penguatan ekspor dan pengendalian kebijakan moneter untuk menjaga stabilitas harga. Kontribusi utama penelitian ini adalah penerapan model matematis VECM sebagai alat analisis kuantitatif yang komprehensif dalam studi dinamika inflasi.
Comparison of Estimate Method of Moment and Least Trimmed Squares in Models Robust Regression Tri Wahyuni Nurmulyati; Dony Permana; Nonong Amalita; Martha, Zamahsary
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.
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, IPB University, 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.
Digitalization Data of Talawi Mudiak 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.
Metode DBSCAN dalam Pengelompokan Provinsi di Indonesia Berdasarkan Rasio Tenaga Kesehatan dan Tenaga Medis pada Tahun 2023 Maharani, Listia; Martha, Zamahsary; Permana, Dony; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): 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/vol3-iss4/423

Abstract

Health is a fundamental right of every citizen. This right is realized in the form of health services. Good health services have an adequate ratio of health and medical personnel. However, in reality, there are still many provinces that have a shortage of health and medical personnel. Therefore, clustering is carried out to make it easier for the government to group provinces that have similarities in terms of the ratio of health and medical personnel in Indonesia in 2023. Density Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the clustering methods used. Using the DBSCAN method, two clusters were obtained with a silhouette coefficient value of 0.49. Cluster 0 is called noise because the observation points in group 0 are outliers. Cluster 0 consists of provinces with a higher ratio of healthcare and medical personnel than cluster 1.
Grouping Of Universities In Indonesia In 2025 Based On The Qs World University Rankings Ranking Indicator Using The Kohonen Self-Organizing Maps Algorithm Raihan Athaya Wudd; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): 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/vol3-iss4/427

Abstract

Increasing the competitiveness of higher education is one of the main focuses in facing global competition. One of the important indicators in assessing the quality of higher education institutions is the QS World University Rankings which assesses universities based on indicators such as academic reputation, citations per lecturer, sustainability, and international collaboration. This study aims to group universities in Indonesia that are included in the QS World University Rankings in 2025 using the Kohonen Self-Organizing Maps (SOM) algorithm. The data used consisted of 10 QS assessment indicators for 26 universities in Indonesia. The normalization process is carried out using the min-max method, and the optimal number of clusters is determined using internal validation indices such as Connectivity, Dunn, and Silhouette. The results of the analysis show that the best models form three main clusters. Cluster 1 contains universities with superior performance in reputation and research, cluster 2 contains universities with a fairly balanced medium performance, and cluster 3 consists of universities with low performance in key indicators. The results of this study are expected to be the basis for policy makers and university managers to develop strategies to improve the quality of higher education in a targeted manner.
Classification of Recipients of the Family Hope Program in West Sumatra Province Using the Random Forest Algoritma Nini Erdiani; Dwi Sulistiowati; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): 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/vol3-iss4/431

Abstract

According to the Central Statistics Agency (BPS), the percentage of poor people in West Sumatra Province increased by 0.02% in 2024. One of the government's efforts to overcome poverty is a social assistance program issued by the government to help people who are economically disadvantaged. The targeted distribution of social assistance is an important challenge in improving community welfare, especially for families receiving PKH benefits. This study aims to classify households receiving the Family Hope Program (PKH) in West Sumatra Province using a random forest algorithm with Synthetic Minority Oversampling Technique (SMOTE). This study uses data on PKH recipient households in West Sumatra Province in 2024, which has a significant class imbalance. Therefore, the SMOTE method was applied to balance the data. The data was divided into training and testing data with a ratio of 80%:20%, then parameter tuning was performed to optimize mtry and ntree. The model was evaluated using a confusion matrix to compare model performance. The results show that the accuracy obtained is 76%. The precision value is 72%, the recall is 84%, and the f1-score is 78%. Based on the Mean Decrease Gini value, the head of household's diploma became the main attribute in determining whether a household received PKH or not. This study concluded that the use of SMOTE in the random forest algorithm performed well in classifying PKH recipients in West Sumatra Province, where the model performed well and was quite reliable in identifying PKH recipients.
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 Dina Fitria Dina Fitria, Dina Dinul Haq, Asra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Febiola Putri, Febi Fitri, Fadhilah fitri, silfia wisa Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Lubis, Riskiani Maharani, Listia Maulidya Hernanda Muharromah, Arssita Nur Mukhti, Tessy Octavia Mukthi, Tessy Octavia 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