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All Journal Media Statistika Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Journal of Natural Science and Integration JURNAL EKSAKTA PENDIDIKAN (JEP) Eksakta : Berkala Ilmiah Bidang MIPA Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Yenni Kurniawati
Jurusan Matematika, FMIPA Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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ANALISIS KEMISKINAN DI INDONESIA MENGGUNAKAN LOCAL INDICATOR OF SPATIAL ASSOCIATION DAN SPATIAL ERROR MODEL Khairani, Putri Rahmatun; Kurniawati, Yenni; Amalita, Nonong; Mukhti, Tessy Octavia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.966

Abstract

Poverty in Indonesia remains a significant socio-economic challenge with notable regional disparities. The eastern provinces, particularly Papua, Maluku, and East Nusa Tenggara, experience persistently high poverty rates, suggesting a strong spatial influence. This study examines the spatial distribution of poverty using the Local Indicators of Spatial Association and the Spatial Error Model with 2024 data from the Indonesian Central Statistics Agency (BPS) for 38 provinces. The analysis employs a K-Nearest Neighbors weighting matrix (k = 10) for spatial dependencies. The LISA results identify High-High poverty clusters in Papua, Maluku, and East Nusa Tenggara. In contrast, Low-Low clusters are concentrated in Java and Bali, indicating a strong spatial pattern (Moran’s I = 0.4448). SEM findings reveal that the Gini index (β = 29.97) and population density (β = 0.016) significantly influence poverty, whereas inflation and total population do not. The model explains 76.1% of poverty variance (R² = 0.760966), highlighting its superiority over traditional regression models. These findings underscore the need for spatially adaptive policies to address poverty effectively. Policymakers should prioritize equitable economic development, regional investment, and infrastructure improvements, particularly in high-poverty clusters. Integrating spatial econometric models with KNN provides deeper insights into interregional disparities, supporting more precise and inclusive development strategies
Mapping Area of Nagari Tanjung Gadang Sijunjung Regency Kurniawati, Yenni; Fitria, Dina; Salma, Admi
Pelita Eksakta Vol 8 No 01 (2025): Pelita Eksakta, Vol. 8, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss01/281

Abstract

Developing a digital village as a government point of view supports Nagari Tanjung Gadang as one of Sicantik (a village loving statistics). The village and server got the up to date data about the village and its sub-village. The problem for the village is presenting and analysing the data to publish as it is used. They also found difficulties in writing it into a publication format. The server gave an assistance to write Lumbuang Data Nagari Tanjung Gadang. The result is a book which explains the demographic condition of the village.
Comparison of The Singular Spectrum Analysis and SARIMA for Forecasting Rainfall in Padang Panjang City Putri, Fadhira Vitasha; Fitri, Fadhilah; Kurniawati, Yenni; Zilrahmi, Zilrahmi
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p61-74

Abstract

Indonesia is an area with a tropical climate, so it has two seasons, namely the rainy season and the dry season. The rainy season lasts from November to March and during this period rainfall tends to be high in several areas. Padang Panjang City is one of the cities with the smallest area in West Sumatra Province, which has the nickname Rain City. This is because the city of Padang Panjang has cool air with a maximum air temperature of 26.1 °C and a minimum of 21.8 °C, so this city has a fairly high level of rainfall with an average of 300 to 400 mm/year. This article discusses rainfall forecasting for Padang Panjang City by comparing the Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average methods. The data used spans 8 years, from January 2016 to December 2023. Forecasting results are obtained from the best method selected based on the smallest Mean Absolute Percentage Error value. The Singular Spectrum Analysis method has a Mean Absolute Percentage Error value of 5.59% and Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average  has a value 7.43%. The best forecasting method is obtained by the Singular Spectrum Analysis method.
Classification of Rice Growth Phase Using Regression Logistic Multinomial Model and K-Nearest Neighbors Imputation on Satellite Data Ghaly, Fayyadh; Kurniawati, Yenni; Amalita, Nonong; Fitria, Dina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p1-9

Abstract

One of the efforts made by the government to maintain food security is to provide statistical data on rice production through accurate calculation of harvest areas using the area sampling framework approach. Although area sampling framework surveys produce accurate estimates, the costs required are quite high when applying this method. To overcome this problem, one solution that can be applied is to utilize satellite imagery to monitor the greenness index of plants using the enhanced vegetation index. However, in real conditions, the Landsat-8 optical satellite is susceptible to cloud cover, which results in missing data. This study aims to model the phase of rice plants using the regression logistic multinomial model by utilizing Landsat-8 satellites and k-nearest neighbors imputation handling to overcome missing data. The results showed that the model had varying performance in each phase, with an average balanced accuracy of 66.45%. This figure shows that the model can classify the area sampling framework data imputed using the k-nearest neighbors imputation method well. The model shows optimal performance in the late vegetative and generative phases but is less effective in detecting the harvest, puso, and non-rice paddy phases.
Application of Singular Spectrum Analysis in Predicting Rupiah Exchange Yuan Hendrawan, Muhammad; Zilrahmi, Zilrahmi; Kurniawati, Yenni; Fitria, Dina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
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.v9i1p75-85

Abstract

The exchange rate between two countries is the price of the currency used by residents of these countries to trade with each other, the relationship between the Rupiah exchange rate and the Yuan is one of the important aspects in the dynamics of international trade. Therefore, forecasting the exchange rate is important as an effort to predict the exchange rate of Rupiah against Yuan in the future. The method used for forecasting is Singular Spectrum Analysis, namely decomposition and reconstruction. The accuracy of the resulting forecast is measured using the Mean Absolute Percentage Error criterion. The exploration results obtained are forecasting accuracy based on the Mean Absolute Percentage Error value of 2.15% with a window length of 23 which identifies that the forecasting results are accurate and effective. Forecasting is said to be accurate if the Mean Absolute Percentage Error value is lower than 10% and close to 10%
Peramalan Curah Hujan Sebagai Upaya Mitigasi Bencana Menggunakan Seasonal Autoregressive Integrated Moving Average Fayyadh Ghaly; Amelia Susrifalah; Yenni Kurniawati
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.55289

Abstract

Rainfall prediction is important in disaster mitigation to reduce impacts such as drought, flood, and landslide. Rainfall data that has a seasonal pattern requires an appropriate forecasting method, one of which is SARIMA. This study predicts rainfall at the Deli Serdang Climatology Station, North Sumatra, based on monthly observation data for 2018–2023, showing a seasonal pattern with a 12-month cycle. The best model obtained is SARIMA (0,0,1) (0,0,1)12 with a MAPE of 19.5%, indicating a prediction accuracy of 80.5%. The forecasting results indicate a decrease in rainfall in the first semester of 2024, which is in the medium rainfall category. These findings can support disaster risk mitigation strategies and natural resource management planning related to climate change. The SARIMA model also has the potential to be applied in further climatology studies.
Binary Logistic Regression to Factors Affecting Unmet Need for Limiting in East Java, Indonesia Sri Wahyuni; Yenni Kurniawati; Sepniza Nasywa; Ardiyatul Putri
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/353

Abstract

East Java, Indonesia's second most populated province, is anticipated to see significant annual population growth in the future, potentially resulting in a population explosion. The elevated birth rate facilitates the swift increase in population size. The unmet need for knowledge-based information among women of reproductive age has posed obstacles for the execution of family planning initiatives aimed at reducing birth rates. This study used binary logistic regression to identify the factors affecting the unmet demand for family planning among women of reproductive age in East Java Province in 2017.The investigation revealed that the woman's age, employment status, and husband's educational level significantly influenced the unmet need for constraint. Moreover, women aged 15-24 who are unemployed, lack schooling, have an illiterate partner, and reside in rural regions are more prone to experiencing an unmet need for contraception. Women aged 15-19 years compared to women aged 45-49 years were at 3,182 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women aged 20-24 years compared to women aged 45-49 years were at 1,316 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women who did not work compared to women who worked were 1,311 times more likely to have an unmet need for family planning compared to a met need for family planning. The binary logistic analysis model that was formed provided a good accuracy of 92,135% in predicting
Application of K-Modes Clustering Method to Identify Low Birth Weight Factors in Central Sulawesi Province Aprotama, Celsy; Yenni Kurniawati; Muhammad Arief Rivano; Devi Yopita Sipayung
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/357

Abstract

Low birth weight (LBW) has long-term effects on maternal and child health, with a high prevalence in Central Sulawesi Province. This study aims to identify factors influencing the occurrence of LBW in the region using the k-modes clustering method. The data used in this research is derived from the 2017 Indonesian Demographic and Health Survey. The analyzed variables include the husband's education level, miscarriage rate, maternal smoking habits, child's gender, husband's occupation, type of residence, and wealth index. The analysis revealed two distinct clusters. The first cluster mainly consisted of husbands with a secondary education level or equivalent to junior high school, working in the agricultural sector, residing in urban areas, and having a medium wealth index. In contrast, the second cluster was dominated by husbands with only primary education or equivalent to elementary school, living in rural areas, and having a very low wealth index. The findings of this study emphasize the need for comprehensive efforts to improve education, enhance environmental conditions, and expand healthcare access to reduce poverty and lower the incidence of LBW in Central Sulawesi. This research also contributes to initiatives aimed at improving maternal and child health in the region.
Logit And Complementary Log-Log Modeling (Case Study: Factors Influencing Birth Control Use in Papua 2017) Sasmita, Riza; Yenni Kurniawati; Sri Wahyuni; Celsy Aprotama
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/358

Abstract

Research was conducted to determine the factors that influence the use of family planning in Papua Province in 2017. Indonesia has the 4th largest total population in the world, facing the challenge of a fairly high and uncontrolled population growth rate, which can have an impact on the welfare of the community, especially Papua Province. This study used secondary data from the 2017 SDKI. The population of this study was all women of childbearing age in the province of Papua. The research was conducted using logit logistic regression and cloglog logistic regression methods and took the best model to analyze the factors affecting family planning use in Papua Province. The results showed that the cloglog logistic regression model proved to be the best model based on AIC and accuracy. The accuracy of this cloglog logistic regression model is 78.54%. With the results of the cloglog logistic regression analysis, it was found that there was a relationship between region of residence, husband's education, and wife's education. The odds of a woman who has a husband with more than a junior high school education having an unmet need for family planning is 1.688 times higher than a woman who has a husband with less than a junior high school education. The odds of a woman with a junior high school education or above having an unmet need for family planning is 0.496 times higher than a woman with less than a junior high school education.
An Application X-bar Chart and Statistical Process Control With R Package Rizkiah, Niswatul; Yenni Kurniawati
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/363

Abstract

Quality control is a critical aspect of ensuring that production processes meet established standards and customer requirements. One widely used approach in Statistical Quality Control (SQC) is the control chart, particularly the X̄ and s charts, which monitor process stability based on the mean and variability of the data. This study aims to evaluate the quality and variation of the feed water boiler process using X̄ and s control charts, as well as to assess process capability with the aid of the R programming language and the qcc package. The dataset comprises hardness measurements of water collected over 25 consecutive days, three times per day, resulting in 75 observations. Initial analysis revealed one data point outside the control limit in the X̄ chart, which, when excluded, improved overall process stability. The s chart indicated more consistent stability compared to the X̄ chart. Process capability analysis yielded Cp and Cpk values of 0.5844 and 0.5600, respectively, indicating that the process is not yet capable of fully meeting product specifications and exhibits relatively high variability.These findings highlight the need for process improvement through variation reduction and six sigma approaches.The use of R/qcc proved to be an effective tool for monitoring and analyzing quality control in production processes.
Co-Authors Aditya, Muhammad Fadhil Aditya Admi Salma Afifa Lufti Insani AL Rezki Ivansyah Alya Aufa, Wafiq Amelia Susrifalah Anang Kurnia Anggara, Rudi Anggi Adrian Danis Anita Fadila Anjelisni, Nining Annisa Ramadhani Aprotama, Celsy Ardhi, Sonia Ardiyatul Putri Arnellis Arnellis arrahmi, nailul Atus Amadi Putra Aulia Wanda Aulia, Yuke Aurumnisva Faturrahmi Baehaqi Berliana Nofriadi Bimbim Oktaviandi Celsy Aprotama Chairina Wirdiastuti Cindy Caterine Yolanda Darwas Deska Warita Devi Yopita Sipayung Dewi Murni Dina Fitria Dina Fitria Dina Fitria, Dina Disti Harlin Diva Aliyah Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Elfiani Sarian Bur Elfin Innaka Hamidah Elza Vinora Eujenniatul Jannah Fadhil Irsyad, Muhammad Fadhilah Fitri Fahmi Amri, Fahmi Fashihullisan Fayyadh Ghaly Fayza Annisa Febrianti Febiola Putri, Febi Fitri, Fadhilah Fitri, Fitri Hayati fitri, silfia wisa Ghaly, Fayyadh Hadiyanti Riskha harelvi, dhea afrila Harpidna, Riska Harpidna Hary Merdeka Helma Helma Helma Helma Hendrawan, Muhammad Ihsan Dermawan Irwan Irwan Khairani, Putri Rahmatun Kusman Sadik Lutfian Almash M Fathoni Arnas Manja Danova Putri Marvero, Andre Maya Ifra Shobia Meira Parma Dewi Minora Longgom Nasution Muhammad Arief Rivano Mukhti, Tessy Octavia NA Mentacem Natasya Dwi Ovalingga, natasyalinggaa Nofriadi, Berliana Nonong Amalita Oktaviani, Bernadita Permana, Dony permana, yazid Prida Nova Sari Putra, Dio Afdal Putri Yeni, Dicha Putri, Fadhira Vitasha Rahma, Dzakyyah rahmad revi fadillah Revina Rahmadani Rizki Amalia, Annisa Rizkiah, Niswatul Ronald Rinaldo Rosa Salsabila Azarine Salma, Admi Salsabilla Khairani Sasmita, Riza Sepniza Nasywa Septrina Kiki Arisandi Silvia Triana Siskha Maulana Basrul Siti Nurhaliza Sondriva, Wilia SRI RAHAYU Sri Wahyuni Susrifalah, Amelia Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tsani, Nahda Maesya Wimmi Sartika Windi Dwi Saputra Wita, Wita Resfi Ananta Yunistika Ilanda Zahrani Asyati Zulika Zamahsary Martha Zilrahmi, Zilrahmi