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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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Application of singular spectrum analysis method to forecast rice production in west sumatra: Artikel nazifatul azizah Nazifatul Azizah; Fadhilah Fitri; Dodi Vionanda; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 3 (2023): 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/vol1-iss3/58

Abstract

The imbalance between the population and rice production will cause various negative impacts such as food crises and increasing poverty, so forecasting needs to be done to maintain food availability in the future. This study aims to determine the results of rice production in West Sumatra Province for 12 periods in 2023 using the SSA method. Based on the results of the analysis, rice production in 2023 for 12 periods tends to decrease compared to the previous year. Forecasting rice production using the SSA method with L=21 can be said to be accurate with a MAPE obtained of 17.69%.
Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java Gezi Fajri; Syafriandi Syafriandi; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 3 (2023): 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/vol1-iss3/67

Abstract

Poverty is crucial problem that negative impact on all sectors, including economic, social, and cultural development in East Java Province. Poverty can also increase unemployment, crime, trigger social disasters and hinder progress East Java province. One efforts overcome problem of poverty in East Java province is detect factors that influence. This effort can be done through statistical modeling to determine factors that influence poverty in East Java province. statistical model that can identify factors that influence poverty and explain relationship between region and surrounding area is spatial regression analysis. In spatial regression analysis, spatial weighting matrix is needed to determine spatial influences between regions where one region influences neighboring regions. spatial weighting matrices that is often used is queen contiguity, and according to Anselin (1988:20), this spatial weighting also considers initial information, purpose of case studied, and theory underlying the research. This weighting uses social and economic variables case under study, namely customized weighting matrix. Based on results of this study, shows that best spatial regression and spatial weighting models are General Spatial Model (GSM) with customized weighting because customized weighting produces better estimation results than SAR, SEM and GSM models with queen contiguity weighting in district and city poverty modeling in East Java province with Akaike Infomation Criterion (AIC) value of 188.77 and detemination coefficient (R2) of 84.95%. School's Expected Time, Life Expectancy Score, and Employment Participation Rate are factors that will have substantial impact on percentage of population living in poverty East Java's districts and cities in 2021.
Analysis of the Poverty Level Model for West Sumatra Province Using Geographically Weighted Binary Logistic Regression april leniati; Dony Permana; Nonong Amalita; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): 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/vol1-iss4/80

Abstract

T   West Sumatra Province (West Sumatra) ranks third lowest in terms of the poverty rate on the island of Sumatra in 2022, with a figure of 5.92%. Although this figure is lower than the national average, the Province of West Sumatra is targeting a reduction in the poverty rate to 5.62% in 2024 in the vision of the 2021–2026 Regional Development Plan. The purpose of this study is to analyze the factors that contribute to the poverty rate in West Sumatra Province based on geography in 2022. The method used to address poverty problems is Geographically Weighted Binary Logistic Regression (GWBLR), which takes geographical influences into account in the analysis. This study uses data on the percentage of poor people (Y) and the influencing factors, namely life expectancy (X1), literacy rate (X2), labor force participation (X3), and economic growth (X4). The results showed that based on the lowest Akaike Information Criterion Corrected (AICc) value, the GWBLR model with a Fixed Gaussian Kernel weight is the best at modeling the problem of poverty in West Sumatra in 2022. According to the model, the life expectancy variable will have a significant impact on the level of poverty in 13 districts and cities in West Sumatra Province in 2022.
Penerapan Metode Self Organizing Maps (SOM) dalam Pengklasteran Berdasarkan Indikator Pemerlu Pelayanan Kesejahteraan Sosial (PPKS) Provinsi Jawa Barat Maulidya Hernanda; Admi Salma; Dodi Vionanda; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): 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/vol1-iss4/82

Abstract

The province of West Java in Indonesia has witnessed a rise in its impoverished population. Being the most populous province in Indonesia, West Java faces complex social welfare issues due to its large population. This study aims to conduct cluster analysis to identify district/city clusters in West Java province and determine the characteristics of these groups based on the indicators of the Need for Social Welfare Services (PPKS). The self-organizing maps (SOM) method will be utilized for this analysis. SOM is an unsupervised learning method, in which the training process does not require supervision (target output) which produces input representations in two dimensions (maps). In this study, the results obtained were 3 clusters where cluster 1 which consisted of 24 districts/cities had a relatively high average score for each member in the cluster, then cluster 2 which consisted of Cianjur and Karawang districts showed high social welfare problems compared to other clusters, and for cluster 3 which consists of Bandung regency, it shows that the most prominent social welfare problem is the indicator of socio-economic vulnerability of women, with an average of 34,549 cases/year. Based on the results obtained, it is necessary to make the right decisions regarding allocations, resources, more effective service planning, and the development of more targeted social welfare programs.
Penerapan Metode Regresi Kuantil pada Data yang Mengandung Outlier untuk Tingkat Kejahatan di Jabodetabek Arssita Nur Muharromah; Zamahsary Martha; Dony Permana; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): 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/vol1-iss5/94

Abstract

The problem of crime is increasingly widespread in Indonesia. The crime rate in Jabodetabek is the second highest in Indonesia. In this study containing outliers, the appropriate method for this research is quantile regression. Quantile regression is the development of median regression or the Least Absolute Deviation (LAD) method which is useful for dividing data into two parts to minimize errors. however, this LAD is considered not good for modeling, therefore comes the quantile regression. Quantile regression is useful for overcoming the problem of unfulfilled assumptions in classical regression, namely the phenomenon of heteroscedasticity and quantile regression can model data that contains outliers. The quantile regression method approach is to separate or divide the data into certain parts or quantiles where it is suspected that there are differences in estimated values. The resulting measurement of the goodness of the model uses the coefficient of determination or R2 in each quantile. In this study, five quantiles were used, namely 0,05; 0,25; 0,50; 0,75; and 0,95. From the results of the analysis it is known that the best parameter estimation model is found in the 0,95 quantile with all independent variables having a significant effect on the dependent variable (crime rate). whereas in the 0,25 and 0,50 quantiles there are no independent variables that have a significant effect, this may be due to the influence of other factors not present in the study that affect each quantile.
Classification of Nutrition Problems for Indonesian Toddler With Decision Tree Algorithm C4.5 Nadha Ovella Syaqhasdy; Zamahsary Martha; Nonong Amalita; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): 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/vol1-iss5/98

Abstract

Having excellent human resources is essential for Indonesia's development. The development of Indonesia is the key to improving the quality of life for its citizens, and a focus on this development can have a positive impact on the health and economy of the community. A healthy and educated generation is fundamental for the expected progress of this nation, as nutritional status is a significant factor affecting the quality of human resources. Nutritional problems can lead to serious consequences, such as abnormal physical growth, a decline in IQ quality, and even death. The objective of this research is to analyze the factors that influence the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flowchart resembling a branching tree structure. The C4.5 algorithm was utilized in this study. This algorithm can process both numeric and categorical data, handle missing attribute values, and generate easily interpretable rules. After conducting the analysis, it was found that the decision tree's results indicated that the attribute "Stunting < 20%" is a determining factor for acutechronic malnutrition issues in toddlers. There are 392 districts and cities in Indonesia where the prevalence of stunted toddler nutritional status is less than 20%. The model created using the C4.5 algorithm was evaluated using a confusion matrix, resulting in an accuracy of 99.8% and a kappa value close to 1. This indicates that the model is capable of accurately classifying toddler nutrition problems in Indonesia.
Sentiment Analysis of Prabowo Subianto as 2024 Presidential Candidate on Twitter Using K-Nearest Neighbor Algorithm Aurumnisva Faturrahmi; Zamahsary Martha; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): 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/vol1-iss5/101

Abstract

The presidential election is one of the most talked topics at this moment. Based on many surveys, Prabowo Subianto is one of the strongest candidates for the upcoming 2024 presidential election. This research aims to see how the public sentiment towards Prabowo Subianto as the presidential candidate tends to be positive or negative. Sentiment classification was conducted using the K-Nearest Neighbor (KNN) algorithm. This algorithm classifies sentiment based on the k value of the nearest neighbor. This analysis was conducted in several stages such as data collection, text preprocessing, data labelling, data classification using the KNN algorithm, and evaluating the accuracy of the model in classifying sentiment. In this research, the results of the sentiment classification were 2731 positive sentiments and 76 negative sentiments. Where the accuracy rate produced by the model using the value of k = 3 on the division of training data and testing data of 80:20 is 97,33%.
Naive Bayes Classifier Method on Sentiment Analysis of Bibit Application Users in Play Store Afifa Lufti Insani; Zamahsary Martha; Yenni Kurniawati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): 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/vol1-iss5/102

Abstract

The Bibit app is one of the most widely used investment apps these days. This application is widely used by novice investors because of its convenience in opening accounts, disbursing funds, purchasing mutual funds and easy-to-understand application design. However, there are still many people who doubt and worry about the quality of the Bibit application due to the lack of understanding of the advantages and disadvantages of the Bibit application. So, review data on the application is used which is available in the play store with the aim of knowing user reviews of the application and being a consideration for prospective users before using the application. Because reviews on the application have a large number and can be positive or negative, so sentiment analysis is needed that can help classify these reviews quickly. Then classification is carried out to obtain a classification model that can be used to predict user sentiment using the Naive Bayes Classifier method. The results obtained by Bibit application users tend to have positive sentiments with an accuracy value of 79.45%.
Fuzzy Geographically Weighted Clustering Method for Grouping Provinces in Indonesia Based on Welfare Indicators Aspects of Information and Communication Technology (ICT) Hefiani Mustika Hasanah; Dina Fitria; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): 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/vol1-iss5/108

Abstract

The welfare of the people is a task and goal that must be realized by the Republic of Indonesia. To find out the condition of the welfare of the Indonesian people, it can be seen in eight areas of Indonesia's welfare indicators. Indicators The welfare of the Indonesian people is undergoing a digital transformation of information and communication technology (ICT) in 2021. However, there was a gap in ICT development due to geographical conditions and the distribution and dynamics of each region's society. Cluster analysis is a solution for target setting for better future decisions. Fuzzy Geographically Weighted Clustering (FGWC) is one of the cluster methods with fuzzy logic that considers geographical and population elements in grouping targets. The results of the research resulted in three optimum clusters with different characteristics for  each cluster based on indicators of ICT aspects of people's welfare. Cluster 1 has a medium status of ICT indicators of people's welfare and is located in the middle or at the end of the island, provinces from cluster 2 have a low status of ICT indicators of people's welfare with a medium area, while cluster 3 has a high status of ICT indicators of people's welfare with a large area or dense populations.
Fuzzy K-Nearest Neighbor to Predict Rainfall in Padang Pariaman District Rizki Amalia, Annisa; Nonong Amalita; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (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-iss1/126

Abstract

Information about rainfall levels at a time and in a region is very important because rainfall influences human activities. Rainfall is the amount of water that falls to the earth in a certain period of time, measured in millimeters. One piece of information related to rainfall is daily rainfall predictions. In this study, an attempt was made to classify daily rainfall at the Padang Pariaman climatology station into 5 categories, namely very light rain, light rain, moderate rain, heavy rain and very heavy rain. There are 4 weather parameters used, namely air temperature, humidity, wind speed and duration of sunlight. One of the methods used to predict rainfall is data mining, a computer learning to analyze data automatically thus obtaining a perfect new model. One of the best prediction algorithms in data mining is Fuzzy K-Nearest Neighbor (FK-NN). FK-NN uses the largest membership degree value of the test data in each class to predict the class. The number of sample classes for rainfall data in Padang Pariaman Regency has an imbalance class. To overcome the imbalance class, Synthetic Minority Over-sampling Technique (SMOTE) method is used to generate minority data as much as majority data. The results of this study by using FK-NN classification with 343 test data, parameters K = 12, and euclidean distance is quite good at the accuracy level of 76,38%..
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