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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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Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5 Futiah Salsabila; Zamahsary Martha; Atus Amadi Putra; Admi Salma
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-iss1/135

Abstract

Stroke is a health condition that has vascular disorders where brain  function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.  
Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm Syahfitrri, Nindi; Nonong Amalita; Dodi Vionanda; 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-iss1/145

Abstract

Investment is an effort to increase economic growth in Indonesia.  A popular investment in the community is gold investment.  The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR).  SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112.  So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.
Artificial Neural Network Model for Estimating the Poor Population in Indonesia as an Effort to Alleviate Poverty Febiola Putri, Febi; Atus Amadi Putra; Yenni Kurniawati; 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-iss2/154

Abstract

Forecasting the poverty rate in Indonesia is one of the activities that is considered to be able to help various parties, such as being able to help the government in planning more effective and efficient poverty alleviation programs. In this study, forecasting the poverty rate in Indonesia was carried out using the backpropagation artificial neural network method. The purpose of this research is to model and predict the poverty rate using the backpropagation artificial neural network model, and to determine the accuracy of the forecasting results produced by this method. This research is an applied researc. The data used is annual data on proverty in Indonesia from 2917-2021. The data is then divided into two parts, namely training data and test data. The results show that the best artificial network model is BP (7,7,2) with 7 neurons in the input layer, 7 neurons in the hidden layer, and 2 neurons in the output layer. The accuracy of this model is good with a MAPE value of 0.07633%. The forecasting results in the next period show that the highest number of poor people is East Java province with a value of 3604.1698 thousand people in the first semester (March) of 2022 and has increased in the second semester period (September) of 2022 with a value of 3698.822 thousand people
Analisis Sentimen Pengguna Aplikasi X terhadap Konflik antara Israel dan Palestina Menggunakan Algoritma Support Vector Machine Carina, Fadhillah Meisya; Admi Salma; Dony Permana; 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-iss2/170

Abstract

The conflict between Israel and Palestine is the Middle East's longest-running conflict since 1917 and is still ongoing today. This is one of the international conflicts that involves many Arab countries and Western countries in the dispute. The conflict between Israel and Palestine has caused countries in the world to be divided into two camps, namely the pro Palestinian independence camp and the contra camp. The impact of this conflict also creates polarization among Indonesians and forms diverse public opinions on the social media application X. The purpose of this research is to find out how the classification of sentiment of X application users affects the conflict between Israel and Palestine. An analysis that is utilized to convert text-based public opinion data into information is sentiment analysis. The chosen algorithm to separate data classes is the Support Vector Machines algorithm, which can classify data by determining the best hyperplane to provide a separator between opinions that are pro Israel or pro Palestine. After the preprocessing stage, 1000 tweets data were obtained with 800 training data and 200 testing data. The accuracy rate is 93%, precision is 92.93%, recall is 100%, and f-measure is 96.33%. From the results of testing 200 data points, there were 198 pro Palestine opinions and 2 pro Israel opinions, so that it might be said that more individuals favor or support Palestinian independence in the conflict that occurred between Israel and Palestine.
Pengelompokan Potensi Kebakarn Hutan/Lahan di Indonesia Berdasarkan Sebaran Titik Panas Mengunakan Metode CLARANS fitri, silfia wisa; Martha, Zamahsary; Kurniawati, Yenni; 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 01, Riska; 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, Yonggi Septa Pramadia; 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.
Application of Multivariate Adaptive Regression Splines for Modeling Stunting Toddler on The Island of Java Rahma, Dzakyyah; Nonong Amalita; Yenni Kurniawati; Zamahsary Martha
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-iss3/205

Abstract

Stunting is a chronic nutritional problem experienced by toddlers, characterized by a shorter body height compared to children their age. The aim of this research is to model and determine the factors that influence Stunting on The Island of Java using Multivariate Adaptive Regression Spline (MARS). MARS is a modeling method that can handle high-dimensional data. The results of this study show that the best MARS model is a combination (BF=24, MI=3, and MO=2) with a minimum GCV value of 0.9475. Based on the model, the factors that significantly influence Stunting on the island of Java are babies receiving complete basic immunization (X4), babies getting exclusive breastfeeding (X3), pregnant women getting K4 (X1), and pregnant women getting TTD (X2). The level of importance of each variable is 100%, 81.64%, 60.38%, and 43.90%. Based on research results, babies receiving complete basic immunization is the variable that most influences stunting on The Island of Java in 2021.
Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022 Bimbim Oktaviandi; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
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-iss3/210

Abstract

The internet facilitates e-commerce by enabling efficient transactions and building consumer trust. With internet users in Indonesia reaching 204 million in 2022, it is crucial to Cluster provinces based on the types of goods and services sold online to design effective marketing strategies. The Fuzzy C-Means (FCM) method is used for Cluster analysis, allowing objects to have different membership degrees in multiple Clusters and providing accurate Cluster center placement. This study applies Fuzzy C-Means to Cluster 34 provinces in Indonesia based on the sale of goods/services in e-commerce in 2022, aiming to provide insights into market preferences and assist companies in developing more effective strategies. The results show that the method forms two Clusters. By evaluating standard deviation values and ratios, Fuzzy C-Means proves effective in Clustering provinces in Indonesia based on e-commerce sales data. Cluster validation reveals a standard deviation ratio of 0.14, indicating clear and significant Cluster separation.
Analisis Tingkat Kejahatan di Jabodetabek Menggunakan Model SARQR Pada Data Yang Mengandung Outlier Martha, Zamahsary; Muharromah, Arssita Nur; Permana, Dony; Mukthi, Tessy Octavia
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 13 No. 2 (2024): September 2024
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.13.2.2024.57594

Abstract

Jabodetabek memiliki permasalahan tingginya tingkat kejahatan yang berdampak pada permasalahan sosial, kemiskinan, pendidikan, dan lain-lain. Tingkat kejahatan berhubungan dengan wilayah yang saling dipengaruhi oleh wilayah sekitarnya dan datanya mengandung outlier. Metode yang tepat dalam memodelkan permasalahan tersebut dengan menggunakan model Spatial Autoregressive Quantile Regression (SARQR). Tujuannya adalah menentukan faktor-faktor yang mempengaruhi tingkat kejahatan menggunakan model SARQR. Data yang digunakan adalah data tingkat kejahatan tahun 2022 serta faktor-faktor yang diduga mempengaruhinya pada 14 Kab/Kota di Jabodetabek. Model SARQR pada kuantil ke-0.95 merupakan model terbaik dan diperoleh faktor persentase penduduk miskin dan tingkat pengangguran terbuka berpengaruh terhadap tingkat kejahatan di Jabodetabek tahun 2022.
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