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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Jurnal Riset Statistika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Salma, Admi
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Biplot and Procrustes Analysis of Poverty Indicators By Province in Indonesia in 2015 dan 2019 Ade Eriyen Saputri; Admi Salma; Nonong Amalita; Dony Permana
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-iss1/124

Abstract

Poverty is one of the country's problems that the government should  overcome. Poverty is influenced by several indicators. The success of a government can be seen from changes in poverty. This study compares the percentage of Indonesia's poverty indicators at the beginning of office (2015) and the end of office (2019) of one government period. The indicators that most affect the poverty rate in 2015 and 2019 are seen using biplot analysis while to measure the similarity and the magnitude of the percentage change in poverty from 2015 to 2019 can use procrustes analysis. The results of the biplot analysis show households that have access to decent and sustainable sanitation services as the indicator with the highest diversity in 2015 while in 2019 it is the percentage of youth  (aged 15-24 years) not in education, employment or training and households that have access to decent and sustainable drinking water services. Kepulauan Riau, DKI Jakarta, DI Yogyakarta, and Bali are the provinces that have the highest values in almost all poverty indicators except the indicator of the percentage of youth  (aged 15-24 years) not in education, employment or training. The results of the procrustean analysis show an increase of 9.7% in Indonesia's poverty indicators in 2019 compared to 2015. So it can be said that the two configurations are very similar.
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.  
Comparison of the C5.0 Algorithm and the CART Algorithm in Stroke Classification Indah Lestari; Dina Fitria; Syafriandi Syafriandi; 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-iss1/144

Abstract

The C5.0 and CART algorithms are similar in terms of velocity and handling of categorical and numeric type data. However, these two algorithms are differences in terms the CART algorithm is binary and classifies categorical, numerical and continuous response variables resulting in classification and regression decision trees. Meanwhile, the C5.0 algorithm is non-binary and classifies categorical response variables resulting in a classification tree. This research aims to classify the Kaggle’s Stroke Prediction Dataset to find out the variables that most influence the risk of stroke, as well as to compare the results of the classification accuracy of the both algorithms. The results of the study showed that CART algorithm has a higher value of accuracy and precision, but its recall value is lower than C5.0. The accuracy value of each algorithm is 77.9% and 77.5%, presision is 89.5% and 83.2%, recall is 67% and 71.4%. Overrall, it can be concluded that there is no difference in classification between the two algorithm. Beside that, in the CART there were 3 variables that most influence on stroke risk, they are age, BMI, and average blood glucose levels. Meanwhile, in C5.0 only 2 variable that most influence, there are age and average blood glucose levels.
Sentiment Analysis Using Support Vector Machine (SVM) of ChatGPT Application Users in Play Store Muthia Sakhdiah; Admi Salma; Dony Permana; Dina Fitria
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-iss2/158

Abstract

The ChatGPT application is an Articial Intelligence (AI) technology that responds to conversations in form text and voice messages, and is accessible via smartphones or computers. The ChatGPT provides answers and solutions related to the problems asked, the speed and complexity of the answers are also added values of this application. However, there are negative impacts, one of which is the vulnerability of scientific papers to plagiarism. Because of this, there are many reviews from the community that assess this application. These reviews can be seen on the Play Store which can be a reference before downloading the ChatGPT application. How the community responds can be seen through sentiment analysis, which will classify positive and negative assessments. Making it easier for companies to evaluate products. Then classification is carried out using Support Vector Machine (SVM), the classification model obtained is used to classify user reviews of the ChatGPT application. The results showed an accuracy of 93.9% with a linear kernel, and the sentiment of people who use the ChatGPT application is more positive.
Impelementation of Subtractive Fuzzy C-Means Method in Clustering Provinces in Indonesia Based on Factors Causing Stunting in Toddlers Hariati Ainun Nisa; Admi Salma; Dodi Vionanda; Tessy Octavia Mukhti
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-iss2/164

Abstract

Indonesia in 2022 has a stunting rate that is still relatively high at 21.6%. For this reason, it is necessary to make various efforts to reduce the stunting rate. One of the efforts that can be made is to understand the characteristics of each province in Indonesia with cluster analysis. This study aims to cluster provinces in Indonesia based on factors that cause stunting in children under five. The method used is Subtractive Fuzzy C-Means which has advantages in terms of speed, iteration, thus producing more stable and accurate results. The results of the validity test with Silhouette Coefficient Index, the optimum number of clusters is 8 clusters with a radius (r) of 0.70. There are 8 provinces that have provided maximum handling and efforts in reducing stunting rates, namely the provinces of Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI Yogyakarta, Bali, East Kalimantan, South Kalimantan, and South Sulawesi. Meanwhile, 7 provinces namely East Nusa Tenggara, South Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, and West Papua, still need special attention from the government in reducing stunting rates based on the factors that cause stunting discussed in this study.
Analisis Sentimen Pengguna Aplikasi X terhadap Konflik antara Israel dan Palestina Menggunakan Algoritma Support Vector Machine Fadhillah Meisya Carina; 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.
Perbandingan Algoritma C4.5 dan C5.0 Dalam Klasifikasi Status Gizi Balita Stunting dhea afrila harelvi; Admi Salma; Yenni Kurniawati; Fadhilah Fitri
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-iss2/172

Abstract

Stunting is one of the health conditions that reflect aspects of nutrition and child growth, allowing us to observe the nutritional status of toddlers. The aim of this study is to determine the classification results of the C4.5 and C5.0 algorithms in cases of stunted toddler nutritional status and to compare the results between the C4.5 and C5.0 algorithms in classifying stunted toddler nutritional status using k-fold cross-validation. The data in this study are secondary data. Which is collected from Puskesmas IV Pesisir Selatan Regency. The research variables are divided into two, namely the response variable Y, which is Toddler Nutritional Status, and predictor variables X including Age, Toddler Gender, Toddler Weight, and Toddler Height. The result of the study obtain the algorithm C5.0 produse accuracy value of the C5.0 algorithm is higher than that of the C4.5 algorithm. The C5.0 algorithm provides an average accuracy result of 83% while the C4.5 algorithm provides an accuracy result of 79%. Thus, it can be concluded that the C5.0 algorithm is better at classifying stunted toddler nutritional status.
Random Forest Implementation for Air Pollution Standard Index Classification in DKI Jakarta 2022 Hanifa Hasna; Nonong Amalita; Dony Permana; Admi Salma
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-iss2/173

Abstract

Air pollution is a serious challenge in various cities, including DKI Jakarta. Based on measurements of the Air Pollution Standard Index carried out by the DKI Jakarta Environmental Service, the air quality in DKI Jakarta is considered moderate to unhealthy. Deteriorating air quality in the Jakarta metropolitan area is very dangerous for humans and living things. Therefore, to prevent the problem, the classification of air quality based on pollutant content is carried out using Random Forest (RF). The application of RF will form several trees that can provide better predictions and are able to produce low errors. The result of this study obtained optimal tree formation, namely tree formation using a combination of mtry (any input variables randomly selected in one sorting node)=2 and ntree (number of trees in the forest) as many as 5000 trees. The resulting accuracy was 99.17% with an OOB error rate of 0.83%. This research identifies that particulate pollutants are the main factor causing air pollution in DKI Jakarta. Based on these results, it shows that RF is able to provide accurate predictions about the level of air pollution in DKI Jakarta and can be identify important factors that affect air pollution.
PENGEMBANGAN DATA NAGARI TANJUNG GADANG MENUJU DESA DIGITAL Yenni Kurniawati; Dina Fitria; Admi Salma
Pelita Eksakta Vol 6 No 2 (2023): Pelita Eksakta, Vol. 6, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol6-iss2/210

Abstract

Developing villages' data toward digital data is one of villages' government programs to improve the villages. The village's government needs collaboration with professional surveyors and data digital builders to achieve the goal, which the government is unable to provide. The Statistics Department provided the team to overcome the problems by giving training surveys to local residents and accompanying them to build Nagari Tanjung Gadang digital data.
Mapping Area of Nagari Tanjung Gadang Sijunjung Regency Kurniawati, Yenni; Fitria, Dina; Salma, Admi
Pelita Eksakta Vol 8 No 1 (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.
Co-Authors Ade Eriyen Saputri Aldwi Riandhoko Amalia, Nonong Anita Fadila Atus Amadi Putra AULIA YUSWITA Aulia, Yuke Bahri Annur Sinaga Belia Mailien Chairina Wirdiastuti Darwas Denia Putri Fajrina dhea afrila harelvi Dina Fitria Dina Fitria Dina Fitria, Dina Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina Fathina Nafisa Putri Faulina Fitria Panca Ramadhani Futiah Salsabila Gilang Ibnul farizi Hanifa Hasna Hariati Ainun Nisa Ihsan Dermawan Ihsanul Fikri Indah Lestari Jimmi Darma putra Kurnia Andrea Diva Lubis, Riskiani Mardhiatul Azmi Maulidya Hernanda Mawaddah, Nurul Melda Safitri Muhamad Rayhan Nanda P Muthia Sakhdiah Nahda Maesya Tsani Nonong Amalita Nurdalia Okia Dinda Kelana Permana, Dony Putri fajriyanti nur rahmad revi fadillah Reny Wulandari Rheizma Dewi Adiningtiyas Riska Ramadani Riwi Dyah Pangesti Seif Adil El-Muslih Sherly Amora Jofipasi Silvia Agustina Silvia Triana Suliswati, Yeni Susi Jumiati Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Ulya Syafitri.J Vidhiya Addini Wafiq Alya Aufa Wilia Sondriva Winartha, Mardia Wulan Septya Zulmawati Yenni Kurniawati Yenni Kurniawati Yulia Pertiwi Zamahsary Martha Zilrahmi, Zilrahmi