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All Journal Pelita Eksakta UNP Journal of Statistics and Data Science
Admi Salma
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics Social Sciences Other

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Implementation of CART Method with SMOTE for Household Poverty Classification in Mentawai Islands 2023 Dewi Adiningtiyas, Rheizma; Admi Salma; Syafriandi Syafriandi; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/232

Abstract

Poverty is a condition in which individuals or groups are unable to fulfill their basic needs due to economic pressure or limited resources. The Classification and Regression Trees (CART) method is a classification technique in the form of a classification tree, which describes the relationship between independent and dependent variables. Data imbalance can lead to low sensitivity values and area under curve (AUC) values. One method that can overcome unbalanced data is to perform Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with the addition of artificial data in the minority class at a stage before analyzing the data. The purpose of this research is to compare the model without and with SMOTE in CART method. The use of SMOTE is applied to balance the amount of data on each poor household. The accuracy value of the method without SMOTE is 89% while with the SMOTE method is 79%. However, the sensitivity value has increased by 80%. Meanwhile, the AUC value in the CART method with SMOTE increased by 31%. So in this study it can be concluded that CART classification analysis with SMOTE is able to provide better performance compared to CART classification analysis without SMOTE.
Prediksi Harga Emas Dunia Menggunakan Metode k-Nearest Neighbor Nanda P, Muhamad Rayhan; Zamahsary Martha; Dodi Vionanda; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/314

Abstract

This research aims to predict world gold prices using the k-nearest neighbor (KNN) method with secondary data from the London Bullion Market Association (LBMA) in the form of monthly time series data from January 2019 to December 2023. In the analysis process, the data is divided into two parts: 80% for training data (January 2019 - December 2022) and 20% for testing data (January - December 2023). The analysis results show that the Mean Absolute Percentage Error (MAPE) value of the KNN method is 4.5%, which indicates a very good level of accuracy. With a MAPE below 10%, the KNN model is proven to be able to accurately predict world gold prices. Gold price predictions for the period January to December 2024 show a consistent upward trend, which is influenced by factors such as global economic fluctuations, increased gold demand, and geopolitical uncertainty. These results show that the KNN model is reliable as a tool for forecasting future world gold prices.
How MUI Fatwa Changes Indonesia Mindset towards Pro-Israel Boycott Products using the Naïve Bayes Classification Method Jumiati, Susi; Dodi Vionanda; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/326

Abstract

Boycotting pro-Israel products has become a popular topic on social media, both in Indonesia and globally. This research aims to analyze the sentiments of Indonesian using the Naive Bayes classification method regarding the boycott before and after the issuance of MUI Fatwa No.83/2023. Through sentiment and word cloud analysis of 3327 tweets, it was found that discussions remained consistent and were not influenced by MUI Fatwa. The sentiment of the majority of Indonesian regarding the boycott of pro-Israel products is positive, with full support for this action. MUI Fatwa has had an impact on the sentiment of Indonesian, as can be seen from the increase in positive sentiment after the fatwa was released. Word cloud analysis shows that both before and after November 8, 2023, the top one word that appears in the word distribution is exactly the same, namely 'boycott'. This similarity shows that the discussion topics that developed on the Twitter platform remained consistent, both before and after the release of MUI Fatwa Indonesian netizens have uniformly discussed boycotting products that support Israel as a form of rejection of the genocide carried out by that country in Gaza, Palestine.
Panel Data Regression on Gross Regional Domestic Product in West Sumatra Eujeniatul Jannah; Admi Salma; Syafriandi Syafriandi
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/328

Abstract

Economic growth is assessed by the amount of gross regional domestic product (GRDP) as part of the development of people's welfare. West Sumatra Province needs a development plan that is able to produce GRDP per capita population of 9 to 11 times the current economic growth. To examine the economic growth of a country, not only using cross section data, because it is important to observe the behavior of the research unit over several periods of time. So that research is carried out whether there is an influence on the level of labor force participation, average length of schooling, life expectancy, and the number of poor people on GRDP per capita in districts / cities in West Sumatra in 2020-2023 using panel data regression. This research is an applied research with secondary data obtained from the Regency / City RPJPD document and the official website of the West Sumatra Statistics Agency consisting of 19 districts / cities as objects and the period 2020-2023.   The factors that are significant to GRDP per capita are average years of schooling and life expectancy with the selected model, namely the fixed effect model. The model has a good ability to explain the dependent variable with a value of 82.72%
Analysis of The Effect of Unemployment, Economic Growth and Inflation on Poverty in West Sumatra Province Ulya Syafitri.J; Zilrahmi; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/329

Abstract

Poverty remains a major challenge in West Sumatra, although various efforts have been made to improve community welfare. In this context, it is important to understand the factors that influence poverty levels. Unemployment, economic growth and inflation are several important variables that can have a significant effect on poverty levels. Unemployment is one of the problems that is often associated with poverty. On the other hand, strong economic growth has the potential to reduce poverty levels by creating new job opportunities and increasing people's incomes. However, non-inclusive economic growth can increase social inequality and uneven income distribution, which in the end can worsen poverty. Apart from that, inflation can also affect poverty levels by reducing people's purchasing power, especially those with low incomes. This research aims to analyze the effect of unemployment, economic growth and inflation on poverty levels. The multiple linear regression analysis method is used to test the relationship between the independent variables (unemployment, economic growth and inflation) and the dependent variable (poverty). Based on the research findings, it can be concluded that unemployment, economic growth and inflation contribute to poverty in West Sumatra at 49,35% and the remainder 50,65% is explained by other factors outside the model.The analysis indicates a significant linear influence on unemployment and economic growth on poverty in West Sumatra and there is no significant linear impact of inflation  on poverty in West Sumatra.
Implementation of the Self Organizing Maps (SOMS) Method in Grouping Provinces in Indonesia Based on the Number of Crimes by Type of Crime fajriyanti nur, Putri; Tessy Octavia Mukhti; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/334

Abstract

Crime cases are often the main topic of daily news in various media in Indonesia. Some of these crime cases are detrimental to the surrounding community and some are detrimental and these actions cannot be avoided in human life because they have become one type of social phenomenon. To protect the community by providing a sense of security and peace, the Indonesian government, especially the police, must pay attention to conditions like this. The results of this study used the Self Organizing Maps (SOMs) method to obtain 3 clusters with the characteristics of each cluster. The first cluster with a low impact crime rate consists of 29 provinces. The second cluster with a moderate impact consists of 3 provinces showing the most dominant crime rate, namely crimes related to fraud, embezzlement, smuggling & corruption compared to other clusters. The third cluster with a high impact consists of 2 provinces with the most prominent characteristics by showing almost all indicators of the number of crimes according to the type of crime experiencing the highest average crime cases compared to other clusters.
Classification of Factors Affecting Preeclampsia in Pregnant Women at RSUP. Dr. M. Djamil Padang using the CART Algorithm YUSWITA, AULIA; Dina Fitria; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/341

Abstract

Preeclampsia is a pregnancy-specific disease characterized by hypertension and proteinuria that occurs after 20 weeks of gestation. Preeclampsia itself is caused by various factors that can influence the occurrence of preeclampsia in pregnant women, including age, parity, history of hypertension, obesity, and kidney disorders. This study aims to determine the risk factors influencing preeclampsia based on preeclampsia diagnosis at RSUP Dr. M. Djamil Padang by classifying each variable using a decision tree. This research employs the CART (Classification and Regression Tree) algorithm. The CART algorithm has a binary nature and can analyze response variables that are either categorical or continuous, handle data with missing values, and produce an interpretable tree structure. The study results indicate that the primary risk factor for preeclampsia is parity. The model developed using the CART algorithm was tested using a confusion matrix, yielding an accuracy of 54%, a precision of 33.3% in correctly classifying patients with mild preeclampsia (PER), and a recall of 23.8% in classifying patients with severe preeclampsia (PEB).
Process Capability Analysis of OPC Cement Production Using Statistical Process Control and IMR Method: Blaine Test Evaluation Alya Aufa, Wafiq; Yenni Kurniawati; Admi Salma; Darwas
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-iss3/379

Abstract

The main challenge in cement production at PT Semen Padang is maintaining consistent product quality, particularly the fineness of cement particles measured by the Blaine test. Variations in raw materials and the production process can cause fluctuations in quality, which affect the performance of the final product. Therefore, it is crucial to monitor and control process stability and capability to consistently meet product specifications. Based on the Statistical Process Control (SPC) analysis using Individuals and Moving Range (I-MR) control charts on 28 observations of Ordinary Portland Cement (OPC) Blaine values from February 2025, one out-of-control point was detected on the Moving Range chart between observations 16 and 17, indicating a significant variation. However, all points on the Individuals chart remained within control limits, suggesting that the individual process values were still under control. After revising the outlier data, the process was confirmed stable. Process capability analysis showed a Cp value of 2.17 and a Cpk value of 1.98, indicating that the production process is not only statistically stable but also highly capable of meeting quality specifications. Therefore, despite some variation between data points, the cement production process at PT Semen Padang can be considered stable and capable. Nevertheless, periodic evaluations are recommended to maintain consistent product quality and provide strategic recommendations for the Quality Assurance division in implementing data-driven quality control.
Comparison Performance of SARIMA and Exponential Smoothing Holt-Winter’s models for Forecasting turnover PT. Indah Logistik Cargo Padang Silvia Triana; Dina Fitria; Yenni Kurniawati; Admi Salma
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-iss4/432

Abstract

Forecasting is an important part of corporate decision making. With forecasting, companies can predict future conditions and demand so that they can make appropriate and strategic decisions. PT. Indah Logistik Cargo Padang's turnover data contains trend and seasonal elements that are forecasted using a time series model. This study was conducted to determine the best model for forecasting PT. Indah Logistik Cargo Padang's revenue in the coming period. The methods used in this study are the SARIMA method and Holt-Winter's Exponential Smoothing. The best model was obtained from the results of a comparative analysis of the two methods, as seen in the forecasting error rate determined by the mean absolute percentage error value. For forecasting the revenue of PT. Indah Logistik Cargo Padang, the best model used was SARIMA with a MAPE value of 3.9%.
Co-Authors Addini, Vidhiya Ade Eriyen Saputri Alya Aufa, Wafiq Anita Fadila Atus Amadi Putra Bahri Annur Sinaga Belia Mailien Carina, Fadhillah Meisya Darwas Denia Putri Fajrina Dewi Adiningtiyas, Rheizma Dina Fitria Dina Fitria Dodi Vionanda Dony Permana Eujeniatul Jannah Fadhilah Fitri fajriyanti nur, Putri Fitria Panca Ramadhani Futiah Salsabila harelvi, dhea afrila Hariati Ainun Nisa Hasna, Hanifa Ibnul farizi, Gilang Ihsan Dermawan Ihsanul Fikri Indah Lestari Jimmi Darma putra Jumiati, Susi Kurnia Andrea Diva Mardhiatul Azmi Muthia Sakhdiah Nanda P, Muhamad Rayhan Nonong Amalita Nurdalia Okia Dinda Kelana rahmad revi fadillah Seif Adil El-Muslih Sherly Amora Jofipasi Silvia Agustina Silvia Triana Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Ulya Syafitri.J Wulan Septya Zulmawati Yenni Kurniawati Yenni Kurniawati Yulia Pertiwi YUSWITA, AULIA Zamahsary Martha Zilrahmi, Zilrahmi