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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Daya Matematis JESS (Journal of Education on Social Science) UNP Journal of Statistics and Data Science Jurnal ISO: Jurnal Ilmu Sosial, Politik dan Humaniora
Syafriandi Syafriandi
Universitas Negeri Padang
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Implementation of CART Method with SMOTE for Household Poverty Classification in Mentawai Islands 2023 Rheizma Dewi Adiningtiyas; 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.
Classification of Poor Households in Padang City Using the Naïve Bayes Algorithm with Synthetic Minority Oversampling Technique anice kartika; Dina Fitria; Syafriandi Syafriandi; Tessy Octavia Mukhti
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-iss4/241

Abstract

Poverty is a condition where a person is unable to meet minimum basic needs or a condition caused by the influence of development policies that have not been able to reach all levels of society. In Indonesia, the government has designed various programs to overcome poverty, but these programs are often not on target. One method to improve the effectiveness of the program is through proper classification of poor and non-poor households. This study uses the Naïve Bayes classification method which is popular in data mining to predict data categories based on the probability distribution of its features. However, challenges arise when the data is unbalanced between different classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) method is used to balance the data. Based on the analysis that has been carried out To determine the performance of Naïve Bayes using SMOTE and without SMOTE in classifying poor households in Padang City in 2023, classification using the Naïve Bayes method without SMOTE produced an accuracy value of 98%, precision of 0%, and recall of 0%. Meanwhile, the classification using the Naïve Bayes method with SMOTE produces an accuracy value of 90%, precision of 87%, and recall of 92% and the results of the criteria for poor households in Padang City in 2023 using Naïve Bayes can be seen from the results that the probability of poor households is much greater than that of non-poor households, therefore the data is classified as  group of households that are classified as poor.
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%
Analisis Sentimen Program MSIB pada Aplikasi X (Twitter) Menggunakan Algoritma Naïve Bayes Nabila Husni; Dodi Vionanda; Nur Leli; Syafriandi Syafriandi
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-iss2/361

Abstract

Certified Internships and Independent Studies (MSIB) is one of the programs of the Independent Learning-Independent Campus (MBKM) curriculum as a policy of the Kemendikbudristek. A government policy, especially in terms of education, will of course give rise to stigmas or feedback from the public regarding the policy. This research aims to find out public opinion regarding the MSIB program in the X (Twitter) application by sentiment analysis using the Naive Bayes Classifier algorithm. From this analysis, it was found that 84.6% of reviews had positive sentiments, while 16.4% of reviews had negative sentiments. Evaluation using the Naïve Bayes Classifier model shows that this model succeeded in classifying 85% of all data correctly, showing quite good performance in classifying the sentiment of these reviews.
Fuzzy C-Means Based Clustering of Central Java’s Regencies and Cities Using Economic Welfare Indicators 2023 Winda Fariza Winda Fariza; Syafriandi Syafriandi; Fadhira Vitasya Putri; Eujeniatul Jannah
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-iss4/414

Abstract

This study aims to cluster the regencies and cities in Central Java Province based on economic welfare indicators using the Fuzzy C-Means (FCM) method. The motivation for this research arises from the evident disparities in development outcomes across regions in Indonesia, particularly in Central Java. Several areas in this province continue to experience high poverty rates, low income, and poor human development despite improvements in labor force participation in others. Five key indicators were used: Labor Force Participation Rate (TPAK), Open Unemployment Rate (TPT), Percentage of Poor Population (PPM), Average Net Income (RPB), and Human Development Index (HDI). The data, obtained from Badan Pusat Statistik (2023), were standardized and analyzed using the FCM algorithm with optimal clusters determined via the elbow method. The clustering results show three distinct regional groupings: Cluster 0 includes areas with relatively high HDI and income despite lower labor participation and higher poverty; Cluster 1 comprises urbanized areas with high labor participation but lower HDI; and Cluster 2 represents the most disadvantaged areas with low income, high unemployment, and poor development outcomes. These findings offer a valuable foundation for targeted policy interventions and strategic regional development planning. Fuzzy C-Means proves to be an effective approach for uncovering nuanced regional profiles in socio-economic development.
Penerapan Model Log Linear Tiga Dimensi dalam Analisis Faktor Risiko Riwayat Sakit Maag Wita Resfi Ananta Wita; Eujenniatul Jannah; Siti Nurhaliza; Yenni Kurniawati; Syafriandi Syafriandi
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-iss4/433

Abstract

Gastritis, or commonly known as an ulcer, is an inflammatory condition caused by excess stomach acid that irritates the stomach lining. This disease is one of the most common in Indonesia and often disrupts daily activities, especially among students who face academic pressure, stress, and irregular diet. Based on Indonesia’s Health Profile Data, gastritis ranks sixth for inpatients with 330,580 cases, 60.86% of which occur in women, and seventh for outpatients with 201,083 cases, of which 77.74% occur in women. This study aims to examine the relationship between gastritis and demographic factors using a three-dimensional log-linear model. The method analyzes interactions between categorical variables to identify the best explanatory model. Results indicate that the most appropriate model involves the interaction between place of residence, gender, and history of stomach ulcers, showing that these factors collectively influence gastritis incidence. In conclusion, gastritis is not only related to physical health but also lifestyle and demographic factors. This study underlines the importance for students to manage stress, maintain healthy eating habits, and adopt preventive measures. The urgency of this research lies in raising awareness that untreated gastritis may reduce productivity and lead to more serious health problems.
Co-Authors Aldri Frinaldi Andriarmi Andriarmi anice kartika Anita Fadila Armiati Chairina Wirdiastuti Dina Fitria Dodi Vionanda Edwin Musdi Elita Zusti Jamaan Eujeniatul Jannah Eujeniatul Jannah Eujenniatul Jannah Fadhilah Fitri Fadhira Vitasya Putri Fauziah Putri Putri I Made Arnawa Laksono Trisnantoro Nabila Husni Nikma Hasanah Nofrinaldi Hendriko Nur Leli Rheizma Dewi Adiningtiyas Salma, Admi Siti Nurhaliza Tessy Octavia Mukhti Winda Fariza Winda Fariza Wita Resfi Ananta Wita Yanrizawati Yanrizawati Yarmaina Yarmaina Yerizon Yerizon Yonggi Septa Pramadia Yonggi Zamahsary Martha