Article Per Year (5 Year)
p-Index From 2021 - 2026
4.986
P-Index
This Author published in this journals
All Journal PROSIDING SEMINAR NASIONAL JURNAL EKSAKTA PENDIDIKAN (JEP) JURNAL PENDIDIKAN TAMBUSAI Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Teknologi Pertanian Andalas Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Leibniz: Jurnal Matematika Journal of Mathematics UNP Aceh International Journal of Science and Technology UNP Journal of Statistics and Data Science Journal of Tourism Sciences, Technology and Industry Sultra Journal of Mechanical Engineering
Syafriandi Syafriandi
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search
Journal : UNP Journal of Statistics and Data Science

 1   2   3 
Vector Error Correction Model for Cointegration Analysis of Factors Affecting Indonesia's Economic Growth during the Pandemic Period Rizqa Fajriaty Fitri MY; Dina Fitria; Syafriandi Syafriandi; Zilrahmi
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-iss3/40

Abstract

Stabel economic growth is the ultimate goal of monetary policy s seen from the stability of the rupiah. The economic situation has decreased due to the spread of Covid-19. In an effort to stabilize the economy, the relationship between factors supporting Indonesia's economic growth is analyzed using the VECM approach. This approach is able to determine the long-term and short-term relationships of time series data. The model results after fulfilling several tests are three significant equations. The model explains that there is an effect in the short term of the inflation and BI Rate variables on inflation as well as the inverse effect between BI-rate one period earlier on the exchange rate. The cointegration coefficient is negative, it indicates that there is a short-term to long-term adjustment mechanism that occurs in the inflation variable. The two cointegration equations for the long term show that for the long term, inflation can be positively influenced by the visa variable. Variable BI-rate in the long run is influenced by the variable exchange rate and visa. The VECM model can explain more than 50% of the variables.
Classification for Covid-19 Affected Family Cash Aid Recipients Using Naïve Bayes Algorithm Mutiara Amazona Sosiawati; Syafriandi Syafriandi; Dony Permana; Zilrahmi
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-iss3/53

Abstract

The COVID-19 pandemic that occurred in Indonesia had a huge impact on the country's economy. One of the solutions set by the government in dealing with COVID-19 is to use APBD funds for social assistance in the form of cash, namely "Village Direct Cash Assistance" (BLT DD). With the hope that the people affected by COVID-19 can be helped by this assistance. There are several problems in the distribution of social assistance, one of which is recipients who are not on target. Therefore, it is necessary to use methods to correctly classify recipients. This study uses the Naïve Bayes method to classify people who receive and do not receive aid. From the results obtained on the confussion matrix, the people who received BLT DD assistance and were predicted to receive were as many as 33 people/KK, the people who did not receive BLT DD and were predicted not to receive as many as 34 people/KK, the people who received BLT DD and were predicted not to receive as many as 2 people/KK , and people who do not receive BLT DD and are predicted to receive as many as 6 people/families. As for the classification accuracy value obtained using the Naïve Bayes method is 89%, while the error rate obtained is 11%.
Grouping The Regencies/Cities in Indonesia Based on Expenditure Groups Inflation Value Using DBSCAN Method Meliani Putri; Dony Permana; Syafriandi Syafriandi; Zilrahmi
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-iss3/61

Abstract

The different characteristics of each regencies/cities in Indonesia can trigger differences in expenditure groups inflation value, the differences that occur will affect Indonesia’s national inflation. The purpose of this research is to create groups of regencies/cities based on expenditure groups inflation value and to identify the characteristics of the resulting groups. DBSCAN is a density-based non-hierarchical cluster method that can be used in data conditions that contain noise. The data used in this study is secondary data obtained from the publication of the Badan Pusat Statistik Republic of Indonesia (BPS RI) regarding expenditure groups inflation value. The analysis includes outlier detection, grouping using the DBSCAN method, performing cluster validation with silhouette coefficient, and identifying the characteristics of the clusters formed. Based on the grouping that has been done, two clusters are produced with a silhouette coefficient value of 0.65. The resulting cluster is cluster 0 in the form of a noise cluster consisting of 3 regencies/cities with regencies/cities that have a high category expenditure groups inflation value. Cluster 1 consisting of 87 regencies/cities is a cluster with regencies/cities that have a low category expenditure groups inflation value.
Geographically Weighted Panel Regression Modeling on Human Development Index in West Sumatra Amelia Fadila Rahman; Syafriandi Syafriandi; Nonong Amalita; Zilrahmi
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-iss3/63

Abstract

  The Human Development Index (HDI) is an important issue that has a negative impact on the field of human development and people's welfare in West Sumatra Province. The HDI is being attempted to be solved by identifying the contributing components. Geographically Weighted Panel Regression (GWPR) is a technique that can be used to find influencing factors and explain the influence of characteristic areas of observation. GWPR is a combination of panel data regression method with GWR which is used when the data has the influence of spatial heterogeneity. The purpose of this study is to form a GWPR model that will be applied to the HDI in Regencies/Cities in West Sumatera from 2019 to 2022. Modeling using GWPR Fixed Effect Model. With a minimum CV of 0,000208, the wighter function utilized is a fixed exponential kernel. The findings demonstrated that the model obtained had an of 99.9%, meaning the predictor variable could account for the model by this percentage. Variables that have a significant on HDI are Life Expectancy, Expected Years of Schooling, Mean Years of Schooling, and Purchasing Power Parity.
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.
Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data Fitria Panca Ramadhani; Dodi Vionanda; Syafriandi Syafriandi; Admi Salma
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-iss4/73

Abstract

CART (Classification and Regression Tree) is one of the classification algorithms in the decision tree method. The model formed in CART is a tree consisting of root nodes, internal nodes, and terminal nodes. After the model is formed, it is necessary to calculate its accuracy. The aim is to see the performance of the model. The accuracy of this model can be determined by calculating the predicted error rate in the model. The error rate prediction method works by dividing the data into training data and testing data. There are three methods in the error rate prediction method: Leave One Out Cross Validation (LOOCV), Hold Out (HO), and K-Fold Cross Validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Therefore, a comparison was made between the three error rate prediction methods with the aim of determining the appropriate method for the CART algorithm. This comparison was made by considering several factors, for instance, variations in the mean, the number of variables, and correlations in normally distributed random data. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. The results of this study indicate that the K-Fold Cross Validation method has the lowest median error rate and the lowest variance, so the most suitable error prediction method for the CART method is the K-Fold Cross Validation method
Step Function Intervention Analysis Model to Estimate Number of Aircraft Passengers in Minangkabau International Airport Velya Rahma Putri; Zilrahmi; Syafriandi Syafriandi; Dina Fitria
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-iss4/77

Abstract

Pandemic of Covid-19 had a quite big impact in air transportation. Minangkabau International Airport (BIM) has also felt the impact of this pandemic, namely a drastic decrease in the number of airplane passengers or there was an intervention event.a stable of airplane passengers is needed to indicate a stable economy in the transportation sector. If there are no passengers or flight activity in an area, it means that there are no entry and exit of economic activities, industrial activities, tourism and trade which help economic development. For this reason, it is necessary to do forecasting so that the problems that arise as a result of the drastic decline can be resolved by making new policies. Forecasting was carried out in this study to obtain an intervention model that will be used for forecast the next 12 months and predict how long the effect of the intervention will last for avoid further losses due to the continued decline in the number of passengers. The intervention model is considered better for data that has intervention variable compared to SARIMA models. The results of forecasting showed that the SARIMA model (0,1,1)(1,1,1)12 b = 0, s = 8, r = 1 is the best model that can be used for forecasting data containing interventions. This is evidenced by the small MAPE of 36.34% so that the model is feasible to use because the accuracy is quite high and close to the actual value.
Sentiment Analysis of TikTok Application on Twitter using The Naïve Bayes Classifier Algorithm Denia Putri Fajrina; Syafriandi Syafriandi; Nonong Amalita; Admi Salma
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-iss5/103

Abstract

TikTok is a popular social media platform that has gained a lot of attention lately. People of all ages are using this application to share short videos with their friends and followers. The content on TikTok is diverse and can be tailored to individual preferences, but there have been concerns about the presence of vulgar content that can be viewed by minors as there are no age restrictions. This has led to public scrutiny of the application on social media platforms like Twitter. To address this issue, sentiment analysis was conducted on reviews of the TikTok application to help users make informed decisions about its use. The aim of this analysis was to determine whether people's opinions about TikTok were positive or negative. To achieve this goal, researchers used the hashtag "TikTok Application".The results were classified into two categories positive and negative using the Naïve Bayes Classifier method. The analysis was carried out using 80% training data and 20% testing data, and the results showed an accuracy rate of 80.32%, with a recall value of 97% and a precision value of 78%. In general, positive feedback from Indonesians on the TikTok application is related to the invitation to download the TikTok application, while in negative feedback, information is obtained that the TikTok application is based on content that is inappropriate for TikTok users to download This information can help users make informed decisions about using the TikTok application.
Fuzzy Geographically Weighted Clustering Analysis for Sectoral Potential Gross Regional Domestic Product in West Sumatera Syifa Nabilah Wandira; Zilrahmi; Syafriandi Syafriandi; 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-iss5/109

Abstract

Gross Regional Domestic Product (GRDP) is the sum of the added value of all goods and services produced or produced in an area that arises as a result of various economic activities in a certain period. Each region certainly has its own advantages and potential, such as in sectors or business fields. GRDP inequality occurs due to differences in geographical conditions and natural resources in each region. The method that can be used to overcome this inequality is cluster analysis. Cluster analysis can group data objects that have the same characteristics so that the inequality that occurs can be seen from the clusters formed. Fuzzy Geographically Weighted Clustering is a clustering method using fuzzy logic which gives a geographic effect to each cluster so that it can better describe the actual cluster situation. The results of  research obtained 3 optimum clusters with different characteristics. Cluster 1 has high potential, cluster 2 has low potential and cluster 3 has medium potential in forming GRDP.
Comparison of Error Rate Prediction in CART for Imbalanced Data Lifia Zullani; Dodi Vionanda; Syafriandi Syafriandi; 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-iss5/117

Abstract

CART is one of the tree based classification algorithms. CART is a tree consisting of root nodes, internal nodes, and terminal nodes. The accuracy of the model in CART can be calculated by measuring prediction errors in the model. One common method used to predict error rates is cross-validation. There are three cross-validation algorithms, namely leave one out, hold out, and k-fold cross-validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Every algorithm has its shortcomings; hold out cannot guarantee that the training set represents the entire dataset, leave one out is very time-consuming and requires significant computation because it has to train the model as many times as there are data points, and k-fold provides longer computation time because the training algorithm must be run k times. In reality, the data often encountered is imbalanced. Imbalanced data refers to data with a different number of observations in each class. In CART, imbalanced data affects the prediction results. This research focuses on comparing error rate prediction methods in the CART model with imbalanced data. The study uses three types of data: univariate, bivariate, and multivariate, obtained from differences in population means and correlations between independent variables. The results obtained indicate that the k-fold algorithm is the most suitable error rate prediction algorithm applied to CART with imbalanced data.
Co-Authors Abda Abda Admi Salma Aldi Prajela Amelia Fadila Rahman Andriani Lubis Anita Fadila Cindy Caterine Yolanda Denia Putri Fajrina Dewi Adiningtiyas, Rheizma Dhafir, Muhammad Dina Fitria Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Eujeniatul Jannah Eujenniatul Jannah Fadhilah Fitri Fadhira Vitasya Putri Fitri, Fadhilah Fitria Panca Ramadhani Gezi Fajri Habibah, Sarah Husni, Nabila Indah Lestari JA, Sudarma kartika, anice Khathir*, Rita Lifia Zullani Lince Magriasti Martia Rosada Meliani Putri Meria Eliza Muhammad Idkham Mukhti, Tessy Octavia Mutiara Amazona Sosiawati Nikma Hasanah Nonong Amalita Nur Fadillah, Nur Nur Leli Permana, Dony Prettya Recha Putri, Eno Dwi Rachman, Fadlul rios Rizqa Fajriaty Fitri MY Safrizal Safrizal Salma, Admi Siti Nurhaliza Suliswati, Yeni Syifa Nabilah Wandira Tessy Octavia Mukhti Tinton Tonika Velya Rahma Putri Venny Oktarinda Winda Fariza, Winda Fariza Wita, Wita Resfi Ananta Wulan Septya Zulmawati Yarman Yarman, Yarman Yenni Kurniawati Yonggi, Yonggi Septa Pramadia Yulhendri Yulhendri Yunita Yunita Yurivo Rianda Saputra Zamahsary Martha Zilrahmi, Zilrahmi Zulkhairi Sofyan