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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics GAUSS: Jurnal Pendidikan Matematika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications

Zilrahmi, Zilrahmi

Universitas Negeri Padang

Author-ID : 1819559

Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Multivariate Adaptive Regression Spline Method for Study Timeliness of the 2017 FMIPA UNP Student Rahmadani Iswat; Fadhilah Fitri; Atus Amadi Putra; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

The punctuality of study is the time period to complete an education, for undergraduate students is 4 years. One of the quality’s determining of higher education is students’ ability to complete their education on time. The purpose of this study is to see the best modeling results and the accuracy of the punctuality of study of class 2017 FMIPA UNP undergraduate students using MARS. MARS is a method of multivariate nonparametric regression between response variables and predictor variables. The type of research used is applied research. The predictor variables used in this study are Grade Point Average (GPA), gender, university entrance, major, school origin status and place of origin. While the response variable is punctuality of learning time. The results of trial and error showed that the best model was obtained from a combination (BF = 18, MI = 3 and MO = 2), with a minimum GCV value of 0.23182 and R2 value of 0.10045. From the model, it can be seen that the factors that significantly affect punctuality of learning time for FMIPA UNP students class 2017 are the X4 (majors) with an importance level of 100%, the X1 (GPA) with an importance level of 96.61%, X3 (university entrance) and the X5 (school origin status) with an importance level of 16.78 %. The classification accuracy on the 2017 student study timeliness is 64% based on graduating on time and not on time, with a classification error rate of 36%.

Comparison of Naïve Bayes and K-Nearest Neighbor for DKI Jakarta Air Pollution Standard Index Classification Nurdalia; Zilrahmi; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Data mining is the process of extracting and searching for useful knowledge and information using certain algorithms or methods according to knowledge or information. The data mining classification methods used in this study are Naïve Bayes and K-Nearest Neighbor. By using the Naïve Bayes and K-Nearest Neighbor methods, it is possible to classify the DKI Jakarta air pollution standard index in 2021 based on six air pollutants, namely dust particles (PM10), dust particles (PM2.5), sulfur dioxide (SO2), carbon monoxide. (CO), ozone (O3) and nitrogen dioxide (NO2). The test was carried out to determine the accuracy in predicting the DKI Jakarta air pollution standard index in 2021 using the confusion matrix evaluation value. So that the best performance of the two methods is found in the Naïve Bayes algorithm with high Naïve Bayes sensitivity values for all categories even though there are data in minority or unbalanced categories, and the frequency of data from each category or in this case the data is not balanced, the Naïve Bayes algorithm shows good performance in accuracy, sensitivity, specificity.

Application of Random Forest for The Classification Diabetes Mellitus Disease in RSUP Dr. M. Jamil Padang FAZHIRA ANISHA; Dodi Vionanda; nonong amalita; zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Diabetes Mellitus is a disease in which blood sugar levels go beyond normal (GDS>200 mg/dl). Diabetes Mellitus may be defined as an insulin function disorder in the pancreatic organ. Diabetes Mellitus is a world health problem as incidents of this disease are increasing in every part of the world, including Indonesia. Prevention and control of the disease need to be made so as not to cause complications in other organs even to death. Because of this, one needs to study a method to predict the occurance of this disease and to knows the variable that most affect a person suffered from it. This could be accomplished by using a classification methods. One of classification methods is Random Forest. In this case study using randomForest packages in RStudio software. In general, the result of this study are the smallest OOB’s error rates (%) and Variable Importance Measure (VIM) using Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) values.The classification by a Random Forest methods on the incidence of Diabetes Mellitus in RSUP Dr. M. Jamil Padang results in OOB’s error rate was 1,2% or accuracy rates was 98,8%. The most optimal model produced using mtry = 4 and ntree = 1000. If used MDA, the variables that most affect are Age, Polyphagia, Polyuria, HB, and BMI. While if used MDG, the variables that most affect are Age, Polyphagia, BMI, HB, and Delayed Healing.

Application of Random Forest to Identify for Poor Households in West Sumatera Province Febri Ramayanti; Dodi Vionanda; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Poverty is a socioeconomic problem in Indonesia. The number of people who were living in poverty in West Sumatera increases for 26.44 thousands from 2020 to 2021. The government has created programs to cope with poverty by taking into account the criteria for the poor households. These criteria have been developed by using the data obtained through The National Socioeconomic Survey (Susenas). However, instead of.showing the actual location of poor household, the existing data only interprets the number of poor household. Thus make the program less effective. This could be overcome by classification analysis of random forest (RF). RF is collection of many decision trees. Before fitting RF, one has to determine the values if three tuning parameters, mtry, ntree and node size. The result are the smallest OOB’s error rate (%) and Variable Importance Measure(VIM). The classification by RF in this research results in OOB’s error rate was 5.65% or accuracy rate was 94.35% with tuning parameter using mtry=5 and ntree=500. Based on the VIM, the poor household’s criteria include sources of drinking water such as protected or unprotected spring water and surface water, lighting tools such as non-PLN electricity or no usage of electricity, fuel for cooking such as charcoal and firewood, and the head of the household being self-employed, a family worker, or unpaid with at least a junior high degree.

Nonparametric Regression Modeling with Fourier Series Approach on Poverty Cases in West Sumatra Province Melin Wanike Ketrin; Fadhilah Fitri; Atus Amadi putra; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Poverty is a complex problem that has an impact on various social problems such as education, unemployment, health and economic growth. Therefore, the problem of poverty is important to overcome in order to create population welfare. One of the analyses that can be used to model the percentage of poverty is regression analysis. Regression analysis is divided into two approaches, namely parametric and nonparametric. Parametric regression has several assumptions while, the only assumption nonparametric regression shape of the curve does not form a certain pattern. There are several approaches to nonparametric regression, one of which is the Fourier Series. The purpose of this study is to model the percentage of poverty in West Sumatra Province. The unclear shape of the curve in the data used is a consideration for using nonparametric regression. Then it is known that the data used in this study is data per region which tends to have a fluctuating nature. So it is suitable to use the Fourier series approach. In this research, nonparametric regression modeling with one, two, and three oscillation parameters was attempted. The best model was obtained which consisted of two oscillation parameters with a Generalized Cross Validation (GCV) value of 2.110 and R² of 92.44%.

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

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

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%.

Comparison Fuzzy Time Series Cheng and Ruey Chyn Tsaur Model for Forecasting Sales at Empat Saudara Store Muhammad Alif Yustin; Zilrahmi; Atus Amadi Putra; Fadhilah Fitri
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-iss3/56

Trading business is a type of business that focuses on buying goods and reselling them with the aim of making a profit without making changes to the condition of the goods being sold. The problem that often occurs at the Empat Saudara Store is excess or deficiency in the stock of goods owned, where consumer demand is high but goods are insufficient and consumer demand is low but goods are available. One effort to overcome these problems is to make stable sales happen by forecasting to find out future sales. Forecasting is an activity that aims to estimate or predict what will happen in the future by using historical data from the past. The research method used is Fuzzy Time Series (FTS) because this method's forecasting system is to capture patterns from past data and then use it to project future data based on linguistic values. FTS models used are FTS Cheng and FTS Ruey Chyn Tsaur. The five-period forecasting results for FTS Cheng are 200,668.2 , 171,761.5 , 222,412.6 , 214,507.4 , 216,294.3 and for the FTS Ruey Chyn Tsaur model are 198,600 , 229,094.2 , 202,203.05, 230,804.80 ,6. With a MAPE value of the FTS Cheng model of 9.904% and a MAPE value of the FTS Ruey Chyn Tsaur model of 14.01%. From the forecasting results it can be concluded that the FTS Cheng model is better than the FTS Ruey Chyn Tsaur model in predicting sales at the Empat Saudara Store.

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

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

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.

Co-Authors Adinda Dwi Putri Admi Salma Admi Salma Afifa Lufti Insani Amanda, Abilya Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Della Amelia Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Fadhilah Fitri Fadhillah Fitri Fadlan Rafly, Muhammad Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fitri Mudia Sari Fitri, Fadhilah fitri, silfia wisa Frandito Rahmanesta Hamida, Zilfa Hari Wijayanto Hendrawan, Muhammad Ibnul farizi, Gilang Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Manja Danova Putri martha, Ully Martha Martia Rosada Meliani Putri Melin Wanike Ketrin Miftahurrahmi, Syifa Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Faisal Mukhti, Tessy Octavia Muslimah, Nailul Amani Mutiara Amazona Sosiawati nazhiroh, hanifah Nilda Yanti Nonong Amalita Nurdalia Permana, Dony Putri, Fadhira Vitasha Putri, Lathifa Putri, Sindy Amelia Rahmad Wanizal Pastha Rahmadani Iswat Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Sepriano Sepriano Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Nabilah Wandira Tessy Octavia Mukhti Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha

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