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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Edukasi Matematika dan Sains Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Gantang NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Basicedu Eksakta : Berkala Ilmiah Bidang MIPA FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Jurnal Basicedu Journal of Mathematics UNP UNP Journal of Statistics and Data Science International Journal of Trends in Mathematics Education Research (IJTMER)
Dony Permana
Jurusan Matematika Universitas Negeri Padang
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Comparison of Error Rate Prediction Methods in Binary Logistic Regression Modeling for Imbalanced Data Bahri Annur Sinaga; Dodi Vionanda; Dony Permana; 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-iss4/86

Abstract

Binary logistic regression is a regression analysis used in classification modeling. The performance of binary logistic regression can be seen from the accuracy of the model formed. Accuracy can be measured by predicting the error rate. One method of predicting the error rate that is often used is cross-validation. There are three algorithms in cross-validation: leave one out, hold out, and k-fold. Leave one out is a method that divides data based on the number of observations so that each observation has the opportunity to become testing data but requires a long time in the analysis process when the number of observations is large. Hold out is the simplest algorithm that only divides the data into two parts randomly, so there is a possibility that important data does not become training data. K-fold is an algorithm that divides data into several groups, but k-fold is not suitable for data that has a small number of observations. In reality, real data is often imbalanced. In logistic regression,when the data is increasingly imbalanced, the prediction results will approach the number of minority classes. This research focuses on the comparison of error rate prediction methods in binary logistic regression modeling with imbalanced data. This study uses three types of data, namely univariate, bivariate, and multivariate, which are generated by differences in population mean and correlation between independent variables.The results obtained show that the k-fold algorithm is the most suitable error rate prediction algorithm applied to binary logistic regression.
Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD) rahmad revi fadillah; Dony Permana; Yenni Kurniawati; 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-iss4/91

Abstract

The exchange rate plays an important role in evaluating the Indonesian economy due to how much it affects the nation's overall financial situation. Activities for projecting future exchange rates can be conducted based on their dynamic characteristics. The purpose of this study is to predict the exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) using the Fuzzy Time Series Markov chain (FTS-MC) method. Researchers apply the FTS-MC approach to analyze the connection between every bit of historical data and the direction in which it moved in order to forecast future data movements. While the rupiah exchange rate Forecast against the USD between January 2 and January 31, 2023, with a MAPE value of 2.41% and a forecast accuracy score of 97.58% result. During up to 8 forecasted periods, the forecasting value gained by the FTS-MC approach is close to the actual value, and the next period is higher than the current value. The forecasting results graph further shows that the FTS-MC approach gives forecast values fluctuate around IDR15,800.
Improving Students Mathematical Critical Thinking Ability With Learning Modules Using Brain-Based Learning Models Nisa Ulkhairat Asfar; Dony Permana; Ahmad Fauzan; Yarman Yarman
Numerical: Jurnal Matematika dan Pendidikan Matematika Vol. 6 No. 1 (2022)
Publisher : Institut Agama Islam Ma'arif NU (IAIMNU) Metro Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v6i1.2415

Abstract

The quality of mathematics learning is influenced by good teaching material and can facilitate students to be active in knowledge and improve mathematical critical thinking skills. Mathematical critical thinking skills focus on activities in analyzing a more specific idea. Developing a learning module using a brain-based learning model is necessary to improve mathematical critical thinking skills. This study aims to determine the characteristics of the learning module using a brain-based learning model that is valid, practical, and effective. This type of research is development research using the Plomp model, which consists of three stages, namely the preliminary, development, and assessment stages. The module is made using a Brain-based learning model for class X Junior High School (SMA). The instruments used are validation sheets, observation sheets, questionnaires, interviews, and tests. The results showed that the learning module using the brain-based learning model for class X SMA was valid, practical, and effective. The validity of the module reaches 78.6. In contrast, the practicality of the module is 91.67%. In terms of effectiveness, it can be seen from students' average mathematical critical thinking ability test results, namely from 52.2 to 88.1
Penerapan Metode Regresi Kuantil pada Data yang Mengandung Outlier untuk Tingkat Kejahatan di Jabodetabek Arssita Nur Muharromah; Zamahsary Martha; Dony Permana; Tessy Octavia Mukhti
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-iss5/94

Abstract

The problem of crime is increasingly widespread in Indonesia. The crime rate in Jabodetabek is the second highest in Indonesia. In this study containing outliers, the appropriate method for this research is quantile regression. Quantile regression is the development of median regression or the Least Absolute Deviation (LAD) method which is useful for dividing data into two parts to minimize errors. however, this LAD is considered not good for modeling, therefore comes the quantile regression. Quantile regression is useful for overcoming the problem of unfulfilled assumptions in classical regression, namely the phenomenon of heteroscedasticity and quantile regression can model data that contains outliers. The quantile regression method approach is to separate or divide the data into certain parts or quantiles where it is suspected that there are differences in estimated values. The resulting measurement of the goodness of the model uses the coefficient of determination or R2 in each quantile. In this study, five quantiles were used, namely 0,05; 0,25; 0,50; 0,75; and 0,95. From the results of the analysis it is known that the best parameter estimation model is found in the 0,95 quantile with all independent variables having a significant effect on the dependent variable (crime rate). whereas in the 0,25 and 0,50 quantiles there are no independent variables that have a significant effect, this may be due to the influence of other factors not present in the study that affect each quantile.
Fuzzy Geographically Weighted Clustering Method for Grouping Provinces in Indonesia Based on Welfare Indicators Aspects of Information and Communication Technology (ICT) Hefiani Mustika Hasanah; Dina Fitria; Dony Permana; Zamahsary Martha
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-iss5/108

Abstract

The welfare of the people is a task and goal that must be realized by the Republic of Indonesia. To find out the condition of the welfare of the Indonesian people, it can be seen in eight areas of Indonesia's welfare indicators. Indicators The welfare of the Indonesian people is undergoing a digital transformation of information and communication technology (ICT) in 2021. However, there was a gap in ICT development due to geographical conditions and the distribution and dynamics of each region's society. Cluster analysis is a solution for target setting for better future decisions. Fuzzy Geographically Weighted Clustering (FGWC) is one of the cluster methods with fuzzy logic that considers geographical and population elements in grouping targets. The results of the research resulted in three optimum clusters with different characteristics for  each cluster based on indicators of ICT aspects of people's welfare. Cluster 1 has a medium status of ICT indicators of people's welfare and is located in the middle or at the end of the island, provinces from cluster 2 have a low status of ICT indicators of people's welfare with a medium area, while cluster 3 has a high status of ICT indicators of people's welfare with a large area or dense populations.
Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk Dewi Febiyanti; Nonong Amalita; Dony Permana; Tessy Octavia Mukhti
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-iss5/113

Abstract

Investors often make mistakes when making stock transactions even though having chosen good company stocks. The thing that needs to be considered in making stock transactions is to see the movement of stock prices. The movement of the stock price in PT Bank Rakyat Indonesia Tbk has changed in the form of a decrease or increase. The increase in stock price will provide benefits for investors by selling stocks. However, the occurrence of mistakes when choosing the time to make stock transactions results in investors being able to take high risks because stock prices fluctuate. Therefore, to anticipate the occurrence of high risk to investors, stock price predictions is made using a Backpropagation Neural Network (BPNN). BPNN can adapt quickly and is able to predict nonlinear data such as stock prices and produce a high level of accuracy. The results of this study obtained the best BPNN model, namely the BP(5,3,1) model with a Mean Absolute Percentage Error (MAPE) of 0,8193%. These results show that the model has good network performance so that it can predict stock prices well because it gets a small prediction error
Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province Widia Handa Riska; Dony Permana; Atus Amadi Putra; Zilrahmi
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-iss1/115

Abstract

In DKI Jakarta Province, the number of fires increases and decreases every year. For this reason, efforts need to be made to prevent and reduce the risk of fire. BPBD DKI Jakarta is responsible for this matter. However, for these efforts to be effective, information is needed regarding fire patterns that frequently occur. Fire patterns can be seen using K-Modes categorical clustering analysis. The data used is fire data in DKI Jakarta in 2018. The optimal number of clusters was obtained as 6 clusters based on the Davies Bouldin Index value with the smallest DBI value is 6,22. Of the six clusters, cluster 3 is the cluster with the highest number of fire cases. Cluster 3 has a centroid, namely that fire cases occurred on Friday, November, in Cakung District, due to an electrical short circuit, burning down residential houses and rarely causing minor injuries, serious injuries or deaths.
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.
Diagnosis of the type of delivery of pregnant women at Semen Padang Hospital Using the C4.5 Method rama novialdi; Dony Permana; Dodi Vionanda; Fadhilah Fitri
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-iss1/130

Abstract

The health of the mother and fetus is very important, but there are many challenges and risks associated with pregnancy and childbirth. According to WHO, in 2020 there were 287,000 cases of women dying during pregnancy and childbirth. Causative factors that affect the type of delivery include the age of pregnant women, MGG, systole, diastole, and pulse. One method that can be used to group the types of childbirth of pregnant women is classification. C4.5 is one of the methods used in forming decision trees to produce decisions. The purpose of C4.5 is to obtain attributes that will be the main criteria in the classification. Based on optimal tree results, the attribute that is the main criterion in classifying the type of delivery of pregnant women who give birth by caesar section and normal delivery at Semen Padang Hospital is MGG. Determination of classification results using confusion matrix resulted in an accuracy value of 74%, sensitivity of 80% to classify the type of delivery of pregnant women who gave birth caesar, and specificity of 66.67% to classify the type of delivery of pregnant women who gave birth normally.
Prediction of Palm Oil Production Results PT.KSI South Solok Using Ensemble k-Nearest Neighbor Nilda Yanti; Atus Amadi Putra; Dony Permana; Zilrahmi
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-iss1/136

Abstract

PT. KSI experienced production decrease that the cause of replanting that happened in 2022. In managing palm oil production PT. KSI has problems with palm oil production results not reaching out the targets so it can affect the Company's Work Plan and Budget, therefore it is very necessary to predict palm oil production results so that all palm oil production and processing activities can run according to plan. The ensemble technique is a method that is capable of making accurate predictions and is used very effectively in the kNN method, therefore there is no need to search for the best k value.Based on the results of the analysis that has been carried out, it can be seen that by using an ensemble the level of accuracy is 9.36%, which is considered high accuracy compared to just using a single kNN with k = 1 of 10.84%. So it can be concluded that the model has worked well with the data.
Co-Authors 01, Riska Addini, Vidhiya Ade Eriyen Saputri Admi Salma Admi Salma Afdhal, Afdhal Rezeki Afifah Zafirah Ahmad Fauzan Aidillah, Kerin Hagia Alandra, Cindy Resha Aldi Prajela Ali Asmar Andini Diva Luthfiyah april leniati Arnellis Arnellis Arssita Nur Muharromah Atus Amadi Putra Azma, Meil Sri Dian Bahri Annur Sinaga Bonita Nurul Afifah Carina, Fadhillah Meisya Denny Armelia Dewi Febiyanti Dina Fitria Dina Fitria, Dina Dinul Haq, Asra Dodi Vionanda Dwi Putri Amilia Dwi Ratih Listiani Yusri Edwin Musdi Elita Zusti Jamaan Elsa Oktaviani Elvina Catria Emi Suryani Putri Fadhilah Fitri Fadhillah Fitri Fadlan Rafly, Muhammad Fanni Rahma Sari Fauzan Arrahman Febri Ramayanti Fenni Kurnia Mutiya Fishuri, Nufhika Hana Rahma Trifanni Hana Zafirah haniyathul husna Hardi, Afifah Hasna, Hanifa Hefiani Mustika Hasanah Helma Helma Huriati Khaira I Made Arnawa Ibnul farizi, Gilang iin aini fitri Indonesia Irma Surya Anisa Isra Miraltamirus Kamil, Fakhri Kurnia Andrea Diva martha, Ully Martha Media Rosha Meidiani Sandra Meliani Putri Mohammad Reza febrino Muslimah, Nailul Amani Muthia Sakhdiah Mutiara Amazona Sosiawati nabillah putri Nadya Nadya nazhiroh, hanifah Nilda Yanti Nisa Ulkhairat Asfar Nisa, Farras Luthfyah Nonong Amalita Nur Fadillah, Nur Nurdalia Nurul Afifah Putra, M. Farel Rusde rahmad revi fadillah rama novialdi Refenia Usman Refina Rintani Revina Rahmadani Ridha Fajria rios Riry Sriningsih RIZKIA, DHEA PUTRI Ronald Rinaldo roza maylinda Salsabilla Khairani Siltima Wiska Siregar, Fauzan Al-Hamdani Sofni Fajriani SRI RAHAYU Suherman Suherman Suwanda Risky Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Vinka Haura Nabilla Wahda Aulia Assara Welgi Okta Irawan Widia Handa Riska Yarman Yarman Yatri Asri Yenni Kurniawati Yerizon Yerizon Yoga Perdana Yuli Andari Wulan Yulia Pertiwi Yulia Utami Putri Yurivo Rianda Saputra YUSWITA, AULIA Zamahsary Martha Zilrahmi, Zilrahmi