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All Journal PROSIDING SEMINAR NASIONAL JURNAL EKSAKTA PENDIDIKAN (JEP) Beta: Jurnal Tadris Matematika JURNAL EDUCATION AND DEVELOPMENT Eksakta : Berkala Ilmiah Bidang MIPA Menara Ilmu Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications

Nonong Amalita

Universitas Negeri Padang

Author-ID : 225497

Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Forecasting Shallot Prices in West Sumatra Province Using the Fuzzy Time Series Method of the Singh Model and the Cheng Model Huriati Khaira; Fadhilah Fitri; Nonong Amalita; Dony Permana
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Shallots are one of the leading spices that are widely used by humans as food seasoning and traditional medicine. The price of shallots always fluctuates which can affect the buying and selling of consumers and producers. Therefore, forecasting is used as a reference to be able to predict the price of shallots in the future and can provide convenience to the public for the condition of shallot prices in the next period. The forecasting method used is the fuzzy time series (FTS) method. FTS is a method whose forecasting uses data in the form of fuzzy sets sourced from real numbers to the universe set on actual data. Forecasting models used in this study are Singh's FTS model and Cheng's model. The data used is monthly data on shallot prices in West Sumatra Province for the period January 2018 to March 2022. The results obtained in this forecast are the Singh model FTS has a smaller MAPE value of 4.41% with a forecasting accuracy value of 95.59 %. This means that Singh's FTS model is better at predicting the price of shallots in West Sumatra Province.

Time Series Modeling on Stock Return at PT. Telecommunication Indonesia Tbk. Hana Rahma Trifanni; Dony Permana; Nonong Amalita; Atus Amadi Putra
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

One of the time series data modeling is the ARMA model which assumes constant volatility. However, in economic and financial data, there are many cases where volatility is not constant. This results in the occurrence of heteroscedasticity problems in the residuals, so a GARCH model is needed. In addition to heteroscedasticity, another problem with residuals is the asymmetric effect or leverage effect. For that we need asymmetric GARCH modeling. This study aims to compare the accuracy of the ARMA, GARCH, and asymmetric GARCH models. This research is an applied research. The data used is daily stock return data from February 2020 to February 2022 as many as 488 data. The results showed that the best model in modeling stock return volatility is ARMA(0,1). The accuracy of this model is very good with MAD value of 0,0018644 and RMSE value of 0,0025352.

Grouping The Districts in Sumatera Region Based on Economic Development Indicators Using K-Medoids and CLARA Methods Retsya Lapiza; Syafriandi; Nonong Amalita; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Inequality in economic development is an economic problem that is often felt by developing countries. In Indonesia, one of the regional areas that has not yet experienced equal distribution of economic development is the regencies/cities of the Sumatera Region. This study aims to determine regional groups and compare the results of grouping with the K-Medoids and CLARA methods. The K-Medoids and CLARA methods are non-hierarchical methods that are strong against outliers. While the best selection method is done by comparing the silhouette coefficient. The results obtained in this study using the K-Medoids and CLARA methods with 2 groups being better than forming 3 groups. The K-Medoids method resulted in cluster 1 as many as 59 districts/cities and cluster 2 as many as 95 districts/cities. Meanwhile, the grouping of districts/cities using the CLARA method with 2 groups resulted in cluster 1 as many as 74 districts/cities and cluster 2 as many as 80 districts/cities. From the comparison of the two methods, the silhouette coefficient values using the K-Medoids and CLARA methods are 0.13 and 0.15 respectively. Therefore, the CLARA method with 2 groups gave better cluster results

Comparison of Forecasting Using Fuzzy Time Series Chen Model and Lee Model to Closing Price of Composite Stock Price Index Mohammad Reza febrino; Dony Permana; syafriandi; Nonong Amalita
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

Investment is an activity to invest with the hope that someday you will get a number of benefits from theinvestment result. In investing, analyzing is important to see the current situation and condition of stock. Investorscan forecast stock prices by looking at trends based on data movements from stock prices in the past. Fuzzy TimeSeries (FTS) was used in this study to forecast. Fuzzy time series is a forecasting technique that uses patterns frompast data to project future data in areas where linguistic values are formed in the data. This study compares theclosing price of composite stock forecasting using the fuzzy time series chen and lee models. The JCI's closing pricefor the following period is 6,904 and has a Mean Absolute Percentage Error (MAPE) of 4.03%, according to the chenfuzzy time series method. In contrast, utilizing Lee's fuzzy time series method, the predicted JCI closing price for thefollowing period is 7,046, with a MAPE value of 3.10 percent. It can be concluded from the forecasting results of theChen and Lee methods that the Lee model FTS is superior to the Chen model FTS in predicting the JCI closing price.

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.

The SMOTE Application of CART Methods for Coping Imbalanced Data in Classifying Status Work on Labor Force in the City of Padang Andini Yulianti; Fadhilah Fitri; Nonong Amalita; Dodi Vionanda
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-iss3/12

Employment issues are one of the main concerns in every country, especially in developing countries including Indonesia. Employment problems faced by Indonesia are the lack of job opportunities, excess labor, and the uneven distribution of labor. This is because the growth of the labor force is higher than the growth of existing job opportunities, so that many workers do not get jobs which will cause unemployment. The city of Padang is the city that has the highest unemployment rate in West Sumatra from 2013 to 2021. The development of a smart city and identification of factors that influence unemployment is one of the efforts to reduce unemployment. This study uses the CART method to determine the factors that affect the number of the workforce in the city of Padang. The advantage of the CART method is that it is easy to interpret the results of the analysis, but the accuracy of the classification tree is low due to data imbalance. Therefore, this study uses the SMOTE method to overcome these problems. The optimal classification tree is formed from 8 terminal nodes and involves 4 explanatory variables consisting of marital status (X3), education level (X4), gender (X2) and age(X1), 5 terminal nodes which classify the labor force into the working category and 3 terminal nodes which classify the labor force into the unemployed category.

Sentiment Analysis og Goride Services on Twitter Social Media Using Naive Bayes Algorithm Puti Utari Maharani; Nonong Amalita; 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-iss3/41

Online motorcycle taxi is an application-based transportation technology innovation. Online motorcycles offer relatively low prices and offer discount features. However, the existence of online motorcycles creates congestion problems and conflicts between conventional transports. One such online motorcycle taxi service is GoRide. This GoRide feature is derived from the Gojek application. The emergence of GoRide raises public opinion and wants to judge an object openly through social media, one of which is Twitter. The assessment given by society is an analytical textual opinion. Sentiment analysis is used to detect opinions in the form of a person's judgment, evaluation, attitude, and emotion. The textual classification algorithm used in this study was Naive Bayes. This research aims to find out the public sentiment towards GoRide's service as an online motorcycle taxi in positive and negative categories and to find out the accuracy results of the Naive Bayes algorithm against GoRide's service. Research data was obtained using the API provided by Twitter developers. Analysis techniques are performed by text preprodeing, data labelling, word weighting, classification, then performance evaluation of classification. The results of the positive category sentiment classification are 698 data, while the negative category sentiment is 517 data. The Naive Bayes algorithm's performance evaluation results obtained an accuracy rate of 77.78%. So as a whole, GoRide can be categorized as a good service.

Comparison of Distance Function in K-Nearest Neighbor Algorithm to Predict Prospective Customers in Term Deposit Subscriptions Muhammad Tibri Syofyan; Nonong Amalita; Dodi Vionanda; Dina Fitria
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-iss3/47

Data mining is often used to analysis of the big data to obtain new useful information that will be used in the future. One of the best algorithms in data mining is K-Nearest Neighbor (KKN). K-NN classifier is a distance-based classification algorithm. The distance function is a core component in measuring the distance or similarity between the tested data and the training data. Various measure of distance function exist make this a topic of kind literature problems to determining the best distance function for the performance of the K-NN classifier. This study aims to compare which distance function produces the best K-NN performance. The distance function to be compared is the Manhattan distance and Minkowski distance. The application of K-NN classifier using bank dataset about predict prospective customers in Term Deposit Subscriptions. This study show that Minkowski distance on K-NN algorithm achieved the best result compared to Manhattan distance. Minkowski distance with power p = 1.5 produces an accuracy rate of 88.40% when the K value is 7. Thus, performance of K-NN algorithm using Minkowski distance (p=1,5, K=7) is best algorithm in predicting prospective costumers in Term Deposit Subscription

Modeling Human Development Index in Papua and West Sumatera with Multivariate Adaptive Regression Spline Yulia Pertiwi; Dony Permana; Nonong Amalita; Admi Salma
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-iss3/54

The Human Development Index (HDI), is an indicator of the successful development of the quality of human life. The high value of HDI, shows the better development of a region. The purpose of this study is to model and determine the factors affect HDI in Papua Province and West Sumatera Province, using Multivariate Adaptive Regression Spline (MARS). MARS is one of the modeling methods that can handle high-dimensional data. The result of this study showed that the best MARS model for Papua Province is a combination of (BF=24, MI=2, and MO=0) with a minimum GCV value of 0.55953. while the best MARS model for West Sumatera Province is a combination of (BF=24, MI=2, and MO=0) with a minimum GCV value of 0.02697. Based on the model, the factors that significantly affect HDI in Papua Province and West Sumatera Province are average years of schooling (X2), adjusted per-capita income (X6), life expectancy (X1), percentage of poor people (X4), and gross regional domestic product (X3). The percentage level of importance of each variable for Papua Province is 100%, 45.26%, 29.24%, 6.55%, and 6.27%. Meanwhile, for West Sumatera Province it is 100%, 96.73%, 57.54%, 34.13%, and 29.6%, respectively. So in this case, based on the results of the study, the average years of schooling (X2) is the variable that most influences HDI in the two regions, with an importance level of 100%.

Analysis of Factors Influencing the Population Growth Rate in West Sumatra Using Geographically Weighted Logistic Regression Rizqia Salsabila; Atus Amadi Putra; Nonong Amalita; 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-iss3/59

The model of Geographically Weighted Logistic Regression (GWLR) was the development of a model of logistic regression that was implemented to data in spatial. GWLR model parameter estimation was carried out at each location for observation using spatial weighting. The research purposes was to reveal the GWLR model on the dichotomous data of the Population Growth Rate (PGR) indicator in each Districts/Cities in West Sumatra in 2020 and learn more factors that influence the probability that the population growth rate will increase in 19 Districts/Cities in West Sumatra in 2020. The parameters estimation of the GWLR model uses the Maximum Likelihood Estimation (MLE) method. Spatial weighting for parameter estimation is determined using the Fixed Gaussian Kernel weighting function and determining the optimal bandwidth using Akaike's Information Citerion (AIC) criteria. The variable of response that is categorical in this study is the rate of population growth in each districts/cities in West Sumatra in 2020 and the predictor variables are the couples number of childbearing age, the live births number, the in-migration number, and the out-migration number. The reseacrh result obtained from research were that the GWLR model is better than the logistic regression model and 4 groups of Districts/Cities are formed based on factors that affect the increase in population growth rate.

Co-Authors Addini, Vidhiya Ade Eriyen Saputri Adinda Dwi Putri Admi Salma Aldwi Riandhoko Ali Asmar Amanda, Abilya Amelia Fadila Rahman Andini Yulianti Anggi Adrian Danis Anjelisni, Nining april leniati Arnellis Arnellis Atika Ahmad Atus Amadi Putra Azwar Ananda Chairina Wirdiastuti Cindy Febrianita Denia Putri Fajrina Dewi Febiyanti Dewi Murni Dina Fitria Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Dwi Sulistiowati Edwin Musdi Elita Zusti Jamaan Elsa Oktaviani Fadhilah Fitri Fajrin Putra Hanifi fajriyanti nur, Putri Fatma Yulia Sari Faulina FAZHIRA ANISHA Fikra, Hidayatul Fitri, Fadhilah Gezi Fajri Ghaly, Fayyadh Hamida, Zilfa Hana Rahma Trifanni haniyathul husna Hasna, Hanifa Helma Helma Helma Helma Herlena Purnama Sari Huriati Khaira Ichlas Djuazva Inna Auliya Jihe Chen Juwita Juwita Khairani, Putri Rahmatun Lilis Sulistiawati Media Rosha Media Rosha Meira Parma Dewi Melly Kurniawati Miftahurrahmi, Syifa Minora Longgom Mohammad Reza febrino Mudjiran Mudjiran Muhammad Tibri Syofyan Mukhti, Tessy Octavia nabillah putri Nadha Ovella Syaqhasdy Natasya Dwi Ovalingga, natasyalinggaa Nini Erdiani Nur Fadillah, Nur Nurhizrah Gistituati Okia Dinda Kelana Oktaviani, Bernadita Permana, Dony Prida Nova Sari Puti Utari Maharani Rahma, Dzakyyah Resti Febrina Retsya Lapiza Rizki Amalia, Annisa Rizqia Salsabila Rusdinal Rusdinal Saddam Al Aziz Safitri, Melda Salma, Admi Seif Adil El-Muslih Shavira Asysyifa S Sondriva, Wilia Sujantri Wahyuni Suparman Suparman Swithania Rizka Putri Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Tamur, Maximus Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Venny Oktarinda Viola Yuniza Wella Saputri Wulan Septya Zulmawati Yarman Yarman, Yarman Yenni Kurniawati Yulia Pertiwi Zamahsary Martha Zilla Zalila Zilrahmi, Zilrahmi

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