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All Journal PROSIDING SEMINAR NASIONAL Pendidikan Matematika JURNAL PENDIDIKAN TAMBUSAI Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science

Atus Amadi Putra

Departemen Statistika, Universitas Negeri Padang

Author-ID : 351174

Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics Social Sciences Other

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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.

Adding Exogenous Variable in Forming ARIMAX Model to Predict Export Load Goods in Tanjung Priok Port Elvina Catria; Atus Amadi Putra; Dony Permana; 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

The main idea of world maritime has been launched by the Indonesia’s Government through the development of inter-island connectivity, namely a logistics distribution line system using cargo ships with scheduled routes. However, in terms of inter-island sea transportation connectivity using sea transportation, the number of ships used for loading and unloading activities at Tanjung Priok in 2020 reached 11,876 units, which number decreased by 12.6% compared to the previous year, this figure was not sufficient for transportation of Indonesian loading and unloading goods (exports). This condition is important to note because the implementation of sea transportation, especially for sea toll transportation, if it cannot reach all regions, will cause freight transportation in some areas to be limited and regional economic growth cannot be distributed evenly. The purpose of this study is to predict the number of goods loaded (exported) at the Port of Tanjung Priok, by establishing an export forecasting model. Exogenous variable in the form of the Indonesian Wholesale Price Index. After analyzing the data, the order of the ARIMA model (5,1,1) was obtained as a parameter to estimate the ARIMAX model. From the ARIMAX model (5,1,1), the model's accuracy rate is 13.25% which is quite feasible to use to predict the total export cargo for the period January 2021-December 2021. Forecasting results show better fluctuations than in 2020.

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 Naive Bayes Method and Binary Logistics Regression on Classification of Social Assistance Recipients Program Keluarga Harapan (PKH) Fanni Rahma Sari; Fadhilah Fitri; Atus Amadi Putra; Dony Permana
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

Population density is one of the causes of economic inequality in society. One of the solutions provided by the government is to distribute social assistance. In 2007 the government created a social assistance program called the “Program Keluarga Harapan” (PKH) with the aim of alleviating poverty. There are several problems in the distribution of social assistance, one of which is receiving aid that is not right on target. Therefore, an appropriate method is needed in classifying the recipients of social assistance properly. This study will use two methods, namely Naive Bayes and Binary Logistic Regression to compare which method is better on the data used. The data used is the DTKS data for PKH assistance recipients in the Anduring Village in 2020. Based on the results obtained, the accuracy of the Naive Bayes method is 70% and Binary Logistic Regression is 73%. So the best method in measuring classification is Binary Logistic Regression.

Comparison of the Performance of the K-Means and K-Medoids Algorithms in Grouping Regencies/Cities in Sumatera Based on Poverty Indicators Mardhiatul Azmi; Atus Amadi Putra; Dodi Vionanda; 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

K-Means is a non-hierarchical approach that separates data into a number of groups according on how far an object is from the closest centroid. K-Medoids is a non-hierarchical clustering technique that separates data into a number of groups according on how far away an object is from the closest medoid. The two approaches were put to the test using data on poverty in Sumatra in 2021, when the Covid-19 outbreak had caused the poverty rate to increase from the year before. This research is an applied research which begins by studying relevant theories. The data used in this study is secondary data sources from the BPS website regarding poverty indicators. This study aims to determine regional groups and compare the results of grouping with the k-means and k-medoids methods. To find out the best performance between the two methods, that is by looking at the lowest Davies Bouldin Index (DBI). The results of this study are the k-means algorithm produces as many as 34 districts/cities incorporated in cluster 1, 52 districts/cities in cluster 2, 23 districts/cities in cluster 3, and 45 districts/cities in cluster 4. k-medoids, namely in clusters 1, 2, 3, and 4, respectively, as many as 53, 40, 37, and 24 districts/cities. Based on the results of the grouping, the DBI k-means of 1,584 and k-medoids of 2,359 were obtained. This means that the k-means algorithm is better than the k-medoids, because the k-means DBI is smaller than the k-medoids.

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%.

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 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.

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.

Regresi Komponen Utama dalam Mengatasi Multikolinearitas pada Faktor yang Mempengaruhi Pendapatan Asli Daerah di Sumatera Barat Syifa Amelia; Atus Amadi Putra
Jurnal Pendidikan Tambusai Vol. 7 No. 2 (2023): Agustus 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pendapatan Asli Daerah (PAD) merupakan salah satu indikator yang menentukan derajat kemandirian suatu daerah. Faktor-faktor yang mempengaruhi PAD yaitu pengeluaran daerah, jumlah penduduk, PDRB, angkatan kerja dan IPM. Tingginya korelasi antar faktor tersebut dapat menyebabkan multikolinearitas. Multikolinearitas dapat dideteksi dengan Variance Inflation Factor (VIF). Penelitian ini menggunakan data PAD Sumatera Barat tahun 2020 yang bersumber dari website BPS Sumbar. Dari hasil pengujian diketahui bahwa terdapat multikolinearitas antar variabel independen pada data. Untuk mengurangi nilai korelasi, dilakukan standarisasi data dan transformasi menjadi variabel komponen utama. Dari transformasi tersebut diketahui komponen utama W1 dan W2 dapat menjelaskan 30% dan 16,9% dari keragaman data. Model PCR yang dihasilkan yaitu dan masing-masing variabel berpengaruh secara signifikan terhadap PAD.

Co-Authors Admi Salma Azma, Meil Sri Dian defal aditya defran Dina Fitria Dinda Fitriza Dodi Vionanda Dony Permana Elsa Oktaviani Elvina Catria Erni Syofna Fadhilah Fitri Fanni Rahma Sari Febiola Putri, Febi Findri Wara Putri Futiah Salsabila Hana Rahma Trifanni Hasnatul Huda Helma Helma Mardhiatul Azmi Melda Elfiyanti Melin Wanike Ketrin Muhammad Alif Yustin Nilda Yanti Nonong Amalita Nurhamidah Nurhamidah Okia Dinda Kelana Puti Utari Maharani Rahmadani Iswat Rahmawita Rahmawita Rina Febriana Rini Hartati Rizqia Salsabila Siskha Maulana Basrul Sofia Edriati Syifa Amelia Tessy Octavia Mukhti Villia Anggraini Vina Delfi Viola Yuniza Widia Handa Riska Yenni Kurniawati Yerizon Yerizon Yolian Widya Ningsih Yulyanti Harisman Zamahsary Martha Zilrahmi, Zilrahmi

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