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All Journal Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science
Dina Fitria
Departemen Statistika Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics Social Sciences Other

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Analysis of Teacher’s Competence About Mathematics Materials for National Final Examination Syafriandi Syafriandi; Dina Fitria
Pelita Eksakta Vol 1 No 1 (2018): Pelita Eksakta Vol. 1 No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol1-iss1/5

Abstract

Principal component in teaching mathematics for teacher is professional competence. It cover how the teacher understand the material of subject matter itself. Teaching Mathematics in Junior High School, teacher have to understand completely in Numbers, Algebra, Geometry and measurements, and also Statistics and probability. Based on the exam and discussion in workshop, known that math teacher in Pesisir Selatan having problems in teaching Geometry and measurement and also Statistics and probability. The problems are complexity of teaching materials, error in translating competence standard and basic competence into lesson plan, time management and student’s motivation in studying math. Solution that offered to the teacher are translating basic competence into learning process and trick how to teach Geometry and Statistics especially. Teaching geometry by explain all geometry object, i.e. plane and space simultaneously and compare each object directly. Teaching statistics and probability starting by counting process.
Application Of Mathematical Literacy In Mathematics Learning For Elementary School Fadhilah Fitri; Dina Fitria; Fridgo Tasman; Defri Ahmad; Suherman Suherman
Pelita Eksakta Vol 2 No 2 (2019): Pelita Eksakta Vol. 2 No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol2-iss2/75

Abstract

Mathematical literacy requires individuals to solve a problem and also apply mathematics in everyday problems, which results in the ability to interpret solutions to those problems. In PISA it is known that Indonesia's mathematics literacy score is among the lowest, as well as in Guguk District Lima Puluh Kota Regency. One way to overcome this is to start introducing literacy to students early on. The introduction of literacy must be instilled in students since they are still in elementary school. Based on this, a training program and workshop was held regarding the application of mathematical literacy in mathematics learning in elementary schools in Guguak District with elementary school mathematics teacher partners who are members of the KKG SD Gugus III Kecamatan Guguak Kabupaten Lima Puluh Kota.
Pengendalian Mutu Alat Skir Katup Otomatis Menggunakan Peta Kendali dengan Metode Bootstrap Yuvani Oksarianti; Dina Fitria
Journal of Mathematics UNP Vol 7, No 3 (2022): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (326.676 KB) | DOI: 10.24036/unpjomath.v7i3.12598

Abstract

Compression leaks occur in valves caused by soot resulting from the combustion process of the combustion engine. To overcome valve leaks, it’s necessary to clean using an innovative tool called valve grinding automatic system. The purpose of this study was to determine the quality of this tool based on the control chart using the bootstrap method. Based on the research results, the control charts X ̅ dan R show that the cleaning process is in a controlled and stable state (in statistical control). The quality of the valve cleaning process using this tool is able of producing cleaning according to standard cleaning specifications.
Survey Training for Collecting Data of Nagari Tanjung Balik Dina Fitria; Nonong Amalita; Syafriandi Syafriandi; Zilrahmi Zilrahmi; Admi Salma; Dodi Vionanda; Yenni Kurniawati
Pelita Eksakta Vol 6 No 1 (2023): Pelita Eksakta Vol. 6 No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol6-iss1/202

Abstract

Collecting data is the initial stage of data processing. Such that, it is needed to make sure the data collected is representative. Surveyor is one of its principal components. But, Nagari as a small component of a residence lack of professional surveyor for the work of the survey. The Statistics Department as a producer of statistician gives training to local residents to collect their own data using the right method in Nagari Tanjung Balik
Classification of Harvest - Non Harvest in Rice Plant Image Using Convolutional Neural Network Algorithm Revina Rahmadani; Yenni Kurniawati; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/181

Abstract

The Area Sample Framework (ASF) survey is an area based survey carried out by direct observation of sample parts whose locations have been determined. Every month ASF officers take photos of observation results using an Android based cellphone, where the results of the photos will be classified manually by supervision officers and sent to a central server for processing. The large amount of rice plant image data included can hinder officers in classifying rice growth phases. Therefore, to speed up the classification process, the Convolution Neural Network (CNN) method is used. In this research, the CNN model built consists of 3 convolution layers, 3 pooling, ReLU and Sigmoid activation functions, with several other parameters such as batch size and epoch value. The training results show that the accuracy value for the training data is 92.86% with an epoch value of 120. Meanwhile, the accuracy value for the validation data is 69.01%. Model evaluation shows a precision value of 21.34% and a recall value of 32.20%. This shows that the CNN model has poor performance in predicting harvest and non-harvest in rice plant images.
Analysis of the Population of Sumatera Island Using Profile Analysis Sri Rahayu; Dony Permana; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/185

Abstract

The distribution of the population in each province according to age groups in Sumatra Island has tended to change over time. Therefore, an analysis is needed to provide a comparative overview of the characteristics between the populations of each province with different age groups. This analysis can help to understand the variations in these characteristics in relation to the population. Profile analysis is a technique within multivariate analysis of variance that can be used to examine the differences between two or more populations, where each population is influenced by several treatments (variables) tested. This method has been applied in various fields, including government, to understand the characteristics of specific regions. This study aims to identify the characteristics of the population in each province on the island of Sumatra based on sixteen age groups. Sumatra is one of the largest islands in Indonesia, comprising ten provinces. In this research, profile analysis is utilized to compare the population profiles of each province in Sumatra based on the sixteen age groups. Based on the profile parallelism test, it was found that the profiles of the ten provinces are not parallel, indicating differences in the average population numbers or trend patterns among the provincial profiles in Sumatra based on age groups. Further testing using Tukey's HSD method was conducted to compare each pair of provinces based on specific age groups. The testing revealed that there are significant differences in several provinces in Sumatra for each age group.
Evaluasi Faktor-Faktor Yang Memengaruhi Indeks Pembangunan Manusia Tahun 2023 Menggunakan Metode SEM-PLS Sindy Amelia Putri; Zilrahmi; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (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-iss3/214

Abstract

The human development index (HDI) is a measure of the success of development in a country. Indonesia as a developing country in 2022 has an HDI value that ranks 112 out of a total of 193 countries in the world. This indicates that there is an urgent need for evaluation in increasing the HDI value in Indonesia which leads to an increase in the quality of human development. The evaluation can be done using the Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis method. With 34 Indonesian provinces as observations, there are three dimensions as variables analyzed in this paper, namely economy, education, and health. These variables are analyzed based on each indicator variable. The results of the analysis show that in the economic variable, the influential indicators are the Open Unemployment Rate, GRDP per Capita at Constant Prices, and Average Wage per Hour Worker. Then in the education variable, the influential indicators are the School Participation Rate Age 7-12, the School Participation Rate Age 13-15, the Pure Enrollment Rate for Elementary/Middle School/Package A, the Pure Enrollment Rate for Junior High School/MTs/Package B, and the Pure Enrollment Rate for Senior High School/SMK/MA/Package C. Furthermore, in the health variable, there are indicators of the Percentage of Households by Province and Source of Adequate Drinking Water, and the Percentage of Ever-Married Women Aged 15-49 Years whose Last Childbirth Processed in a Health Facility which affect the value of HDI in Indonesia in 2023.
Optimization of Sentiment Analysis for MBKM Program using Naïve Bayes with Particle Swarm Optimization Diva Aliyah; Zilrahmi; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/220

Abstract

In early 2020, Kemendikbudristek launched the MBKM program with the aim of improving the quality of higher education through a student-focused learning approach. The launch of this program triggered various reactions on social media, especially on Twitter, both positive and negative. This study aims to analyze the sentiment of Twitter users towards the MBKM program using the Naive Bayes algorithm optimized with Particle Swarm Optimization (PSO). The data used are Indonesian tweets containing the keywords "MBKM" and "Merdeka Campus" from the period July to December 2022. The research stages include data collection through crawling, manual labeling of data into positive and negative sentiments, data preprocessing, application of the Naive Bayes algorithm, and feature selection with PSO. The results showed that the group of tweets categorized based on positive and negative sentiments towards the implementation of the MBKM program in Indonesia in 2022, showed that the NB-PSO experiment achieved an accuracy of 90.87%, an increase of 7.12% compared to the Naive Bayes algorithm alone. Thus, the use of Particle Swarm Optimization algorithm in Naive Bayes classification algorithm is proven to improve classification performance, especially in the case of sentiment analysis. Keywords: Sentiment Analysis, Merdeka Belajar Kampus Merdeka, Twitter, Naive Bayes, Particle Swarm Optimization.
Penerapan Metode Choice-Based Conjoint Analysis pada Preferensi Pekerjaan Mahasiswa Departemen Statistika Universitas Negeri Padang M. Farel Rusde Putra; Dodi Vionanda; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/221

Abstract

In the realm of psychology studies, it is widely assumed that the age range between 18 and 25 represents a critical period during which individuals preferences begin to take shape. This developmental phase encloses college students who despite their academic pursuits, remain relatively unfamiliar with the dynamic job market, particularly in the context of rapid technological advancements. Statistics as a discipline with broad applicability across both social and scientific domains, offers student of statistics significant career prospects. This research would likely estimate the job preferences of statistics students using one of the most common use methods called choice-based conjoint (CBC) analysis. The analysis reveals that work hours were the most substantial influence on statistics students’ job preferences, with a percentage of 40.29%. In addition, other factors that influence the preferences of statistics students are such as first salary (36.87%), correlation with the field of statistics (12.04%), work environment (7.18%), and type of workplace (3.62%).
PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) hanifah nazhiroh; Dina Fitria; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/223

Abstract

The movement of the share price of PT Telkom (Tbk) fluctuates so it is necessary to do a forecasting analysis. Forecasting the share price of PT Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN) method. In this study using PT.Telkom (Tbk) stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023). The purpose of this research is to determine the movement of PT.Telkom (Tbk) stock prices in 2024, to find out the difference in forecasting using PT.Telkom (Tbk) 2018-2023 stock price data with PT.Telkom (Tbk) stock price data after covid-19 2021-2023, and to determine the level of accuracy of forecasting PT.Telkom (Tbk) stock prices using the LSTM method. The results showed that both data have a small MAPE value. to forecast the share price of PT.Telkom for 1 year, PT.Telkom (Tbk) share price data for 2018-2023 is used which has more data to analyze long-term forecasting. From the analysis results obtained MAPE of 1.016% with the optimal parameter combination of neuron 4, batch size 64, and epoch 80. The results of forecasting the share price of PT.telkom (Tbk) in 2024 experienced very rapid fluctuations with an average share price of PT.Telkom (Tbk) in 2024 Rp 4,668 / sheet.
Co-Authors anice kartika Asra Dinul Haq AULIA YUSWITA Defri Ahmad Dicha Putri Yeni Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dwika Larissa Faddiah Gusti Handayani Fadhilah Fitri Fadhilah Fitri Fakhri Kamil Fathina Nafisa Putri Fitri Mudia Sari Fridgo Tasman Hanifah Nazhiroh khairunnisa Pane M. Farel Rusde Putra Muhammad Fadlan Rafly Nonong Amalita Revina Rahmadani Ronald Rinaldo Rudi Anggara Salma, Admi Silvia Triana Sindy Amelia Putri SRI RAHAYU Suherman Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Vinna Sulvia Wahda Aulia Assara yazid permana Yenni Kurniawati Yuvani Oksarianti Zamahsary Martha Zilrahmi, Zilrahmi