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All Journal Pelita Eksakta UNP Journal of Statistics and Data Science
Fadhilah Fitri
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Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis Viola Yuniza; Atus Amadi Putra; Nonong Amalita; 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-iss1/128

Abstract

  Dengue fever is a disease transmitted by the bite of the Aedes aegypti mosquito. Central Agency of Statistic of Lima Puluh Kota District reported that the morbidity rate of this disease was 14.40% per 100,000 population, which was higher than the previous year's morbidity rate of 3.30% per 100,000 population. The main symptoms of this disease are fever lasting 2-7 days, muscle and joint pain with or without rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms ranging from dengue fever, dengue hemorrhagic fever to dengue shock syndrome. Therefore, a classification method is needed to help and facilitate early diagnosis of this disease. The method used is the Naive Bayes algorithm by classifying the positive and negative patients with dengue fever. The purpose of this research is to determine the classification of patients with dengue fever disease and the accuracy of using the Naive Bayes algorithm. The results of the analysis stated that the Naïve Bayes model successfully classified patients into 12  Dengue fever positive patients and 22  Dengue fever negative patients based on 34 testing data. The accuracy of the model is 91,18%, which shows that the model is very good  in classifying Dengue fever patients.
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.
Structural Equation Modeling Partial Least Square (SEM-PLS) Untuk Membandingkan Kondisi Public Speaking Anxiety Mahasiswa Soshum dan Saintek Sabina Chairun Najwa; Natasya Dwi Ovalingga; Hanifah Nazhiroh; Rizki Akbar; Fadhilah Fitri
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-iss5/132

Abstract

Public speaking is a communication skill to deliver opinion or massage to the audience. Public speaking anxiety, caused by various factors. Social and science students have differences in culture and learning systems. Therefore, students in both educational clusters have their own ways of overcoming communication barriers. This study aimed to identify factors that influence public speaking anxiety in social and science students at Padang State University. The method used is the Structural Equation Model Partial Least Square (SEM-PLS) to understand the influential factors in more detail and minimize analysis errors caused by missing values and multicollinearity due to diverse samples. The results of the analysis are path diagrams for structural models and outer loading tables. If the < value is 0.7, then recalculation is carried out so that a new model is formed. The feasibility of the social science family model was obtained 35% and the scientific science family was 36.5%. The effect of latent or exogenous variables in this study is weak. Social students have higher levels of speech anxiety than science students. This is influenced by humiliation, unfamiliar role, and negative result factors. In science students, the influencing factors are humiliation, preparation, and unfamiliar Role.
Comparison of Modeling Infant Mortality Rate in West Sumatra and West Java Province in 2021 Using Negative Binomial Regression Afdhal, Afdhal Rezeki; Fadhilah Fitri; Dodi Vionanda; Dony Permana
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/156

Abstract

In Poisson regression analysis, there is an assumption that must be met, namely equidispersion (the variance value of the response variable is the same as the mean). In reality, conditions like this very rarely occur because overdispersion usually occurs (the variance value of the response variable is greater than the mean). One way to overcome this problem is to use the Negative Binomial regression method. The aim of this article is to obtain the best modeling results in Negative Binomial regression analysis to overcome overdispersion in cases of infant mortality in West Sumatra Province and West Java Province. The model obtained using Negative Binomial regression produces an AIC value in West Sumatra province of 192.65 which is smaller than the AIC value in West Java Province it was 283.47. Based on the Negative Binomial regression model equation obtained in West Sumatra Province, it can be explained that the number of health centers (X3) has a significant influence on the infant mortality rate and in West Java Province it can be explained that the number of medical personnel (X1) has a significant influence on the infant mortality rate.
K-Modes Analysis with Validation of the DBI in Grouping Provinces in Indonesia based on Indicators of Poor Households Syifa Azahra; Zilrahmi; Dodi Vionanda; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/165

Abstract

Poverty is the most pressing social problem in Indonesia. Efforts to alleviate poverty are to group provinces in Indonesia based on indicators of poor households using the K-modes algorithm. The data used is data from the 2017 Indonesian Demographic and Health Survey (IDHS) on the Household List. The analysis includes data noise detection, data clustering using K-Modes algorithm, and cluster validation with Davies Bouildin Index (DBI). Based on the clustering that has been done, two clusters are obtained, where cluster 1 consists of 26 provinces and cluster 2 consists of 8 provinces. cluster 1 is a cluster that fulfills 9 indicators of poor households and cluster 2 only a few indicators of poor households. So that the government can prioritize these 8 provinces to overcome poverty in Indonesia. For the DBI value obtained is 1.89 which means that 2 clusters are already well used in the algorithm.
Perbandingan Algoritma C4.5 dan C5.0 Dalam Klasifikasi Status Gizi Balita Stunting harelvi, dhea afrila; Admi Salma; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/172

Abstract

Stunting is one of the health conditions that reflect aspects of nutrition and child growth, allowing us to observe the nutritional status of toddlers. The aim of this study is to determine the classification results of the C4.5 and C5.0 algorithms in cases of stunted toddler nutritional status and to compare the results between the C4.5 and C5.0 algorithms in classifying stunted toddler nutritional status using k-fold cross-validation. The data in this study are secondary data. Which is collected from Puskesmas IV Pesisir Selatan Regency. The research variables are divided into two, namely the response variable Y, which is Toddler Nutritional Status, and predictor variables X including Age, Toddler Gender, Toddler Weight, and Toddler Height. The result of the study obtain the algorithm C5.0 produse accuracy value of the C5.0 algorithm is higher than that of the C4.5 algorithm. The C5.0 algorithm provides an average accuracy result of 83% while the C4.5 algorithm provides an accuracy result of 79%. Thus, it can be concluded that the C5.0 algorithm is better at classifying stunted toddler nutritional status.
PEMODELAN INDEKS PEMBANGUNAN GENDER (IPG) PROVINSI JAWA BARAT DENGAN PENDEKATAN REGRESI NONPARAMETRIK DERET FOURIER RIZKIA, DHEA PUTRI; Fadhilah Fitri; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/174

Abstract

Gender equality is a development target in many countries. The ideal condition in human development that is expected is that male and female population groups have equal access to play a role in development, control over existing development resources, and receive benefits from development equally and fairly. The gender gap still occurs today in all aspects. The condition of the gender gap can be known by looking at the Gender Development Index . In observing the data curve, between the Gender Development Index and each independent variable does not form a certain pattern. In addition, the data patterns that are formed tend to repeat. Nonparametric regression analysis is the solution. Fourier series is a nonparametric analysis used for repetitive data. Modeling was performed using 1, 2, and 3 oscillation parameters. Of the three parameters, the best model resulted from the K=3 oscillation parameters with a GCV value of 2.8084 and a coefficient of determination of 42.39%.
Data Analysis and Visualization Training on Microsoft Excel Using Artificial Intelligence At SMA N 1 Ampek Angkek Kabupaten Agam Tessy Octavia Mukhti; Fadhilah Fitri; Devni Prima Sari
Pelita Eksakta Vol 6 No 2 (2023): Pelita Eksakta, Vol. 6, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol6-iss2/212

Abstract

Based on observations and discussions with several teachers at SMA N 1 Ampek Angkek Kabupaten Agam, problems were found in describing and analyzing the significance of the development of students' abilities. The next problem which is no less important is the difficulty of measuring the effectiveness of the teaching materials used in the classroom. To improve the quality of learning, teachers are required to optimize the learning process by making students actively involved and making learning more interesting for them. To overcome this problem, data analysis and visualization training was carried out in Microsoft Excel using artificial intelligence at SMA N 1 Ampek Angkek Kabupaten Agam.
Markov Chain Model Application for Rainfall Pattern in Padang City haniyathul husna; Dony Permana; Nonong Amalita; Fadhilah Fitri
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-iss3/179

Abstract

Rainfall is a natural phenomenon that includes climate variables and is observed every time in every place. Daily rainfall data is a time series data, which is random. It is a data transfer from one time to another which can be expressed as a state of light, medium, heavy or very heavy rainfall intensity. Rainfall prediction is needed for people's lives and supports the economy. In addition, rainfall prediction is an anticipation of prevention if high rain intensity will occur in a long time. One of the rainfall prediction methods that can be used is the stochastic process approach. Markov chain is part of the stochastic process that can be used for prediction of rainfall at the present time based on one previous time. The focus of this research is the application of Markov Chains for rainfall prediction. Through Markov chains, long-term opportunities for rainfall phenomena are obtained. This study will look at the rainfall pattern of Padang City using Markov chains and also to predict rainfall in Padang City. The results of predicting the weather conditions of Padang City with any rainfall conditions today are 36.9% for the chance of no rain tomorrow, 46% for the chance of light rain tomorrow, 10% for the chance of moderate rain tomorrow, 5.3% for the chance of heavy rain tomorrow, and 1.8% for the chance of very heavy rain tomorrow.The results of this study are expected to be a recommendation for parties directly involved in taking preventive measures due to rainfall.
K-Medoids Cluster Analysis for Grouping Provinces in Indonesia Based on Agricultural Households ST2023 01, Riska; Zamahsary Martha; Dony Permana; Fadhilah Fitri
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-iss3/193

Abstract

Agriculture plays a crucial role in Indonesia's national development, providing essential resources such as raw materials, household income, and contributing significantly to Gross Domestik Product (GDP). According to the 2023 Agricultural Census (ST2023), there has been an increase in the number of Agricultural Household Enterprises (RTUP) across various agricultural subsectors. However, the welfare of agricultural entrepreneurs remains low, with 48.68% of poor household heads working in this sector. Therefore, an analysis is needed to understand the patterns and characteristics of RTUPs in each province. This study aims to cluster the provinces in Indonesia based on the number of Agricultural Household Enterprises (RTUP) using K-Medoids cluster analysis. K-Medoids, an extension of K-Means, was chosen for its ability to handle outliers by using medoids as cluster centers instead of means. The research utilized data from the 2023 Agricultural Census, covering 38 provinces and eight variables representing different agricultural subsectors. The optimal number of clusters was determined using the Elbow method, resulting in four distinct clusters. The findings revealed that Cluster 1 consists of 12 provinces with moderate RTUP numbers, Cluster 2 includes 23 provinces with low RTUP numbers, Cluster 3 comprises one province with high RTUP numbers, and Cluster 4 contains two provinces with very high RTUP numbers. The cluster validation using the Davies-Bouldin Index (DBI) yielded a value of 0.722, indicating that the clustering results are optimal.
Co-Authors 01, Riska Admi Salma Afdhal, Afdhal Rezeki AL Rezki Ivansyah Andini Yulianti Anggi Adrian Danis Aprilla Suhada Asyifa Dwi Ayshah Atus Amadi Putra Aurumnisva Faturrahmi Azma, Meil Sri Dian Chairina Wirdiastuti Devni Prima Sari Dewi Adiningtiyas, Rheizma Dhiyaa Fitri Yafe Diah Triwulandari Dina Fitria Dodi Vionanda Dony Permana Fanni Rahma Sari Fashihullisan Fayza Annisa Febrianti Febiola, Latifah Jayatri Fenni Kunia Mutiya Fenni Kurnia Mutiya Findri Wara Putri Fishuri, Nufhika Hanifah Nazhiroh haniyathul husna harelvi, dhea afrila Herlena Purnama Sari Huriati Khaira Indonesia Isra Miraltamirus Kurnia Andrea Diva Mawarni, Iga Mayrita, Mayrita Addila Putri Melin Wanike Ketrin Miftahul Barokah, Bunga Mila, Sarmilah Muhammad Alif Yustin Musthafa Imran Natasya Dwi Ovalingga Nazifatul Azizah Nonong Amalita Novi, Yoli Marda Pane, khairunnisa permana, yazid Puti Utari Maharani Rahmadani Iswat rama novialdi Rizki Akbar RIZKIA, DHEA PUTRI Rizqia Salsabila Rosa Salsabila Azarine Sabina Chairun Najwa Salsabilla Khairani Salwa Hifa Fadilah Septrina Kiki Arisandi Silvia Agustina Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Vinka Haura Nabilla Viola Yuniza Yenni Kurniawati Zamahsary Martha Zilrahmi, Zilrahmi