UNP Journal of Statistics and Data Science
UNP Journal of Statistics and Data Science is an open access journal (e-journal) launched in 2022 by Department of Statistics, Faculty of Science and Mathematics, Universitas Negeri Padang. UJSDS publishes scientific articles on various aspects related to Statistics, Data Science, and its application. Articles can be in the form of research results, case studies, or literature reviews. All papers were reviewed by peer reviewers consisting of experts and academicians across universities.
Articles
16 Documents
Search results for
, issue
"Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science"
:
16 Documents
clear
<![CDATA[Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province ]]>
<![CDATA[Widia Handa Riska]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/115
<![CDATA[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.]]>
<![CDATA[Biplot and Procrustes Analysis of Poverty Indicators By Province in Indonesia in 2015 dan 2019 ]]>
<![CDATA[Ade Eriyen Saputri]]>;
<![CDATA[Admi Salma]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Dony Permana]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/124
<![CDATA[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.]]>
<![CDATA[Fuzzy K-Nearest Neighbor to Predict Rainfall in Padang Pariaman District ]]>
<![CDATA[Rizki Amalia, Annisa]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Zamahsary Martha]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/126
<![CDATA[Information about rainfall levels at a time and in a region is very important because rainfall influences human activities. Rainfall is the amount of water that falls to the earth in a certain period of time, measured in millimeters. One piece of information related to rainfall is daily rainfall predictions. In this study, an attempt was made to classify daily rainfall at the Padang Pariaman climatology station into 5 categories, namely very light rain, light rain, moderate rain, heavy rain and very heavy rain. There are 4 weather parameters used, namely air temperature, humidity, wind speed and duration of sunlight. One of the methods used to predict rainfall is data mining, a computer learning to analyze data automatically thus obtaining a perfect new model. One of the best prediction algorithms in data mining is Fuzzy K-Nearest Neighbor (FK-NN). FK-NN uses the largest membership degree value of the test data in each class to predict the class. The number of sample classes for rainfall data in Padang Pariaman Regency has an imbalance class. To overcome the imbalance class, Synthetic Minority Over-sampling Technique (SMOTE) method is used to generate minority data as much as majority data. The results of this study by using FK-NN classification with 343 test data, parameters K = 12, and euclidean distance is quite good at the accuracy level of 76,38%..]]>
<![CDATA[Classification the Characteristics of Traffic Accident Victims in Pariaman Using the Chi-square Automatic Interaction Detection Algorithm ]]>
<![CDATA[Manja Danova Putri]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/127
<![CDATA[Traffic accidents are incidents that occur when motor vehicles collide on the road, resulting in damage to vehicles and road infrastructure, as well as the potential for material losses, injuries, physical damage, and even death for those involved. Data from the Indonesian National Police show that the number of traffic accident victims between 2010 and 2020 ranged from 147.798 to 197.560 people, with fatalities predominantly occurring among individuals aged 15-34. The high number of traffic accident victims has negative impacts on various aspects of life, ranging from material losses to physical damage to the victims. Classification is a technique used to group objects or data into pre-defined classes or categories based on their attributes or features. One method in the field of classification is Chi-Square Automatic Interaction Detection (CHAID). The results of the classification using this method indicate that the age of the victims and the type of accident are the most significant variables influencing the condition of traffic accident victims. The evaluation of the model using a confusion matrix yielded an accuracy rate of 92%. This indicates that the model performs well in overall data classification.]]>
<![CDATA[Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis ]]>
<![CDATA[Viola Yuniza]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Nonong Amalita]]>;
<![CDATA[Fadhilah Fitri]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/128
<![CDATA[ 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.]]>
<![CDATA[Diagnosis of the type of delivery of pregnant women at Semen Padang Hospital Using the C4.5 Method ]]>
<![CDATA[rama novialdi]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Fadhilah Fitri]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/130
<![CDATA[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.]]>
<![CDATA[Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5 ]]>
<![CDATA[Futiah Salsabila]]>;
<![CDATA[Zamahsary Martha]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/135
<![CDATA[Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly. ]]>
<![CDATA[Prediction of Palm Oil Production Results PT.KSI South Solok Using Ensemble k-Nearest Neighbor ]]>
<![CDATA[Nilda Yanti]]>;
<![CDATA[Atus Amadi Putra]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/136
<![CDATA[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.]]>
<![CDATA[Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG) ]]>
<![CDATA[Aditya, Muhammad Fadhil Aditya]]>;
<![CDATA[Zilrahmi]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Tessy Octavia Mukhti]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/137
<![CDATA[Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results. The purpose of this research is to give forecasting values with high accuracy to describe the movement of IHSG close price values using the ANN method based on the Backpropagation algorithm. The research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.]]>
<![CDATA[Comparison of the C5.0 Algorithm and the CART Algorithm in Stroke Classification ]]>
<![CDATA[Indah Lestari]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Syafriandi Syafriandi]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/ujsds/vol2-iss1/144
<![CDATA[The C5.0 and CART algorithms are similar in terms of velocity and handling of categorical and numeric type data. However, these two algorithms are differences in terms the CART algorithm is binary and classifies categorical, numerical and continuous response variables resulting in classification and regression decision trees. Meanwhile, the C5.0 algorithm is non-binary and classifies categorical response variables resulting in a classification tree. This research aims to classify the Kaggle’s Stroke Prediction Dataset to find out the variables that most influence the risk of stroke, as well as to compare the results of the classification accuracy of the both algorithms. The results of the study showed that CART algorithm has a higher value of accuracy and precision, but its recall value is lower than C5.0. The accuracy value of each algorithm is 77.9% and 77.5%, presision is 89.5% and 83.2%, recall is 67% and 71.4%. Overrall, it can be concluded that there is no difference in classification between the two algorithm. Beside that, in the CART there were 3 variables that most influence on stroke risk, they are age, BMI, and average blood glucose levels. Meanwhile, in C5.0 only 2 variable that most influence, there are age and average blood glucose levels.]]>
