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

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Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia Okia Dinda Kelana; Atus Amadi Putra; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/36

Abstract

Chen and Singh's Fuzzy Time Series Model is a forecasting method that uses the basi fuzzy logic in the process. The differences in the models are in the fuzzy logic relations. Chen's model uses Fuzzy Logical Relationship Groups. Meanwhile, the Singh model uses only Fuzzy Logical Relationships in the forecasting process. To find out the best model between the two models, forecasting the Farmer's Exchange Rate is carried out. Farmers' exchange rates are the option for observers of agricultural development in assessing the level of welfare of farmers in Indonesia. With changes in farmer exchange rates every month, it is necessary to forecast data in order to obtain an overview for the following month. Research used is applied research where the initial step is to study and analyze the theories related to our research, then colect the necessary data. The data used is data secondary data obtained online from the official website of the Badan Pusat Statistika (BPS). the forecasting results of the two models were compared using MAPE. The results of the comparison of the accuracy of the prediction accuracy of Chen and Singh's fuzzy time series models on farmers' exchange rates obtained Chen's MAPE fuzzy time series values ​​of 0.679% and Singh's fuzzy time series models of 0.354%. This means that the best forecasting model for farmer exchange rates in Indonesia is the Singh model.
Pemodelan Waktu Survival Pasien Tuberkulosis menggunakan Regresi Cox Proportional Hazard dengan Data Tersensor Elsa Oktaviani; Nonong Amalita; Atus Amadi Putra; Dony Permana
UNP Journal of Statistics and Data Science Vol. 1 No. 4 (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-iss4/65

Abstract

Tuberculosis is an infectious disease that needs to be watched out for in West Sumatra Province. West Sumatra Province is the province with the 12th highest TB case in Indonesia in 2021 with a total of 8,216 TB cases and a TB treatment cure rate that is still far from the target of the Indonesian Ministry of Health. The purpose of this study is to determine the Cox proportional hazard regression model and factors that affect the survival time of tuberculosis patients at Dr. M. Djamil Padang Hospital. The survival period used is the time when the patient is taking TB treatment at RSUP Dr.  M. Djamil Padang in 2021 until the patient is declared dead. The method used in the Cox Proportional Hazard Regression analysis is the Maximum Partial Likelihood Estimation Method. By using the cox proportional hazard regression model, the factors that influence the survival time of tuberculosis patients at RSUP Dr.  M. Djamil's BMI , leukocytes , fever , shortness of breath , and decreased appetite . 
Classification of Coronary Heart Disease at Semen Padang Hospital using Algorithm Classification And Regression Trees (CART) defal aditya defran; Atus Amadi Putra; Dodi Vionanda; Tessy Octavia Mukhti
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-iss5/104

Abstract

Cardiovascular disease is a degenerative disease caused by decreased function of the heart and blood vessels. One of the heart diseases that is very popular today is coronary heart disease (CHD). The main factors that cause CHD include age, gender, hypertension, blood sugar and cholesterol. One method that can be used to group CHD is classification. Classification And Regression Trees (CART )is a decision tree that describes the relationship between a response variable and one or more predictor variables. The goal of CART is to obtain an accurate data group as a characteristic of a classification. Based on the results of the optimal tree, the attribute that is the main characteristic in classifying CHD patients at Semen Padang Hospital is age. The determination of the classification results using the confusion matrix produced an accuracy value of 66.67%, a sensitivity of 56.52% for classifying CHD patients, and a specificity of 84.61% for classifying non-CHD patients.
Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province Widia Handa Riska; Dony Permana; Atus Amadi Putra; Zilrahmi
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-iss1/115

Abstract

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.
Comparison of Error Prediction Methods in Claassification Modeling with CHAID Methods for Balanced Data Findri Wara Putri; Dodi Vionanda; Atus Amadi Putra; 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-iss5/116

Abstract

Chi-Squared Automatic Interaction Detection (CHAID) is an exploratory method for classifying data by building classification trees. The classification result are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The goal is to see the performance of the model. The accuracy of this model can be determined by calculating the level of prediction error in the model. The error rate prediction method works by dividing data into training data and testing data. There are three methods in the error rate prediction method, such as Leave one out cross validation (LOOCV), Hold out, and k-fold cross validation. These methods have different performance in dividing data into training data and test data, so that each method has advantages and disadvantages. Therefore, a comparison of the three error rate prediction methods was carried out with the aim of determining the appropriate method for the CHAID. This research is included in experimental research and uses simulation data from data generation results in RStudio. This comparison is carried out by considering several factors, namely the marginal probability matrix and different correlations. The comparison results will be observed using a boxplot by looking at the median error rate and lowest variance. This research found that k-fold cross validation is the most suitable error rate prediction method applied to the CHAID method for balanced data.
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.
Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5 Futiah Salsabila; Zamahsary Martha; Atus Amadi Putra; Admi Salma
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-iss1/135

Abstract

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.  
Prediction of Palm Oil Production Results PT.KSI South Solok Using Ensemble k-Nearest Neighbor Nilda Yanti; Atus Amadi Putra; Dony Permana; Zilrahmi
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-iss1/136

Abstract

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.
Artificial Neural Network Model for Estimating the Poor Population in Indonesia as an Effort to Alleviate Poverty Febiola Putri, Febi; Atus Amadi Putra; Yenni Kurniawati; Zamahsary Martha
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-iss2/154

Abstract

Forecasting the poverty rate in Indonesia is one of the activities that is considered to be able to help various parties, such as being able to help the government in planning more effective and efficient poverty alleviation programs. In this study, forecasting the poverty rate in Indonesia was carried out using the backpropagation artificial neural network method. The purpose of this research is to model and predict the poverty rate using the backpropagation artificial neural network model, and to determine the accuracy of the forecasting results produced by this method. This research is an applied researc. The data used is annual data on proverty in Indonesia from 2917-2021. The data is then divided into two parts, namely training data and test data. The results show that the best artificial network model is BP (7,7,2) with 7 neurons in the input layer, 7 neurons in the hidden layer, and 2 neurons in the output layer. The accuracy of this model is good with a MAPE value of 0.07633%. The forecasting results in the next period show that the highest number of poor people is East Java province with a value of 3604.1698 thousand people in the first semester (March) of 2022 and has increased in the second semester period (September) of 2022 with a value of 3698.822 thousand people
Classification of Poor Households in West Sumatra Province using Decision Tree Algorithm C4.5 Dinda Fitriza; Atus Amadi Putra; Dodi Vionanda; Zilrahmi
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-iss2/157

Abstract

The significant and increasingly complex issue of poverty poses a considerable challenge to Indonesia's development, including West Sumatra Province, with a poverty rate was 5.92% in 2022. The government has initiated programs to address poverty by focusing on the criteria of impoverished households. Data on impoverished households can be obtained through the National Socio-Economic Survey (Susenas). One method that can classify impoverished households is the decision tree. Decision tree is a flowchart that resembles a tree. The C4.5 algorithm used in this research has the ability handle discrete and continuous data, manage variables with missing values, and prune decision tree branches. The result of the analysis shows that the variables affecting the classification of poor households are the number of household members, then the age of the household head, type of house floor, type of house wall, source of drinking water, and cooking fuel. The accuracy of the test data using a confusion matrix is 69.89%, sensitivity of 71.15% for classifying regular households, and specificity of 68.72% for classifying impoverished households.
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