scholarly journals PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

2020 ◽  
Vol 6 (1) ◽  
pp. 7-14
Author(s):  
Achmad Udin Zailani ◽  
Nugraha Listiana Hanun
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Study Results ◽  
Credit Worthiness ◽  
The Impact ◽  
Credit Analysis

In English : Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed. In Indonesian : Kredit adalah penyediaan uang atau tagihan yang dapat dipersamakan atas persetujuan atau kesepakatan pinjam meminjam antara bank dengan pihak lain yang mewajibkan pihak peminjam melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga. Koperasi Mitra Sejahtera menghadapi masalah pembayaran pihak peminjam atas tunggakan kredit. Penelitian ini bertujuan untuk memprediksi kelayakan kredit dengan penerapan Algoritma Klasifikasi Random Forest agar dapat memberikan solusi untuk penentuan kelayakan pemberian kredit. Metode penelitian ini adalah riset eksperimen absolut yang mengarah kepada dampak yang dihasilkan dari eksperimen atas penerapan model pohon keputusan menggunakan pendekatan Algoritma Klasifikasi Random Forest. Hasil pengujian dengan algoritma klasifikasi Random Forest mampu menganalisis kredit yang bermasalah dan yang debitur yang tidak bermasalah dengan nilai akurasi sebesar 87,88%. Di samping itu, model pohon keputusan ternyata mampu meningkatkan akurasi dalam menganalisis kelayakan kredit yang diajukan calon debitur.

scholarly journals PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

2020 ◽  
Vol 6 (1) ◽  
pp. 7-14
Author(s):  
Achmad Udin Zailani ◽  
Nugraha Listiana Hanun
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Study Results ◽  
Credit Worthiness ◽  
The Impact ◽  
Credit Analysis

Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed.

scholarly journals Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

2021 ◽  
Vol 18 (10) ◽  
pp. 5126
Author(s):  
Pouya Gholizadeh ◽  
Ikechukwu S. Onuchukwu ◽  
Behzad Esmaeili
Keyword(s):  
Decision Tree ◽  
Occupational Safety ◽  
Decision Rules ◽  
Decision Tree Model ◽  
Body Parts ◽  
Contributing Factors ◽  
Tree Model ◽  
Statistical Probability ◽  
Electrical Burns ◽  
The Impact

This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injury, injured part of body, nature of injury, and event type. In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials; followed by whether the source of injury is tools, instruments, and equipment. In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

scholarly journals ПОБУДОВА Й АНАЛІЗ МОДЕЛІ ПРИЙНЯТТЯ РІШЕНЬ ПРИ ОЦІНЦІ НАСЛІДКІВ ВАКЦИНАЦІЇ ПРОТИРОТАВІРУСНОЮ ВАКЦИНОЮ В УКРАЇНІ

2012 ◽  
Author(s):  
S. O. Solovyov ◽  
I. V. Dziublyk
Keyword(s):  
Decision Tree ◽  
Markov Processes ◽  
Hospital Admissions ◽  
Decision Tree Model ◽  
Theoretical Studies ◽  
Rotavirus Vaccine ◽  
Computer Implementation ◽  
Tree Model ◽  
The Impact ◽  
Efficiency Indicators

The results of computational and theoretical studies related to assessing of efficiency indicators of immunization with rotavirus vaccine in Ukraine among children under five years are presented. The Indicators of the impact were received with computer implementation of decision tree model based on Markov processes. Under strategies of vaccination and no vaccination projected levels of morbidity, number of hospital admissions, mortality of rotavirus infection and other factors were received. It was shown that the vaccination with rotavirus vaccine will have significant medical significance in Ukraine.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

scholarly journals Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

2018 ◽  
Vol 10 (4) ◽  
pp. 580 ◽  
Author(s):  
Tedros Berhane ◽  
Charles Lane ◽  
Qiusheng Wu ◽  
Bradley Autrey ◽  
Oleg Anenkhonov ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Decision Tree ◽  
Wetland Mapping ◽  
Multispectral Imagery ◽  
Rule Based ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Roghieh Ramezankhani ◽  
Nooshin Sajjadi ◽  
Roya Nezakati Esmaeilzadeh ◽  
Seyed Ali Jozi ◽  
Mohammad Reza Shirzadi
Keyword(s):  
Environmental Factors ◽  
Decision Tree ◽  
Cutaneous Leishmaniasis ◽  
Decision Tree Model ◽  
Vegetation Coverage ◽  
Tree Model ◽  
Seasonal Incidence ◽  
Isfahan Province ◽  
The Mean ◽  
The Impact

Cutaneous Leishmaniasis (CL) is a neglected tropical disease that continues to be a health problem in Iran. Nearly 350 million people are thought to be at risk. We investigated the impact of the environmental factors on CL incidence during the period 2007- 2015 in a known endemic area for this disease in Isfahan Province, Iran. After collecting data with regard to the climatic, topographic, vegetation coverage and CL cases in the study area, a decision tree model was built using the classification and regression tree algorithm. CL data for the years 2007 until 2012 were used for model construction and the data for the years 2013 until 2015 were used for testing the model. The Root Mean Square error and the correlation factor were used to evaluate the predictive performance of the decision tree model. We found that wind speeds less than 14 m/s, altitudes between 1234 and 1810 m above the mean sea level, vegetation coverage according to the normalized difference vegetation index (NDVI) less than 0.12, rainfall less than 1.6 mm and air temperatures higher than 30°C would correspond to a seasonal incidence of 163.28 per 100,000 persons, while if wind speed is less than 14 m/s, altitude less than 1,810 m and NDVI higher than 0.12, then the mean seasonal incidence of the disease would be 2.27 per 100,000 persons. Environmental factors were found to be important predictive variables for CL incidence and should be considered in surveillance and prevention programmes for CL control.

scholarly journals The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Na’eem Hoosen Agjee ◽  
Onisimo Mutanga ◽  
Kabir Peerbhay ◽  
Riyad Ismail
Keyword(s):  
Random Forest ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Classification ◽  
Node Splitting ◽  
Forest Classification ◽  
Vector Machines ◽  
Classifier Performance ◽  
The Impact

Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy and low infested water hyacinth plants. Results from this study showed that RF was slightly influenced by simulated noise with classification accuracies decreasing for week one and week two with the addition of 30% noise. In comparison to RF, oRF-RR and oRF-SVM yielded higher test accuracies (oRF-RR: 5.36%–7.15%; oRF-SVM: 3.58%–5.36%) and test kappa coefficients (oRF-RR: 10.72%–14.29%; oRF-SVM: 7.15%–10.72%). Notably, oRF-RR test accuracies and kappa coefficients remained consistent irrespective of simulated noise level for week one and week two while similar results were achieved for week three using oRF-SVM. Overall, this study has demonstrated that oRF-RR can be regarded a robust classification algorithm that is not influenced by noisy spectral conditions.

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