scholarly journals Prediksi Kebutuhan Tempat Tidur Menggunakan Metode Data Mining

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dwi Indah Puspitasari ◽  
Edi Jaya Kusuma ◽  
Kriswiharsi Kun Saptorini ◽  
Evina Widianawati
Keyword(s):  
Data Mining ◽  
Random Forest

Dalam studi kasus yang dilakukan di bangsal Umar di Rumah Sakit Islam (RSI) Kendal, Jawa Tengah, diketahui bahwa pada tahun 2020 bangsal tersebut memiliki 22 tempat tidur. Berdasarkan data statistik yang diperoleh, nilai indikator Barber-Johnson yang ada pada bangsal Umar masing-masing adalah BOR 65.2%; LOS 2.85; TOI 1.52; dan BTO 83.68. berdasarkan indikator tersebut efisiensi penggunaan tempat tidur di bangsal Umar di RSI Kendal masih belum tercapai. Maka dari itu, agar mutu pelayanan kesehatan khususnya dalam hal efisiensi penggunaan tempat tidur pada bangsal Umar di RSI Kendal tercapai, penelitian ini mengimplementasikan metode data mining random forest untuk melakukan prediksi jumlah tempat tidur sesuai dengan standar Barber Johnson untuk tahun 2021 hingga 2023. Dari hasil pengolahan data didapatkan jumlah tempat tidur untuk tahun 2021 hingga 2023 pada bangsal Umar diprediksi memiliki 20 hingga 22 tempat tidur. Evaluasi terhadap tingkat efisiensi penggunaan tempat tidur di bangsal Umar dilakukan dengan memanfaatkan grafik Barber-Johnson. Dari grafik yang dihasilkan, dapat disimpulkan bahwa hasil prediksi jumlah tempat tidur mampu mencapai tingkat efisien. Hal ini disebabkan karena pada grafik Barber-Johnson bangsal Umar untuk tahun 2021 hingga 2023 titik pertemuan indikator tepat berada di daerah efisien.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique

2016 ◽  
Vol 31 (2) ◽  
pp. 581-599 ◽  
Author(s):  
David Ahijevych ◽  
James O. Pinto ◽  
John K. Williams ◽  
Matthias Steiner
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Weather Prediction ◽  
False Alarms ◽  
Convective System ◽  
Training Set ◽  
Data Mining Technique ◽  
High Detection Rate ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

2021 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Maulida Ayu Fitriani ◽  
Dany Candra Febrianto
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Direct Marketing ◽  
Class Imbalance ◽  
Processing Technique ◽  
Customer Segmentation ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mining Methods ◽  
Bank Marketing

Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

scholarly journals Garment Categorization Using Data Mining Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 984
Author(s):  
Sheenam Jain ◽  
Vijay Kumar
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Random Forest ◽  
Good Deal ◽  
Classification Model ◽  
Upper Body ◽  
Whole Body ◽  
Product Data ◽  
Soft Classification ◽  
Using Data

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

scholarly journals Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques

2020 ◽  
Vol 16 (11) ◽  
pp. 57
Author(s):  
Benjawan Hnusuwan ◽  
Siriwan Kajornkasirat ◽  
Supattra Puttinaovarat
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Random Forest ◽  
Dengue Fever ◽  
Patient Information ◽  
Public Health Problem ◽  
Random Tree ◽  
Correct Result ◽  
Major Public Health Problem ◽  
Data Mining Techniques

Dengue fever is a major public health problem and has been an epidemic in Thailand for a long time. Therefore, there is a need to find a way to prevent the disease. This research aimed to explore the important factors of dengue fever, to study the factors affecting dengue hemorrhagic fever in Surat Thani Province, and to map the potential outbreak of dengue fever. Collecting patient information was done including, Rainfall, Digital Elevation Model (DEM), Land Use and Land Cover (LULC), Population Density, and Patients in Surat Thani Province, which was analyzed using data mining techniques involving analysis using 3 algorithms comprising Random Forest, J48, and Random Tree. The correct result is Random Forest since the accuracy of the data is 96.7 percent followed by J48 with accuracy of 95.9 percent. The final sequence is Random Tree with accuracy of 93.5 percent. Then, using the information can be displayed through ArcGIS program to see the risk points that are compared to the risk areas that have been previously done. The results can be very risky in Mueang District, Kanchanadit District, and Don Sak District, corresponding to the information obtained from the Public Health Office and the risk map created from the patient information.

scholarly journals Random Response Forest for Privacy-Preserving Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Gábor Szűcs
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Trees ◽  
Original Data ◽  
Privacy Preserving ◽  
Random Response ◽  
Privacy Preserving Data Mining ◽  
Binary Variables ◽  
Binary Decision ◽  
Optimal Coding

The paper deals with classification in privacy-preserving data mining. An algorithm, the Random Response Forest, is introduced constructing many binary decision trees, as an extension of Random Forest for privacy-preserving problems. Random Response Forest uses the Random Response idea among the anonymization methods, which instead of generalization keeps the original data, but mixes them. An anonymity metric is defined for undistinguishability of two mixed sets of data. This metric, the binary anonymity, is investigated and taken into consideration for optimal coding of the binary variables. The accuracy of Random Response Forest is presented at the end of the paper.

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