Efficiency comparison of Python and RapidMiner

Kovács, László; Ghous, Hina

doi:10.35925/j.multi.2020.3.26

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The collected data was verified, and any missing data was obtained from the project websites. The retrieved data were then analyzed through RapidMiner, a software recommended by László and Ghous (2020) for its high accuracy in data analysis. In total, seven models were analyzed, including: (1) Decision Tree (DT), (2) Random Forest (RF), (3) Gradient Boosted Tree (GBT), ( 4) an Ensemble Classifier of DT and RF, (5) an Ensemble Classifier of DT and GBT, (6) an Ensemble Classifier of RF and GBT, and ( 7) an Ensemble Classifier of DT, RF, and GBT.…”

Section: Methodsmentioning

confidence: 99%

“…This is consistent with the findings of Ngoendee and Charoenruengkit (2021), who determined that the use of an ensemble model was the most effective and precise approach in data mining. The study by László and Ghous (2020) also concluded that the utilization of model integration techniques is an appropriate method for combining multiple models to obtain the most accurate and optimal predictions.…”

Section: Machine Learning Techniquementioning

confidence: 99%

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House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

Tochaiwat,

Pultawee

2024

NJEDP

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Specifying the house type of a housing development project is extremely necessary. However, the determination of a project type nowadays has become a delicate matter, requiring the expertise and knowledge of seasoned project developers. This study aimed to apply four machine learning techniques: Decision Tree, Random Forest, Gradient Boosted Tree and Ensemble Classifier, to analyze the data from 179 housing estate projects collected from market reports of real estate companies in Thailand, with a focus on selecting projects with average monthly sales rates that are higher than the average of all acquired projects. This process resulted in a reduced dataset of 59 projects, including 31 townhouses, 22 single-family houses, and six semi-detached houses. As a result, the Ensemble Classifier model has the highest accuracy of 90.91%. The factors most influential in identifying the type of project are the distances from a main road, sky train station, bus station, hospital, and department store. Single-detached house projects are suitable for locations with high potential. The ideal location should be in proximity to a main road, bus station, department store, and hospital. In addition, townhouse projects are ideal for medium-potential locations that are not near shopping malls, but still require proximity to a hospital, sky train station, or bus station. Ultimately, semi-detached house projects are ideal for medium-potential locations that require proximity to a main road for convenient access to sky train station or public transportation, depending on the specific context.

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Section: Methodsmentioning

confidence: 99%

Section: Machine Learning Techniquementioning

confidence: 99%

House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

Tochaiwat,

Pultawee

2024

NJEDP

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“…Berbeda dari penelitian yang dikaji sebelumnya, penelitian ini melakukan klastering [11] menggunakan data penyakit tertinggi di RSUD Kota Bandung dengan berbagai kelompok usia yang kemudian diolah menggunakan algoritma K-Means. Pengolahan data menggunakan tahapan standard CRISP-DM (Cross-Industry Standard Process for Data mining) yang dilakukan dengan menggunakan perangkat lunak RapidMiner 9.10 [12], Microsoft Excel dan Bahasa pemrograman python dengan library Scikit-Learn [13] dalam pengolahannya.…”

Section: Pendahuluanunclassified

Grouping the Prevalence of Disease Cases by Age in Bandung City Hospitals Using K-Means

Soliani

Juanita

2022

J. Tek. Inform. (JUTIF)

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In 2019, the World Health Organization (WHO) stated that the top 10 types of diseases accounted for 55% of the 55.4 million deaths in the world. Meanwhile, in Indonesia, the province of West Java has the largest population, with the capital city of Bandung. Based on the health profile of the Bandung City Hospital, there were the ten highest diseases based on 18,147 cases. However, the data has not been processed into helpful information for the health department, especially the city of Bandung, to help determine disease cases by age group. So that the contribution of this study is to classify the prevalence of disease cases by age in Bandung City Hospital; this study aims to help the Bandung City Health Office take preventive, treatment and counselling actions against diseases that have a prevalence of disease cases based on age. This study uses the CRISP-DM methodology, with the K-Means clustering method and the testing method using the elbow method and the Davies-Bouldin Index (DBI). Data processing using rapid miner software and python programming. This study concludes that the optimal cluster value is K=2. The value of cluster 0 consists of the type of disease with the lowest case, and cluster 1 consists of the kind of disease with the highest case. Cluster 1 is the elderly and adult age group, while the age group in cluster 0 is the infant age group, the toddler age group, and the child age group.

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“…This software was developed by Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer (Pynam et al, 2018). RapidMiner requires a shorter processing time than other machine learning software (Faid et al, 2019;László & Ghous, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…One of the machine learning algorithms that shows superior performance in various research is the Random Forest proposed by Breiman (László & Ghous, 2020). This algorithm is a development of a decision tree (Hadiprakoso et al, 2022) that combines bootstrap aggregating (bagging) and random feature selection methods (Mantri et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data

Rifqah Fahira,

Lawi,

Aqsha

2023

J. Nat. Scien. & Math. Res.

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Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection.

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Efficiency comparison of Python and RapidMiner

Cited by 8 publications

References 5 publications

House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

Grouping the Prevalence of Disease Cases by Age in Bandung City Hospitals Using K-Means

Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data

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