Pillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java

Nishom, M.; Handayani, Sharfina Febbi; Dairoh, Dairoh

doi:10.30595/juita.v9i2.9860

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The ratio 80% for training and 20% for testing are a commonly used ratio in many fields of machine learning [36]. Ecludian distance was used to calculate the closest distance between the training and test data using [37].…”

Section: Classification Using K-nnmentioning

confidence: 99%

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Yaddarabullah,

Rahman,

Saad

2024

IJEECS

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This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.

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Section: Classification Using K-nnmentioning

confidence: 99%

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Yaddarabullah,

Rahman,

Saad

2024

IJEECS

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“…Research conducted by [14] to analyze cyberbullying through Instagram and [15] is used for classifying store sales data. Then [16] identified the availability of health human resources in Central Java. In addition, [17] conducted research on the performance of PDAMs in providing water quality, namely based on healthy and unhealthy, unhealthy and sick, and healthy and sick features.…”

Section: Introductionmentioning

confidence: 99%

K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters

Novianta

Syafrudin

Warsito

2023

JUITA

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The Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and action can be carried out. This study conducted a grouping analysis of rivers in DIY based on water quality parameters such as Total Suspended Solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Phosphate, Fecal Coli, and Total Coliform. The grouping method uses the K-means algorithm. The data source is secondary data from the DIY Provincial Environment and Forestry Service. The data is in the form of 56 river samples observed in November 2020. The description of the data shows that the average of the 56 river water samples is 24.95 for TSS, 8.84 for DO, 4.33 for BOD5, 20.36 for COD, 0 .54 for Phosphate, 22.820 for Fecal Coli, and 59.210 for Total Coliform. The results of grouping with k=6 are the best compared to k = 2, 3, 4, 5, 7, and 8. The number of members in this grouping is n1 = 14, n2 = 1, n3 = 1, n4 = 5, n5 = 18, and n6 = 17. The cluster that has the highest average TSS, BOD, and COD values is the 3rd cluster (Rivers in Bantul and Sleman Regencies). The cluster that has the highest DO value is the 6th cluster (Rivers in Bantul Regency). The cluster that has the highest average Phosphate value is the 2nd cluster (Rivers in Bantul, Sleman, and Gunungkidul Regencies). The cluster that has the highest average Fecal Coli and Total Coliform values are the 4th cluster (Rivers in Bantul Regency, Yogyakarta City, and Sleman Regency).

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Classification of Occupant Position Based Ventilation in the Under-Actuated Zone Using K-Nearest Neighbor's

Yaddarabullah,

Megawati,

Rahman

et al. 2024

2024 International Conference on Information Technology and Computing (ICITCOM)

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Pillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java

Cited by 4 publications

References 7 publications

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters

Classification of Occupant Position Based Ventilation in the Under-Actuated Zone Using K-Nearest Neighbor's

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