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Journal : Journal of Advanced Computer Knowledge and Algorithms

The Application of the K-Nearest Neighbor (KNN) Method to Determine House Locations in the Batuphat and Tambon Tunong Areas, Aceh Khairi, Abil; Fahrezi, Irgi; Sahputra, Irfan; Anshari, Said Fadlan
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 1 (2024): Journal of Advanced Computer Knowledge and Algorithms - January 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i1.14531

Abstract

This study aims to apply the K-Nearest Neighbors (KNN) method to find the location of a house situated precisely on the border between Batuphat and Tambon Tunong. The issue faced by the college friends is the difficulty in determining whether the house falls within the Batuphat or Tambon Tunong area. The KNN method is used due to its ability to classify based on the nearest neighbors' distance.The data used in this research includes information on the house's location and the Batuphat and Tambon Tunong areas. The training process is conducted to form the KNN model based on the known location data, while the testing process is employed to classify the unknown house location into either the Batuphat or Tambon Tunong area.The results of the study demonstrate that the KNN method can be utilized to determine the location of a house situated on the border between Batuphat and Tambon Tunong. By considering the nearest neighbors' distance, the house can be classified into one of the areas with a high level of accuracy.This research contributes to providing a solution for college friends who face difficulties in determining the house location on the Batuphat and Tambon Tunong border. The KNN method can serve as an effective tool in addressing this problem. Moreover, this study can serve as a basis for further development in the field of location classification based on the KNN method.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.