Optimasi Pengurutan Data Bilangan dengan Menggabungkan Algoritma Selection Sort Hybrid dan Bucket Sort

Sorting algorithms are crucial in data processing, particularly for integer data. However, as the number of integers to be sorted increases, the sorting algorithm takes longer to complete, especially for algorithms with O(n2) complexity. This article discusses optimizing integer data sorting by combining the Selection Sort Hybrid and Bucket Sort algorithms. The study aims to test the performance of the Selection Sort Hybrid and Bucket Sort algorithms and compare them with other data sorting algorithms. The research method used is experimental quantitative research, using randomly generated data using Python. The data were tested using the Combined Selection Sort Hybrid with Bucket Sort algorithm, Selection Sort Hybrid, Quick Sort, and Merge Sort. Data analysis was done by calculating the execution time of each sorting algorithm. The results show that the Selection Sort Hybrid and Bucket Sort algorithms are faster than other sorting algorithms in testing with large and complex integer data. Therefore, combining Selection Sort Hybrid and Bucket Sort algorithms can improve the efficiency and speed of sorting complex integer data

Klasterisasi Jumlah Penduduk Provinsi Jawa Timur Tahun 2021-2023 Menggunakan Algoritma K-Means

Understanding the population data of a region is crucial for policy development and strategic planning. East Java Province, the second-largest province in Indonesia, has undergone significant population growth from 2021 to 2023. Uneven growth poses challenges in resource and infrastructure management. The K-Means algorithm clusters population data into several groups based on specific characteristics. The Elbow method is used to determine the optimal number of clusters, ensuring the accuracy of the analysis. This research aims to analyze and cluster the population distribution in each city in East Java Province, providing a more detailed and accurate depiction. The research findings reveal three significant clusters. Cluster 0 includes 21 towns, Cluster 1 comprises 4, and Cluster 2 encompasses 13. These findings have important implications for targeted development policy formulation at the city level in East Java Province. Additionally, this study contributes to the development of demographic analysis and population management, using valid methods and consistent results between RapidMiner and manual calculations. In conclusion, this research provides a solid foundation for more effective development policy formulation in East Java Province, offering essential information for sustainable population management