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All Journal Jurnal Sistem Informasi dan Sains Teknologi Journal of Data Mining and Information Systems
Sardo Sipayung
Universitas Katolik Santo Thomas Medan
Computer Science & IT

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Penerapan Normalisasi Data pada Angkatan Kerja Indonesia Bulan Februari 2025 Berdasarkan Kelompok Umur Anastasya Jesica Sidauruk; Juan Sebastian Sirait; Sardo Sipayung
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7023

Abstract

Data normalization is a crucial initial step in the data mining process, aiming to reduce scale differences in numerical attributes, allowing for more objective and accurate analysis. This study aims to implement and evaluate data normalization techniques on the Indonesian workforce in February 2025 based on age category. The data used is secondary data obtained from the Central Bureau of Statistics (BPS) thru the National Labor Force Survey (SAKERNAS), which includes numerical attributes such as the number of employed people, the number of unemployed, the size of the labor force, and the percentage of the working population. The normalization methods used in this study consist of Min-Max Normalization, Z-Score Normalization, and Decimal Scaling Normalization. The research process includes data collection, selection of data from the period February 2025, data cleaning, application of normalization techniques, and analysis of the normalization results. The research findings indicate that all three normalization methods successfully leveled the value scales across attributes that previously showed significant differences in their value ranges. Min-Max normalization is effective in converting data to a specific range, Z-Score can identify deviations from the mean value, while Decimal Scaling facilitates proportional comparisons between age categories. Empirically, this study confirms that the 25-44 age group will be the most dominant in the structure of the Indonesian workforce in February 2025. Implementing data normalization has proven to improve data quality and support more accurate labor analysis.
OPTIMASI KINERJA SSH SERVER MENGGUNAKAN ALGORITMA KOMPRESI LZ4 PADA PROTOKOL TRANSFER Jumita Yohana Hutagalung; Febrina Yohana Sinaga; Sardo Sipayung
Jurnal Sistem Informasi dan Sains Teknologi Vol 8, No 1 (2026): Jurnal Sistem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v8i1.2703

Abstract

Penelitian ini bertujuan mengoptimalkan kinerja SSH server melalui implementasi algoritma kompresi LZ4 untuk mengatasi bottleneck transfer data pada protokol SSH, khususnya dalam lingkungan jaringan enterprise Indonesia.Metodologi eksperimental menggunakan testbed VirtualBox dengan dua VM Debian 10 Buster. Penelitian membandingkan kinerja SSH server pada tiga konfigurasi: kompresi LZ4, zlib default, dan tanpa kompresi. Pengujian mencakup berbagai jenis file dengan ukuran 1MB-1GB. Parameter yang diukur meliputi throughput, pemanfaatan CPU, konsumsi memori, rasio kompresi, dan latensi menggunakan OpenSSH 7.9 yang dimodifikasi.Kompresi LZ4 menunjukkan peningkatan throughput 35% dibandingkan zlib dengan pengurangan utilisasi CPU sebesar 22%. Throughput rata-rata mencapai 145 MB/s versus 107 MB/s untuk zlib. Penggunaan memori berkurang 18% sambil mempertahankan efektivitas kompresi 68% relatif terhadap zlib. Latensi meningkat 15% untuk sesi real-time.Integrasi LZ4 secara signifikan meningkatkan kinerja server SSH dengan keseimbangan optimal antara kecepatan kompresi dan pemanfaatan sumber daya, menawarkan manfaat praktis penerapan SSH throughput tinggi di lingkungan perusahaan Indonesia. 
Co-Authors Anastasya Jesica Sidauruk Febrina Yohana Sinaga Juan Sebastian Sirait Jumita Yohana Hutagalung