VISUALISASI DATA SISWA BERPRESTASI MENGGUNAKAN K-MEANS CLUSTERING UNTUK PENYELEKSIAN OLIMPIADE (STUDI KASUS : SDN KAPUK 08 PETANG)

PUTRA, DAFFA ABHINAYA (2025) VISUALISASI DATA SISWA BERPRESTASI MENGGUNAKAN K-MEANS CLUSTERING UNTUK PENYELEKSIAN OLIMPIADE (STUDI KASUS : SDN KAPUK 08 PETANG). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The process of identifying high-achieving students at SDN Kapuk 08 Petang is still conducted manually and is not yet supported by a data-driven system, resulting in challenges related to objectivity and efficiency in selecting participants for academic competitions. This study aims to develop a dashboard-based data visualization system that can cluster students based on academic performance using the K-Means Clustering algorithm. The system was developed using the Waterfall model, with student academic scores analyzed and grouped into three clusters: low, medium, and high. Data visualization was created using Google Looker Studio and Google Sheets to produce an interactive dashboard. The results show that the system helps the school evaluate students’ academic achievements, select potential candidates for Olympiads more objectively and effectively, and support data-driven decision-making. The system can also be implemented sustainably to monitor students' academic progress over time. Keywords: Data Visualization, K-Means Clustering, Google Looker Studio Proses identifikasi siswa berprestasi di SDN Kapuk 08 Petang masih dilakukan secara manual dan belum didukung oleh sistem berbasis data, sehingga menimbulkan kendala dalam objektivitas dan efisiensi seleksi peserta Olimpiade. Penelitian ini bertujuan untuk mengembangkan sistem visualisasi data berbasis dashboard yang mampu mengelompokkan siswa berdasarkan kinerja akademik menggunakan algoritma K-Means Clustering. Pengembangan sistem dilakukan dengan model Waterfall, menggunakan data nilai siswa yang dianalisis menjadi tiga cluster: rendah, sedang, dan tinggi. Visualisasi data dibuat menggunakan Google Looker Studio dan Google Sheets untuk menghasilkan dashboard interaktif. Hasil penelitian menunjukkan bahwa sistem ini dapat mempermudah sekolah dalam mengevaluasi capaian akademik siswa, melakukan seleksi calon peserta Olimpiade secara lebih objektif dan terarah, serta mendukung pengambilan keputusan berbasis data. Sistem ini juga dapat diimplementasikan secara berkelanjutan untuk memantau perkembangan akademik siswa. Kata Kunci: Visualisasi Data, K-Means Clustering, Google Looker Studio

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 044
NIM/NIDN Creators:	41821010069
Uncontrolled Keywords:	Visualisasi Data, K-Means Clustering, Google Looker Studio
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem > 003.1 System Identification/Identifikasi Sistem 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum > 658.3 Personnel Management/Manajemen Personalia, Manajemen Sumber Daya Manusia, Manajemen SDM
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	06 Aug 2025 07:55
Last Modified:	06 Aug 2025 07:55
URI:	http://repository.mercubuana.ac.id/id/eprint/96617

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