Analisis pola keberhasilan sertifikasi bagi jabatan fungsional arsiparis secara nasional menggunakan metode k-means clustering dan regresi random forest = analysis of the successful pattern of certification for archivist functional positions nationally using the k-means clustering and random forest regression method

Pratama, Muhammad Yoga (2025) Analisis pola keberhasilan sertifikasi bagi jabatan fungsional arsiparis secara nasional menggunakan metode k-means clustering dan regresi random forest = analysis of the successful pattern of certification for archivist functional positions nationally using the k-means clustering and random forest regression method. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Peran Jabatan Fungsional Arsiparis (JFA) sangat penting dalam pengelolaan dan pelestarian arsip di berbagai institusi dan lembaga. Jabatan Fungsional Arsiparis bertanggung jawab dalam pengelolaan arsip, termasuk perencanaan, organisasi, pemilihan, penyimpanan, pemeliharaan, dan pengecekan keaslian serta integritas arsip. Namun masih banyak Arsiparis yang tidak memahami peran dan tanggung jawab menjalankan tugasnya. Untuk meningkatkan kompetensi Arsiparis di Indonesia, dibutuhkan Uji Sertifikasi Kompetensi yang meningkatan kualitas dan mutu Jabatan Fungsional Arsiparis. Dalam pelaksanaannya, banyak JFA yang gagal dalam uji sertifikasi ini. Dari data hasil Sertifikasi JFA dari tahun 2009 sampai 2023 yang dimiliki ANRI, bisa dianalisis pola keberhasilan JFA dalam uji sertifikasi. Penggunaan metode algoritma K-Means Clustering mampu mengelompokkan asesi tanpa definisi kelompok sebelumnya, memungkinkan identifikasi pola yang sulit dipahami serta memberikan pendekatan analisis yang obyektif. Metode Regresi Random Forest memberikan wawasan yang mendalam tentang faktor-faktor utama yang memengaruhi keberhasilan sertifikasi JFA. K-Means menghasilkan tiga klaster berdasarkan variabel Pangkat, Angkatan, dan Nilai, algoritma ini diterapkan untuk mengevaluasi hubungan antara variabel independen (Pangkat, Angkatan, dan Jabatan) dengan Nilai sebagai variabel target. Analisis feature importance menunjukkan bahwa Pangkat dan Jabatan memiliki pengaruh terbesar terhadap hasil sertifikasi. Dengan tingkat akurasi sebesar 65,50%, metode ini memberikan wawasan yang mendalam mengenai faktor-faktor utama yang memengaruhi keberhasilan sertifikasi. Penelitian ini dapat disimpulkan bahwa analisis pola keberhasilan sertifikasi JFA secara nasional menggunakan metode k-means clustering dan regresi random forest dapat berjalan dengan baik. Implikasi penelitian ini bisa menjadi bahan evaluasi dalam penyelenggaraan sertifikasi JFA. / The role of Functional Archivist Positions (JFA) is crucial in the management and preservation of archives across various institutions and organizations. JFA is responsible for archive management, including planning, organizing, selection, storage, maintenance, and verifying the authenticity and integrity of archives. However, many archivists still lack an understanding of their roles and responsibilities in carrying out their duties. To enhance the competence of archivists in Indonesia, a Certification Competency Test is required to improve the quality and standards of JFA. In practice, many JFA fail this certification test. Using the certification data of JFA from 2009 to 2023 provided by ANRI, the patterns of JFA's success in certification tests can be analyzed. The K-Means Clustering algorithm effectively groups examinees without predefined groups, enabling the identification of complex patterns and providing an objective analytical approach. The Random Forest Regression method offers deep insights into the key factors influencing the success of JFA certification. K-Means identified three clusters based on the variables of Rank, Batch, and Score, while Random Forest Regression evaluated the relationships between independent variables (Rank, Batch, and Job Title) and Score as the target variable. Feature importance analysis revealed that Rank and Job Title have the most significant impact on certification outcomes. With an accuracy rate of 65.50%, this method provides valuable insights into the primary factors affecting certification success. This study concludes that analyzing the patterns of JFA certification success nationwide using K-Means Clustering and Random Forest Regression has proven effective. The implications of this research can serve as an evaluation tool for improving the JFA certification process.
Item Type: Thesis (Bachelor)
Creators:
Creators
NIM
Email
ORCID
Pratama, Muhammad Yoga
NIM01035220023
muhyogapratama25@gmail.com
UNSPECIFIED
Contributors:
Contribution
Contributors
NIDN/NIDK
Email
Thesis advisor
Martoyo, Ihan
NIDN0318057301
ihan.martoyo@uph.edu
Uncontrolled Keywords: Jabatan Fungsional Arsiparis, Uji Sertifikasi Kompetensi, K-Means Clustering, Regresi Random Forest, Analisis Feature Importance, The Functional Position of Archivist, Competency Certification Test, Random Forest Regression, Feature Importance Analysis
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Electrical Engineering
Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Electrical Engineering
Depositing User: Magang Input
Date Deposited: 22 Apr 2025 01:35
Last Modified: 22 Apr 2025 01:35
URI: http://repository.uph.edu/id/eprint/68152

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