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Record W7118808332 · doi:10.35965/jbm.v8i1.7772

ANALISIS KOMPETENSI SDM DALAM PENINGKATAN KINERJA MELALUI PENGUASAAN TEKNOLOGI PADA KANTOR PERTANAHAN KABUPATEN BONE

2025· article· W7118808332 on OpenAlex
Arfian Arfian, Lukman Setiawan, Herminawati Abubakar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndonesian Journal of Business and Management · 2025
Typearticle
Language
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCompetence (human resources)Human resourcesSample (material)Data collection

Abstract

fetched live from OpenAlex

Penelitian ini dilakukan bertujuan untuk: (1) menganaliisis pengaruh langsung Kompetensi SDM, dan Penguasaan teknologi terhadap kinerja, (2) menganaliisis pengaruh langsung Penguasaan teknologi terhadap kinerja, (3) menganaliisis pengaruh langsung Kompetensi SDM terhadap kinerja, (4) menganaliisis pengaruh tidak langsung Kompetensi SDM terhadap kinerja melalui Penguasaan teknologi. Penelitian ini menggunakan data primer melalui survei sebanyak 52 Pegawai sebagai populasi. Adapun sampel dalam penelitian adalah sebanyak 50 orang dengan meteode penentuan sampel menggunakan rumus Slovin, penelitian dilakukan selama 2 (dua) bulan yaitu Februari s.d April 2025. Data dianalisis dengan menggunakan program SmartPLS. Hasil penelitian menunjukkan bahwa: (1) Pengaruh Kompetensi SDM berpengaruh langsung terhadap Kinerja Pegawai, (2) Kompetensi SDM berpengaruh terhadap implemenatsi teknologi, (3) Penguasaan teknologi berpengaruh terhjadap kinerja, (4) dan Kompetensi SDM berpengaruh tidak langsung terhadap kinerja Pegawai melalui Penguasaan teknologi. This research was conducted with the following objectives: (1) to analyze the direct influence of human resource competence and technology mastery on performance, (2) to analyze the direct influence of technology mastery on performance, (3) to analyze the direct influence of human resource competence on performance, and (4) to analyze the indirect influence of human resource competence on performance through technology mastery. This research used primary data obtained through a survey of 52 employees in the population. The sample in this research consisted of 50 people, with the sample determination method using the Slovin formula. The research was conducted for two months, from February to April 2025. The data was analyzed using the SmartPLS program. The results of the study show that (1) HR competency has a direct effect on employee performance, (2) HR competency affects technology implementation, (3) technology mastery affects performance, and (4) HR competency has an indirect effect on employee performance through technology mastery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.263
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it