ADAPTASI MODEL MCGILL YANG DIMODIFIKASI GUNA MENGUJI KEBERHASILAN IMPLEMENTASI SISTEM INFORMASI PENGELOLAAN KEUANGAN DAERAH (SIPKD) KABUPATEN LAMPUNG TIMUR
Bibliographic record
Abstract
ABSTRAK INDONESIA \n \nPenelitian ini bertujuan menganalisis dan menjelaskan pengaruh aplikasi Sistem Informasi Pengelolaan Keuangan Daerah terhadap kinerja karyawan (dampak individu) pada Pemerintah Daerah Kabupaten Lampung Timur. \nPenelitian ini menggunakan data primer yang merupakan data penelitian yang diperoleh langsung dari sumber aslinya. Objek penelitian adalah semua pengguna akhir sistem (end-user) pada Dinas Pendapatan, Pengelola Keuangan dan Aset Daerah (DP2KAD) Kabupaten Lampung Timur. Metode pemilihan sampel penelitian ini adalah purposive sampling yang merupakan metode pengambilan sampel dengan didasarkan pada kriteria tertentu. Sampel dalam penelitian ini berjumlah 62 responden. Metode analisis data adalah dengan menggunakan Partial Least Square (PLS) yaitu salah satu metode alternatif Structural Equation Modeling (SEM). \nBerdasarkan hasil penelitian terhadap 62 responden yang menggunakan aplikasi SIPKD dalam penelitian ini, hasil penelitian secara statistik memberi bukti bahwa hampir keseluruhan hipotesis yang diajukan oleh peneliti diterima. Hanya pada hipotesis 5 yaitu kualitas informasi memiliki pengaruh positif terhadap penggunaan sistem ditolak. \nKata Kunci : Keberhasilan Sistem Informasi, Kualitas Sistem, Persepsi Kualitas Sistem, Kualitas Informasi, Penggunaan Sistem, Kepuasan Pengguna Akhir, Dampak Individu \n \nABSTRAK INGGRIS \n \nThis study aim to analyze and explain the effect of Government Financial Management Information System (SIPKD) application on employee performance (individual impact) at East Lampung Government. \nThis study using primary data which is obtained directly from the original source. The object of research is all the end user in Department of Revenue, Financial Management and Public Asset (DP2KAD) at East Lampung Government. The sampling method of this study using purposive sampling which is sampling method is based on certain criteria. The sample in this study amounted to 62 respondents. This study using Partial Least Square (PLS) for data analysis, It's one of the alternative method of Structural Equation Modeling (SEM). \nBased on the results of a study from 62 respondents who uses SIPKD application in this research, the results statistically provide evidence that almost the entire tested hypothesis proposed by researchers are supported. The only one tested hypothesis which isn’t supported in this research are fifth hypothesis, Information Quality has a positive influence on Intended Use. \nKeyword : Information System Success, System Quality, Percieved System Quality, Information Quality, Intended Use, User Satisfaction, Individual Impact \n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".