EVALUASI ATAS IMPLEMENTASI APLIKASI SISTEM AKUNTANSI INSTANSI BASIS AKRUAL (SAIBA) PADA MITRA KERJA KPPN GORONTALO DAN MARISA
Bibliographic record
Abstract
The purpose of this research is to evaluate the success of accrual basic application system (SAIBA) implementation based on user perception with DeLone & McLean (DM) Information System (IS) Success Model Approach. The data used are primary and secondary data. Primary data gathered through questionnaires distributed to respondents while secondary data gathered from other institutions such as the Directorate General of Budget, Directorate General of Treasury, and Treasury Office of Gorontalo and Marisa. This model uses six variables which are system quality, information quality, user satisfaction, use, individual impact, and organizational impact. This research empirically showed that the accrual basic application system (SAIBA) currently implemented not successfully running yet based on all Delone and McLean's success measurement criteria.AbstrakPenelitian ini bertujuan untuk mengevaluasi sejauh mana keberhasilan implementasi aplikasi SAIBA yang telah berjalan selama ini berdasarkan sudut pandang pengguna (user) dengan menggunakan pendekatan Delone & McLean Information System Success Model. Sumber data yang digunakan dalam penelitian ini berasal dari data primer dan data sekunder. Data primer berupa data yang diperoleh langsung dari responden melalui kuesioner yang dibagikan. Sedangkan data sekunder adalah data yang diperoleh dan disajikan oleh pihak-pihak lainnya seperti Direktorat Jenderal Anggaran Kementerian Keuangan, Direktorat Jenderal Perbendaharan Negara Kementerian Keuangan, dan Kantor Pelayanan Perbendaharaan Negara (KPPN) Gorontalo dan Marisa. Model ini menggunakan enam variabel pengukuran yaitu kualitas sistem (system quality), kualitas informasi (information quality), kepuasan pengguna (user satisfaction), penggunaan sistem (use), dampak individu (individual impact) dan dampak organisasi (organizational impact). Penelitian membuktikan secara empiris bahwa implementasi aplikasi SAIBA belum berjalan sukses berdasarkan kriteria pengukuran sesuai model kesuksesan DeLone dan McLean (1992).
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.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".