PERENCANAAN STRATEGIS SISTEM INFORMASI PERUSAHAAN DENGAN PENDEKATAN MODEL WARD AND PEPPARD (Studi Kasus PG.FANADA SUPER PLERED PURWAKARTA)
Bibliographic record
Abstract
ABSTRAK Perencanaan strategi SI PG.FANADA SUPER dilakukan dengan dua pendekatan. Pertama menggunakan metodologi pemaduan strategis (strategic alignment), yaitu merumuskan strategi SI berdasarkan strategi bisnis PG.Fanada Super Plered dengan alat bantu Balanced Scorecard (BSC) dan Critical Success Factor (CSF). Kedua menggunakan metodologi dampak kompetitif (competitive impact), yaitu merumuskan strategi SI untuk meningkatkan daya saing beberapa perusahaan genteng di wilayah PURWASUKA (Purwakarta, Subang,Karawang) dan dengan alat bantu analisis rantai nilai (value chain analysis). Hasil perencanaan strategi SI berupa portofolio aplikasi, yang dikategorikan berdasarkan kontribusinya terhadap strategi bisnis PG.Fanada Super Plered dan perannya dalam proses bisnis ini. Kata Kunci : perencanaan strategi, pemaduan strategis, dampak kompetitif, balanced scorecard, critical success factor, portofolio aplikasi.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".