SISTEM PENDUKUNG KEPUTUSAN PENENTUAN TINGKAT RADIKALISME MENGGUNAKAN METODE FUZZY INFERENCE SYSTEM
Bibliographic record
Abstract
Radikalisme berkembang begitu pesat. Hal ini menjadi pekerjaan rumah bagi kita semua agar ideologi radikal tidak semakin meluas. Maka perlu dibuatnya suatu pendekatan untuk membuat keputusan terkait tingkat pemahaman radikalisme tersebut apakah memiliki pemahaman tersebut atau tidak. Hal yang berkaitan dengan ideologi radikalisme ada 4 yaitu 1. Toleransi, 2. Sosial, Ekonomi, dan Politik, 3. Pemahaman Budaya dan 4. Nilai Keagamaan. 4 Perkara yang telah disebutkan dijadikan sebagai Parameter. Dalam menentukan parameter, peneliti melakukan studi literatur. Dalam parameter tersebut, masing-masing memiliki sub parameter, pada parameter Toleransi memiliki 4 sub parameter, parameter Sosial, Ekonomi, Politik memiliki 4 sub parameter, parameter Budaya memiliki 3 sub parameter, parameter Keagamaan memiliki 4 sub parameter. Metode yang digunakan dalam penelitian ini adalah Fuzzy Inference System. Fuzzy Inference System memiliki 4 proses diantaranya adalah fuzzifikasi, inference system, defuzzifikasi, dan rule base. Hasil dari penelitian ini adalah sebuah keputusan, yang dimana terdapat 3 kriteria keputusan yaitu Moderat, Konservatif dan Radikal. Kata Kunci: Radikalisme, Parameter, Fuzzy Inference System
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".