Perancangan Sistem Pendeteksi Berita Hoax Menggunakan Algoritma Levenshtein Distance Berbasis Php
Bibliographic record
Abstract
Di era 4.0 dimana Internet menjadi bagian penting dalam kehidupan saat ini, informasi dapat dengan mudah di akses kapanpun dan dimanapun. Namun tidak seluruh informasi yang disebarkan melalui internet berupa fakta. Data yang dipaparkan oleh Kementrian Komunikasi dan Informatika berdsarkan survey yang dilakukan pada tahaun 2018 menyebut sebanyak 800.000 situs di Indonesia terindikasi penyebar berita non-fakta atau hoax. Akibat yang ditimbulkan berita hoax sangat berbahaya karena menyerang pikiran alam bawah sadar manusia, sehingga sangat dibutuhkan sistem yang dapat mendeteksi berita hoax. Dalam penelitian ini digunakan database yang berisi dokumen berita hoax. Algoritma yang diterapkan adalah algoritma TF-IDF untuk mengukur bobot suatu kata dalam dokumen hoax dan dikombinasikan dengan algoritma Levenshtein Distance (LD) untuk mengukur jarak antar kata dalam dokumen. Penerapan Metode Levenshtein Distance dalam Sistem Deteksi Hoaxmemiliki beberapa tahap yang dimulai dengan tahap pra-pemrosesan kata (prepocessing text) dilanjutkan dengan tahap perhitungan TF-IDF dankemudian tahap perhitungan jarak minimum antar kata menggunakan algoritmaLevenshtein Distance. Hasil batas 0,1 pada 40 dokumen yang sudah terklasifikasi sebagai data uji memiliki nilai Precision, Recall dan Accuracy yang tinggi, yaitu Precision1; Recall0,71;dan Accuracy80%.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".