PERANCANGAN SISTEM PENDETEKSI BERITA HOAX MENGGUNAKAN ALGORITMA LEVENSHTEIN DISTANCE BERBASIS PHP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the 4.0 era where the Internet is an important part of life today, information can be easily accessed anytime, anywhere. But not all information distributed through the internet is in the form of facts. Data presented by the Ministry of Communication and Information based on a survey conducted in 2018 said that as many as 800,000 sites in Indonesia indicated that non-fact or hoax news spreaders were indicated. As a result of hoax news generated is very dangerous because it attacks the minds of the human subconscious, so it is needed a system that can detect hoax news. In this study used a database containing hoax news documents. The algorithm applied is the TF-IDF algorithm to measure the weight of a word in a hoax document and combined with the Levenshtein Distance (LD) algorithm to measure the distance between words in a document. The application of the Levenshtein Distance Method in the Hoax Detection System has several stages that begin with the pre-processing of the word (prepocessing text) followed by the TF-IDF calculation phase and then the minimum distance calculation between words using the Levenshtein Distance algorithm. The result of a limit of 0.1 on 40 documents that have been classified as test data has high Precision, Recall and Accuracy values, namely Precision 1; Recall 0.71; and Accuracy 80%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it