Learning Vector Quantization untuk Klasifikasi Abstrak Tesis
Bibliographic record
Abstract
Data berukuran besar yang sudah disimpan jarang digunakan secara optimal karena manusia seringkali tidak memiliki waktu dan kemampuan yang cukup untuk mengelolanya. Data bervolume besar seperti data teks, jauh melampaui kapasitas pengolahan manusia yang sangat terbatas. Kasus yang disoroti adalah data abstrak tugas akhir mahasiswa jurusan teknik informatika Universitas Trunojoyo Madura. Dokumen tugas akhir oleh mahasiswa terkait hanya diupload pada SIMTAK (Sistem Informasi Tugas Akhir) dan pelabelan bidang minat penelitian dilakukan manual oleh mahasiswa tersebut, sehingga akan ada kemungkian saat mahasiswa mengisi bidang minat tidak sesuai. Untuk menanggulangi hal tersebut, diperlukan adanya mekanisme pelabelan dokumen secara otomatis, untuk meminimalisir kesalahan. Pada penelitian kali ini dilakukan klasifikasi dokumen abstrak tugas akhir menggunakan metode Learning Vector Quantization (LVQ). Data abstrak diklasifikasikan menjadi 3 yaitu SI RPL (Sistem Informasi – Rekayasa Perangkat Lunak), CAI (Computation – Artificial Intelligence) dan Multimedia. Dari berbagai ujicoba yang dilakukan didapatkan hasil metode LVQ berhasil mengenali 90% data abstrak, dengan berhasil mengenali 100% bidang minat SI RPL dan CAI, dan hanya 70% untuk bidang minat Multimedia. Dengan kondisi terbaik didapatkan dengan parameter reduksi dimensi 20% dan nilai learning rate antara 0,1-0,5.Huge size of data that have been saved are rarely used optimally because people often do not have enough time and ability to manage. Large volumes of data such as text data, exceed human processing capacity. The case highlighted was the final project abstract data from informatics engineering student Trunojoyo University. Documents abstract just uploaded on SIMTAK (Final Project Information System) and the labeling of the areas of interest of research is done manually by the student, so that there will be a possibility to fill the field of interest while the student is not appropriate. To overcome this, we need a mechanism for labeling a document automatically, to minimize errors. In the present study conducted abstract document classification using Learning Vector Quantization (LVQ). Abstract data classified into three class, SI RPL, CAI and Multimedia. Of the various tests carried out showed that LVQ method successfully recognize 90% of abstract data, to successfully identify 100% interest in the field of RPL SI and CAI, and only 70% for areas of interest Multimedia. With the best conditions obtained with the parameter dimension reduction of 20% and the value of learning rate between 0.1-0.5.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".