SEBARAN KATA KUNCI TIGA JURNAL PERPUSDOKINFO TERAKREDITASI SINTA DI INDONESIA PERIODE 2017 - 2021
Bibliographic record
Abstract
Belum adanya kajian tentang sebaran kata kunci di jurnal Perpusdokinfo di Indonesia, mendorong dilakukannya penelitian tentang sebaran kata kunci khususnya di BACA, Khizanah Al Hikmah (KAH), dan Jurnal Ilmu Informasi, Perpustakaan, dan Kearsipan (JIPK) periode 2017 – 2021. Penelitian dilakukan dengan metode bibliometri, dengan tujuan untuk mengetahui 1) Jumlah artikel yang diterbitkan BACA, KAH, dan JIIPK; 2) Penulis yang berkontribusi ;3) Kata kunci yang dibuat penulis BACA, KAH, dan JIKP; 4) Topik penelitian dari kata kunci terbanyak di BACA, KAH, dan JIIKP; 5) Topik penelitian berdasarkan jumlah keseluruhan kata kunci terbanyak dari 3 jurnal. Pengunpulan data dilakukan dari sumber data masing- masing jurnal, yaitu; BACA, https://jurnalbaca.pdii.lipi.go.id/ index.php/baca, KAH, dengan alamat journal.uin-alauddin.ac.id/index.php/khizanah-al-hikmah dan JIIPK adalah jipk.ui.ac.id/index.php/jipk. Hasil penelitian: 1) Artikel di BACA 93 judul, 99 judul untuk KAH, dan JIPK 60 judul; 2) Penulis untuk BACA adalah 220 orang, KAH 217 orang, dan JIPK 127 orang; 3). Jumlah kata kunci BACA adalah 423, KAH 297, dan 264. 4). Topik penelitian terbanyak BACA adalah academic library (10 kali), KAH adalah bibiliometrics (14 kali), dan JIPK adalah information (3 kali).5) Secara keseluruhan penelitian terbanyak berdasarkan kata kunci adalah, Bibliometrics (17 kali), academic library, (10 kali), dan Information, Information needs, dan Information retrieval, masing – masing 5 kali. Kesimpulan penelitian adalah, jumlah artikel, penulis, dan kata kunci terbanyak dipegang oleh BACA. Penelitian terbanyak di BACA berkaitan dengan academic library, bibliometric paling banyak diteliti di KAH, dan information paling banyak diteliti di JIPK.The absence of astudy on the distribution of keywords in LIS journals in Indonesia has prompted research on the distribution of keywords, especially in BACA, Khizanah al Hikah (KAH), and Jurnal Ilmu Informasi Perpustakaan, dan Kearsipan (JIPK), for the 2017-2022 period. The research was conducted with bibliometric method, with the aim of to find out: 1) Number of articles published by BACA, KAH and JIPK; 2) Authors who contributed; 3) Keywords created by the authors were BACA, KAH, and JIPK; 4) Research topics from 3 journals. Data collection is carried out from data sources for each journl, namely BACA, https://jirnalbaca.pdii.lipi.go.od/index.php/baca, KAH, with the address journal.uinalauddin.ac.id/ index.php/khizanah-al-hikmah and JIPK is ui.aac.id/index.php/jipk. Researh results:1) Articles in BACA have 93 titles, 99 titles for KAH, and 60 titles for JIPK; 2) Authors for BACA are 220 people, KAH 217 people, and JIPK 127 people; 3) The number of keywords BACA is 423, KAH 297, and JIPK 264. 4) The research topic with the most BACA is academic library (10 times), KAH is bibliometrics (14 times), and JIPK is information (3 times). 5) Overall, it is known that the most rserach based on keywords is bibliometrics (17 times), academic library (10 times), and information, information needs, information retrieval each 5 times. The conclusion is , that during 2017 – 2021, the highest number of articles, authors, and keywords is held by BACA. Most of the research at BACA is related to the academic library, bibliometrics is the most studied at KAH, and the most researched information is at JIPK
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".