Analisis Sentimen Respons Twitter terhadap Persyaratan Badan Penyelenggara Jaminan Sosial (BPJS) di Kantor Pertanahan
Bibliographic record
Abstract
The Indonesian government has issued a policy regarding applications for registration services for the transfer of land rights or ownership rights to apartment units because buying and selling must be accompanied by a photocopy of the BPJS Health participant card. This policy has given rise to various kinds of public opinion, including that of internet residents (netizens) on Twitter and social media. This research aims to determine netizens' responses to this policy. This research uses quantitative methods. Crawling and data analysis use the Python programming language or lexicon-based method with the program execution time before August 19, 2023. The research results show that as many as 57.7% of Twitter users' opinions responded positively to BPJS requirements for service activities at land offices, compared to opinions that responded negatively by as much as 33.1% and neutrally by as much as 9.2%. Based on the source, tweets from government accounts tend to give positive responses, while personal accounts give negative responses. Sentiment analysis can provide valuable insight for the government and related agencies to evaluate and improve public services quickly and practically by examining the views, concerns, and hopes of the community regarding the policies that have been established. Pemerintah Indonesia mengeluarkan kebijakan pada permohonan pelayanan pendaftaran peralihan hak atas tanah atau hak milik atas satuan rumah susun karena jual beli harus dilengkapi dengan fotokopi kartu peserta BPJS Kesehatan. Kebijakan tersebut menimbulkan berbagai macam opini masyarakat, termasuk warga internet (netizen) di media sosial twitter. Penelitian ini bertujuan untuk mengetahui tanggapan netizen terhadap kebijakan tersebut. Penelitian ini menggunakan metode kuantitatif. Crawling dan analisis datanya menggunakan bahasa pemrograman python atau metode lexicon-based dengan waktu execute program sebelum tanggal 19 Agustus 2023. Hasil penelitian menunjukkan sebanyak 57,7% opini pengguna twitter memberikan tanggapan positif terhadap persyaratan BPJS pada kegiatan pelayanan di kantor pertanahan, opini yang menanggapi negatif sebanyak 33,1%, dan netral sebanyak 9,2%. Berdasarkan sumbernya, tweet dari yang berasal dari akun milik pemerintah cenderung memberikan tanggapan positif, sedangkan akun personal memberikan tanggapan negatif. Analisis sentimen dapat memberikan wawasan yang berharga bagi pemerintah dan instansi terkait untuk mengevaluasi serta meningkatkan pelayanan publik secara cepat dan praktis dengan mengkaji pandangan, kekhawatiran, dan harapan masyarakat atas kebijakan yang telah ditetapkan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".