PENERAPAN DATA MINING PENGELOMPOKAN PESERTA BPJS KETENAGAKERJAAN BERDASARKAN PROGRAM YANG DIAMBIL MENGGUNAKAN METODE CLUSTERING
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
The implementation of the social security program is one of the responsibilities and obligations of the State, to provide socio-economic protection to the community. Indonesia, like other developing countries, develops social security programs based on funded social security, namely social security that is funded by participants and is still limited to working people in the formal sector. BPJS Ketenagakerjaan continues to improve competence in all aspects of service while developing various programs and benefits that can be directly enjoyed by workers and their families. Non-Wage Recipient Workers (BPU) are employees who carry out economic activities or businesses independently to earn income from their activities or business. The problem that hinders the length of data collection for BPJS Employment participants is the process of determining the social security program that will be taken by Non-Wage Recipient (BPU) workers from the program taken by BPJS Ketenagakerjaan participants. owned is very small and only enough for the daily needs of participants. Data Mining is a data mining process in very large amounts of data using statistical, and mathematical methods, and utilizing the latest Artificial Intelligence technology. Data mining in the process of grouping data can use a grouping method, namely the Clustering method. The system is designed with the MATLAB R2014a programming application, after testing with the system, the results obtained are that in group 1 there are 370 data, group 2 there are 359 data and group 3 there are 271 data with a total of 100 data participants.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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