Application of the Apriori Algorithm to Determine Public Service Patterns at the Subdistrict Office
Bibliographic record
Abstract
Public services at the sub-district office are a form of direct interaction between the government and the community. One of the public services is administrative services such as issuing ID cards, family cards, document legalization, domicile certificates, and SKTM certificates. At the Hamparan Perak District Office, there are several major issues that frequently arise in public services, including long waiting times, many citizens complaining about the length of service due to long queues and processes that are still conducted manually. The lack of service management due to the absence of a clear pattern in public services leads to an imbalance in the allocation of resources and manpower. By using the Apriori Algorithm, patterns of interrelated services can be identified, enabling the subdistrict office to optimize its service system. The Apriori Algorithm works by identifying frequently used service combinations (frequent itemsets) and forming association rules, thereby providing recommendations for service improvement. This method can improve service efficiency by identifying which services are frequently used together. Through testing using the RapidMiner application, this study identified age, gender, occupation, address, type of service, service time, and application status. The results show that 248 association rules were formed, with the highest Best Rule value of 5% support and 82% confidence on 3 item sets. The rule states: “If gender is male and age is between 56 and 46 years, then the application status is ‘Processed.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".