Sistem Pendukung Keputusan Berbasis Metode Topsis untuk Rekomendasi Pengadaan Alat Kesehatan
Bibliographic record
Abstract
The development of information technology has brought significant changes to the medical device procurement process, particularly within government institutions such as the Health Office. The procurement of appropriate, efficient, and objective medical devices is crucial to supporting optimal medical services, yet the decision-making process is often constrained by limited budgets and the complexity of multiple assessment criteria. This study aims to design and implement a decision support system (DSS) based on the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to provide recommendations for medical device procurement at the Binjai Health Office. The DSS evaluates six main criteria: price, quality, durability, ease of maintenance, medical necessity, and safety level, using procurement data from the 2022–2024 period. The TOPSIS method is applied to calculate the relative closeness of each alternative to the ideal solution, enabling decision-makers to rank medical device options objectively and systematically. The findings show that the DSS successfully prioritizes procurement alternatives, helping stakeholders allocate budgets more effectively and transparently. In addition, the system minimizes subjective bias by integrating quantitative analysis with clearly defined criteria. The system is implemented in a web-based environment with MySQL as the database, ensuring accessibility and scalability for future use. Overall, this research demonstrates that integrating TOPSIS into a decision support system can enhance the efficiency, accuracy, and accountability of medical device procurement in public health agencies. The study is expected to contribute to improving budget management and strengthening the quality of health services through better resource allocation.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".