APLIKASI SOFTWARE DETEKSI DINI FAKTOR RISIKO DIABETES MELLITUS PADA DEWASA DI PUSKESMAS KERTASMAYA KABUPATEN INDRAMAYU
Bibliographic record
Abstract
Diabetes Mellitus adalah penyakit menahun (kronis) berupa gangguan metabolik yang ditandai dengan kadar gula darah yang melebihi batas normal. Organisasi International Diabetes Federation (IDF) memperkirakan sedikitnya terdapat 463 juta orang pada usia 20 – 79 tahun di dunia menderita diabetes pada tahun 2019. Pusat data dan informasi Jawa Barat Tahun 2019 menunjukkan jumlah penderita Diabetes Mellitus sebanyak 5504 orang. Diabetes Mellitus sering disebut dengan the mother of desease, dimana komplikasi dari diabetes mellitus ini mengakibatkan beberapa penyakit lainnya, diantaranya kebutaan, gagal ginjal, gagal jantung, sampai kematian. Hal ini ditunjang dengan pola makan yang tidak sehat, aktifitas fisik yang minimal dilakukan. Oleh karena itu diperlukan deteksi dini faktor risiko Diabetes Mellitus untuk melakukan pencegahan terjadinya penyakit. Berdasarkan uraian masalah tersebut, maka pengabdi tertarik untuk melaksanakan kegiatan pengabdian masyarakat dengan membuat Penguatan Promosi Kesehatan melalui Aplikasi Software Deteksi Dini Faktor Risiko Diabetes Mellitus dan Cara Pencegahan Diabetes Mellitus yang dapat digunakan oleh Kader Kesehatan yang ada di masyarakat. Dalam aplikasi tersebut di dalamnya tertuang tentang bagaimana cara deteksi dini faktor risiko Diabetes Mellitus dengan menggunakan The Canadian Diabetes Risk Questionnaire (CANRISK) serta cara pencegahan dengan Video Senam Diabetes. Kegiatan pengabdian mendapat dukungan dinas Kesehatan dan puskesmas, kegiatan ini bertujuan untuk meningkatkan keterampilan Kader dalam mendeteksi Diabetes Mellitus di wilayahnya sehingga penanganan pada pasien Diabetes Mellitus bisa cepat diatasi dengan baik yang dapat menekan angka kematian akibat penyakit Diabetes Mellitus. Diabetes Mellitus (DM) is a chronic disease characterized by blood sugar levels that exceed normal limits. The International Diabetes Federation (IDF) organization in 2019 estimates that at least 463 million people aged 20 - 79 years in the world suffer from diabetes. The data acquired from West Java information centre in 2019 shows that there are 5504 people suffered from DM. DM is often called the mother of diseases, because the complications from diabetes mellitus result in several other diseases, including blindness, kidney failure, heart failure, and even death. These cases are supported by unhealthy eating patterns also lack of physical activity. Therefore, early detection of risk factors for DM is needed to prevent the disease since the beginning. According to the problem explained, the writer is interested in carrying out community service activities. The writer created Health Promotion Strengthening through Software Applications for Early Detection of DM Risk Factors and Methods for Preventing DM which can be used by Agent of Health in the community. This application explains how to detect early risk factors for DM using The Canadian Diabetes Risk Questionnaire (CANRISK) as well as how to prevent it using Diabetes Exercise Videos. The service activity is supported by the Health Service and Community Health Centre. This activity aims to improve Agent of Health’s skills in detecting people with high risk in DM, especially in their surrounding in order to DM patients can be treated immediately.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".