Claim Missing Document
Check
Articles

Found 2 Documents
Search

Implementasi Pelayanan Medis dan Bantuan Obat kepada Warga Terdampak Banjir di Desa Labansari Nisa Nurhidayanti; Arum Tarina; Emmelia Tan; Dadang Heri Kusumah; Tri Ngudi Wiyatno; Edi Widodo
JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Vol 3, No 4: November (2022)
Publisher : ICSE (Institute of Computer Science and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jpkmi.v3i4.528

Abstract

Abstrak: Kabupaten Bekasi merupakan kabupaten yang mengalami musibah banjir pada Sabtu, 20 Februari 2021, musibah ini telah melanda 17 kecamatan dan 40 desa dengan ketinggian air antara 40 – 150 sentimeter. Hal ini disebabkan karena intensitas curah hujan yang tinggi, meluapnya beberapa aliran sungai sebagai akibat dari berkurangnya lahan hijau dan meningkatnya pembangunan. Dampak negatif pasca banjir adalah kotornya rumah warga akibat lumpur yang harus segera dibersihkan, beberapa kerugian material dan munculnya beberapa penyakit akibat banjir yaitu penyakit kulit dan diare. Kegiatan pengabdian kepada masyarakat ( PkM) ini bertujuan untuk memberikan bantuan pelayanan medis dan obat-obatan kepada warga yang terdampak banjir. Kegiatan PkM ini dibagi menjadi 3 tahap yaitu tahap perencanaan, tahap pelaksanaan dan tahap evaluasi. Hasil PkM menunjukkan sebanyak 12,18% warga yang berobat dengan keluhan penyakit kulit dan diare; batuk flu sebanyak 35,90%; darah tinggi sebanyak 11,54% ; diabetes, asam urat dan kolesterol sebanyak 27,56% dan lain-lain sebanyak 12,82%. Dampak positif dari PkM ini yaitu terlaksananya program pelayanan medis dan bantuan obat-obatan yang dapat membantu meringankan beban ekonomi warga dalam melakukan pengobatan gratis, kemudahan akses dan pelayanan yang baik dari petugas yang ramah.Abstract: Bekasi Regency is a district that experienced floods on Saturday, February 20, 2021, this disaster hit 17 sub-districts and 40 villages with air altitudes between 40-150 centimeters. This is due to high rainfall, excessive rainfall, multiple river flows as a result of reduced green land and development. negative post-flood are residents' houses due to mud that must be cleaned immediately, some material losses and the emergence of several diseases due to flooding, namely skin diseases and diarrhea. This community service activity (PKM) aims to provide medical services and medicines to residents in the form of floods. This PkM activity is divided into 3 stages, namely the planning stage, the implementation stage and the evaluation stage. The results of the PkM show that as many as 12.18% of residents who seek treatment with complaints of skin diseases and diarrhea; cough flu as much as 35.90%; high blood pressure as much as 11.54%; diabetes, uric acid and cholesterol as much as 27.56% and others as much as 12.82%. The positive impact of this PkM is the implementation of a medical service program and medical assistance that can help ease the economic burden of residents in providing free treatment, easy access and good service from friendly officers.
Pengelompokan Untuk Penjualan Obat Dengan Menggunakan Algoritma K-Means Holwati; Edi Widodo; Wahyu Hadikristanto
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.848

Abstract

Drug grouping is an arrangement that adjusts to the flow of placement or drug layout is more suitable for standard processes. Utilization of existing data through the clustering method approach can be applied to analyze in grouping drug data on data availability and inventory in warehouses so as to provide knowledge and information. The clustering method is processed using the K-Means algorithm where the results also show a new knowledge, namely the grouping of drug data based on 2 clusters. Cluster 1 is a high need category with availability of 71 out of 100 availability categories based on the amount of drug data tested, then cluster 2 is a drug category with moderate or low availability, namely 29 out of 100 availability categories based on the number of drug data tested. Tests using Rapid Miner tools can also produce similar insights, namely each cluster has cluster group members according to manual calculations such as Cluster_0 in Rapid Miner has 72 cluster members representing the Medium cluster, Cluster_1 has 72 cluster group members as high cluster representations, and Cluster_2 has 3 cluster members corresponding to low representation.