Soni Soni, Soni
Jurusan Teknik Informatika, Fakultas Ilmu Komputer, Universitas Bina Nusantara, Jl. K.H. Syahdan No. 9, Kemanggisan/Palmerah, Jakarta Barat 11480

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Analysis of Service Quality Policy on Patients Satisfaction at Benjeng Health Centers during COVID-19 Pandemic Soni, Soni; Katmini, Katmini
Journal of Health Policy and Management Vol. 8 No. 1 (2023)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

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Abstract

Background: Patient's assessment of the services of health workers is based on the patient's experience. The purpose of this study is to analyzeinfluence tangible, reliability, responsiveness, assuranceand empathy for the level of satisfaction of visitors to the Benjeng Health Center during the Covid 19 Pandemic.Subjects and Method: This was a cross sectional study with the focus of the research being directed at analyzing influence tangible, reliability, responsiveness, assuranceand empathy for the level of satisfaction of visitors to the Benjeng Health Center during the Covid 19 Pandemic. The total population is 2,000 respondents and a sample of 333 respondents taken using the simple random sampling. The data were described into number and percentage.Results: The findings showed that most of the respondents had a good tangible category as many as 171 respondents (51%). Most of the respondents have a good category of reliability as many as 178 respondents (53%). Most of the respondents have good category responsiviness as many as 165 respondents (50%). Most of the respondents have good assurance category as many as 184 respondents (55%). Most of the respondents have empathy in the good category as many as 174 respondents (52%). Most of the respondents have a satisfied category of satisfaction as many as 237 respondents (71%).influence tangible, reliability, responsiveness, assuranceand empathy for the satisfaction level of visitors to the Benjeng Health Center during the Covid 19 Pandemic with a magnitude of 79.1%.Conclusion: It is expected that respondents can provide constructive input and criticism so that the services provided can be in accordance with what is expected.Keywords: Quality of health service, satisfactionCorrespondence: Soni. Masters Program in Public Health, Strada Indonesian Institute of Health Sciences. Jl. Manila 37, Kediri, East Java 64133, Indonesia. Email: sonihamzah1@gmail.com. Mobile: +628121704715.Journal of Health Policy and Management (2023),08(01): 1-12 https://doi.org/10.26911/thejhpm.2023.08.01.01
Pengembangan Produk UMKM Berdasarkan Preferensi Pasar Menggunakan Algoritma Apriori Astuti, Theresia Widji; Jagat, Lang; Soni, Soni
Sehati Abdimas Vol 7 No 1 (2024): Prosiding Sehati Abdimas 2024
Publisher : PPPM POLTESA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47767/sehati_abdimas.v7i1.953

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) merupakan tulang punggung ekonomi di banyak negara, termasuk Indonesia. UMKM berperan penting dalam penciptaan lapangan kerja, distribusi pendapatan, dan inovasi. Namun, UMKM sering kali menghadapi tantangan dalam mengembangkan produk yang sesuai dengan preferensi pasar yang dinamis. Salah satu tantangan utama yang dihadapi UMKM adalah keterbatasan dalam memahami dan merespon tren pasar. Hal ini sering kali mengakibatkan produk yang tidak sesuai dengan kebutuhan dan keinginan konsumen, sehingga berpotensi menurunkan daya saing UMKM. Menggunakan data mining dalam teknik menggali informasi berharga dari sejumlah besar data, data mining dapat membantu dalam memahami pola pembelian konsumen dan preferensi pasar. Adapun tujuan penelitian ini adalah menerapkan algoritma apriori dalam data mining untuk membantu UMKM dalam mengembangakan produk yang lebih sesuai dengan preferensi pasar. Hasil penelitian ini diharapkan dapat memberikan kontribusi bagi UMKM dalam strategi pengembangan produk serta memberikan manfaat bagi stakeholder terkait dalam mendukung pertumbuhan UMKM yang berkelanjutan. Hasil penelitian menggunakan software weka untuk menemukan hasil analisis yang sudah ditentukan, jumlah data transaksi yang diolah adalah 1360 data dengan nilai support minimum 0.02 dan confidencenya adalah 0.4 yang menghasilkan 5 rules/aturan.