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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Forecasting the Number of Patient Visits by Arima and Holwinters Method at the Public Health Center Basri K, Ilham; David Fahmi Abdillah; Titik Khotiah; Jumain; Abdul Rohman
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2008

Abstract

As the number of human populations increases and the economy becomes more advanced, people's awareness of health increases. This can increase the number of patient visits if the community will visit for treatment, therefore it is necessary to pay special attention from the health center to carry out readiness in the fulfillment of facilities and service support equipment, such as services in the outpatient registration place where registration documents must be adjusted to the number of existing patients, if the documents are lacking or have not been made, there can be long queues or accumulation of patients which leads to inadequate service. For this reason, the public health center must carry out careful planning activities, one of which is by conducting forecasting activities in order to overcome these problems.This study compares the best method among the 2 time series methods, then the forecasting results will be compared with the actual data to find which forecasting is the best.The final results showed the MAPE value of the arima method for Direct Patient Visits data was worth 22.55% while the Referral Patient Visits were valued at 47.40% with the Moderate/Feasible category, the Holwinters method for Direct Patient Visits data was worth 7.90% while the Referral Patient Visits were worth 11.90% with the excellent category.can be said that the smallest error value is Holtwinters from Direct Patient Visit data with MAPE 7.90% and from Referral Patient Visit data with MAPE 11.90%. Which is where it is said to be an excellent forecasting category
Co-Authors Ach Khoiri Nabil Adhi Gunawan Advendi Kristiandaru Afiatna, Puji Afni Putri Hairani Afrizal Zein Agung Yulianto Agus Haris Agustina Ari Murti Budi Hastuti Aida Syarif Alif Alvinas Amelia Kartini5 Annisah Rahma Palupi Aqib Zuhri Adi Kusuma Aysha Widya Utami Aysyah Lulu Fuadina Bayu Cahyadi Budiman Abdullah Catur Supriyanto Darda, Ahmad David Fahmi Abdillah David Prayogo Dea Nurazizah Dedy Alan Wirawan Dika Ade Royani Dwi Gemina Dwiki Rahmadhana Dyah Eko Setyowati Dzodi Wijaya Ramadhan Elfrida Ratnawati Emmanuel Kevin Gunawan Endang Silaningsih Erna Susanti Esther Tirza Suzanti Kalalo Evi Dwi Kartikasari Evi Tri Anjani Faiz Budi Hakim Fanji Fakhru Zaman Fernanda Bagus Dwi Prastyo Fiki Wijayanti Gebrina Oktaviani Tarigan Giyo Sastra Manggali Gusti Randy Pratama Halimatussadiah HARIYANTI Hasni Radhwatul Awaliyah Haya Desi Afifah Danu Ado Hilda Apriyani Idri IGNB. Catrawedarma Ilham Basri K Indriani Safitri Intan Febriyana Irene Trivena Montalili Iskar Prayoga Ismail, Ahmad Hafizh Jangka Rulianto Jumain Kania Pusriani Amalia Khairul Muzaka Khilda Afriyani Khoirul Asrofi Kovovatun Nada Kurnia Sakti Latifatul Fajriyah Leila Kalsum Lily Arsanti Lestari Lily Arsanti Lestari Lufitasani Khusnunisa M. Fatih Naufal M. Firdaus Tri Kuncoro M. Qairul Rodzikin Maftuchah Maftuchah Maria Magdalena Melani Maria Suryaningsih Min Sahril Mita Okta Viyani Muhammad Ikbal Riyadi Muhammad Muhyidin Muhammad Nasiruddin Muhammad Ridwan Munasiroh Munasiroh Mustika Erlinaningrum Nadia Miftahul Jannah Nopita Widayanti Noviyana Dwi Herlinda Nurul Chotidjah Rahman, Wadidur Ries Ardhian Sultansyah Rifka Rifka Rijaal Qurrota A’yuni Rinda Kuswati Rischa Nur Anisa Rusdianasari Rusdianasari Salman Salman Sinaga, Marissa Siswanto Soemarijadi Sri Devi Sri Wahyuni Sufiati Bintanah Sugeng Deri Pratama Sulistri Supriadi Surya Rahardja Susi Susilawati Syolahudin Sahariawi Tara Indah Afelia Taryanto Taufiq, Wildan Tiara Ramadhani Titik Khotiah Vincent Suhartono Wahyu Aji Kuswoyo Widi Syahtia Pane Wildan Khoiri Priandana Yanti Mulyawati Yelsa Dearestiani Yuliana Noor Setiawati Ulvie Zahra Humaira Febriyanti Zuli Arista