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All Journal Journal of Information Systems and Informatics IPSIKOM
Beby Tiara
STMIK Insan Pembangunan
Computer Science & IT

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Development of Coronavirus Disease Patient Registration Information System with Object Oriented System Approach Riyanto Riyanto; Mustar Aman; Beby Tiara; Nuri Wiyono; Yunianto Agung Nugroho
Journal of Information System and Informatics Vol 3 No 4 (2021): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v3i4.195

Abstract

The rapid development of Information and Communication Technology (ICT), it certainly has a big impact on very significant changes in various fields. Hospitals are health service institutions that provide complete individual health services that provide inpatient, outpatient, and emergency services. emergency. Until now, the registration system for Coronavirus patients at the Tangerang Regency Hospital has not been maximized and has not displayed inpatient rooms for Coronavirus patients. The problem that occurs in the Coronavirus patient registration system at the Tangerang Regency Hospital is that it is not yet computerized or web-based because the Coronavirus patient registration process is still using the Microsoft Excel application, patient data processing is still conventional and the list of Coronavirus patients has not been detailed, patients who have already registered, it turns out that there are no inpatient rooms available so that many Coronavirus patients are slow to be helped. The aim of the research is to build a fast and accurate Coronavirus patient registration system. The research method used is descriptive qualitative. Data collection techniques were carried out by means of observation, interviews, and literature study. The results of this study are in the form of software, namely a web-based Coronavirus patient registration system that can display detailed information on Coronavirus patient registration and Hospital location maps.
PREDIKSI PENJUALAN MENGGUNAKAN ALGORITMA NEURAL NETWORK: STUDI KASUS DI PT. BALARAJA FOOD MAKMUR ABADI Beby Tiara
Insan Pembangunan Sistem Informasi dan Komputer (IPSIKOM) Vol 6, No 1 (2018): JUNI
Publisher : Universitas Insan Pembangunan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v6i1.93

Abstract

ABSTRAKPrediksi atau peramalan penjualan mempunyai peranan yang penting dalam perusahaan, PT.BalarajaFood Makmur Abadi merupakan perusahaan yang bergerak di bidang penjualan makanan ringan(snack). Salah satu masalah yang dihadapi oleh perusahaan ini adalah sulitnya menentukan angkaprediksi penjualan untuk bulan berikutnya, sehingga bagian purchasing mengalami kesulitan dalammemperhitungkan ketersediaan bahan baku akibat terjadinya perbedaan antara forecast penjualan danaktual penjualan hal ini dapat mengecewakan konsumen karena barang yang akan dipesan tidak ada,sehingga permasalahan ini tentunya akan mengurangi keuntungan perusahaan.Untuk mengatasipermasalahan tersebut dengan menggunakan metode pengenalan pola yaitu neural network denganalgoritma backpropagation yang menggunakan data penjualan dari tahun 2013 sampai dengan 2015penghitungan yang telah dilakukan menggunakan neural network backpropagation maka dapatdihasilkan akurasi prediksi mencapai 90,8 % yang sebelumnya akurasi prediksi 81,75%,sehinggadapat meningkatkan akurasi sebesar 9.05 %.Kata Kunci: Neural Network,Backpropagation, Prediksi, Akurasi, MAPEABSTRACTPrediction or forecasting sales has an important role in the company, PT. Balaraja Food Holding is acompany engaged in the sale of snacks (snacks). One of the problems faced by these companies is thedifficulty of determining the sales forecast for the next month, so that the purchasing experiencedifficulties in taking into account the availability of raw materials due to differences between forecastsales and actual sales of this can be frustrating consumers for goods to be ordered nothing, so thatthis problem will certainly reduce the company''s profits. To overcome these problems by usingpattern recognition methods, namely neural network with backpropagation algorithm that uses salesdata from 2013 to 2015 the calculation was done using the back propagation neural network can begenerated prediction accuracy reached 90.8% previously forecast accuracy 81.75%, so as to improvethe accuracy of 9,05%.Keywords: Neural Network, Backpropagation, Prediction, Accuration, MAPE
Co-Authors . JUMRONI Diyah Kiki Widiyaningrum Hery Herlambang Jaka Suwita Mustar Aman Nuri Wiyono Riyanto Riyanto Rizki Hesananda Vanessa Stefanny Yunianto Agung Nugroho