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All Journal Journal of Information Technology and Computer Science (JOINTECS) SENTIA 2017 Prosiding Seminar Nasional Sains dan Teknologi Terapan Systemic: Information System and Informatics Journal IJEEIT : International Journal of Electrical Engineering and Information Technology Jurnal Studi Guru dan Pembelajaran JATI (Jurnal Mahasiswa Teknik Informatika) MOTORIC e-NARODROID Jurnal Sistem Informasi dan Sistem Komputer Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Journal Computer Science and Informatic Systems : J-Cosys Internet of Things and Artificial Intelligence Journal Insand Comtech : Information Science and Computer Technology Journal
Natalia Damastuti
Department Of Information System, Narotama University, Surabaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Journal : Systemic: Information System and Informatics Journal

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Implementasi Perbandingan Algoritma Apriori Dan FP-Growth Untuk Mengetahui Pola Pembelian Konsumen Pada Produk Panel Di PT Surya Multi Perkasa Movinko Diego Armando Pratama Putra; Tresna Maulana Fahrudin; Natalia Damastuti
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.963

Abstract

Some companies have not used much consumer purchase transaction data as one of their sales strategies, this transaction data contains what items are often bought by consumers in one purchase transaction at a different time and structure. If the transaction data is analyzed and explored in more depth, the company will gain insight into consumer purchase patterns analysis and be profitable for the company. In this research, an analysis of consumer purchase transaction data was carried out using Apriori algorithm and FP-Growth, both of which are association rule method group that aims to determine consumer purchasing patterns. The data used in this study were obtained from panel product purchase transaction data at PT Surya Multi Perkasa Movinko. The transaction data consist of 23 types of product items and 492 transactions. The experimental results of this study showed that the best performance of Apriori algorithm with a support factor of 0.0054 and a confidence factor of 0.30 generating 12 association rules, while the best performance of FP-Growth algorithm with a supporting factor of 2 and a confidence factor of 0.7 generating 9 association rules.
Implementasi Model Regresi Logistik dalam Klasifikasi Kebutuhan Ruang ICU Terhadap Pasien Positif COVID-19 Baharudin Pratama; Natalia Damastuti
Systemic: Information System and Informatics Journal Vol. 7 No. 2 (2021): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v7i2.1300

Abstract

Coronavirus Disease 19 (COVID-19) is a type of disease caused by a virus called SARS-CoV-2. The origin of the infection came from Huanan Seafood Market, Wuhan City, Hubei Province, People's Republic of China. The virus attacks the lungs and is indicate to spread to other organs such as the heart, blood vessels, kidneys, intestines, and brain. SARS-CoV-2 virus infection can threaten the life safety of infected patients by attacking the respiratory system and can spread to other organs that trigger comorbidities. The condition of COVID-19 patients with comorbidities is a consideration for ICU admission. Statistics state that 1 in 5 COVID-19 patients undergo treatment in a hospital, and 1 in 10 of them require treatment in the ICU (Intensive Care Unit). In this study, the classification of ICU room needs on COVID-19 patients based on comorbidities and certain conditions using a logistic regression model. Logistic regression implemented with consideration of the data and research variables having a categorical data scale. The data is divide into two, training data and testing data with a ratio of 80%:20%. The purpose of this research is to get the accuracy of the classification. The results showed that the level of accuracy reached 87.29%.
Co-Authors Agung Widodo Agung Widodo andini dwi arumsari Arif Abriyanto Awalludiyah Ambarwati Baharudin Pratama Cahyo Darujati Diego Armando Pratama Putra Febriansyah, Muhammad Adam Lasarus Setyo Pamungkas Latipah Latipah Latipah, Latipah Lukman Junaedi Lukman Junaedi Lukman Junaedi, Lukman Muchamad Arif Muchamad Arif, Muchamad Prakoso, Bagus Ridho Pratama Ryan Harnanda Rahmat Hidayat Rendi Kusuma, Hafiz Ridzki Okta Pratikto Ronny Durrotun Nasihien, Ronny Durrotun Setiawan, Eman Sulikan Sulikan Tresna Maulana Fahrudin Tresna Maulana Fahrudin virdaus, varia virdania Yoesril Ihza Mahendra Zakki, Achmad