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All Journal Jurnal Teknik Komputer AMIK BSI Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Paradigma
Yonata Laia
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Jurnal Tekinkom (Teknik Informasi dan Komputer)

 1 
PENERAPAN DATA MINING CLASSIFICATION UNTUK DATA PASIEN COVID- 19 MENGGUNAKAN METODE NAÏVE BAYES Tessalonika Siahaan; Yonata Laia; Manusun Silitonga; Friska Claudia Pasaribu
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.879

Abstract

Covid-19 is an infectious disease caused by a new coronavirus discovered in 2019, hereafter Sars-Cov 2 (Severe Acute). Coronavirus Respiratory Syndrome 2). This virus is very small in size (120-Knowledge is participants' understanding of a given topic. Knowledge is the ability to receive, store and use information, influenced by experience and skills. This research creates a system that can help anyone who wants to know what causes are behind the increasing spread of bacteria in the form of viruses. Therefore, it is necessary to find out what factors have caused the increase in the number of people infected with this deadly virus. Using the Naive Bayes method, researchers identified the factors causing the increase in the number of medical records for Covid-19 patients. The results obtained are based on attributes that have values, so the Bayesian value is 19.8714.
ANALISIS DATA MINING UNTUK PENGARUH KUALITAS PELAYANAN, PENGIKLANAN, DAN HARGA TERHADAP KEPUTUSAN KONSUMEN DALAM MEMILIH PENJUAL ONLINE Elfrin Hulu; Yonata Laia; Naomita Sihombing; Wandry Sitorus; Yuliani C. Simanjorang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.878

Abstract

Almost everyone uses this buying option when selling online, for example, home appliances, fabrics, and many other products sold online. Some consumers complain that the service is still ineffective, and scams pretending to sell products sold through the site are common. As this study shows, many of the jobs humans do today can be done by computer systems. This study explores how to identify optimal online sales sites using the K-NN method. The aim is to prevent consumers from making mistakes when shopping online. It is hoped that this system will help visitors find simple and useful websites. All human activity must be innovative. This research has solved this problem and enabled the construction of an easy-to-use system. Based on calculations of load speed 2, page structure 3, interesting titles and content 4, short and recognizable links 5, and the results are useful.
Co-Authors Abraham Manalu Alferedo Tanzil Asima Putri Banjarnahor, Jepri Dewi Roito Eika Rizkiadina Elfrin Hulu Eli Suriadi Simatupang Erick Erick Frankie Frankie Friska Claudia Pasaribu Imanuel Purba Juli Rostianita Sitopu M Afifuddin Alwy Nasution Manusun Silitonga Marlince NK Nababan Meliany Sondang Sumantri Naomi Br Hutagalung Naomita Sihombing Randy Aldany Barasa Tessalonika Siahaan Tomi Darmawan Bangun Vickash Prasadh Vincent Leonardy Wandry Sitorus Yuliani C. Simanjorang