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All Journal Speed - Sentra Penelitian Engineering dan Edukasi Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa Computer Based Information System Journal Jurnal Informatika Global JISKa (Jurnal Informatika Sunan Kalijaga) ICIT (Innovative Creative and Information Technology) Journal Al-Ulum: Jurnal Pendidikan Islam IICS Computer Science (CO-SCIENCE) Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Al-Ulum: Jurnal Pendidikan Islam Jurnal Insan Nuris Journal of Education and Islamic Studies SATIN - Sains dan Teknologi Informasi AMMA : Jurnal Pengabdian Masyarakat
Doni Andriansyah
STMIK Nusa Mandiri
Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Nursing Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Expert System For Diagnose Covid19 Using Certainty Factor Method Lukman Nulhakim; Doni Andriansyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5069

Abstract

AbstractAt the end of 2019 the world was shocked by the emergence of a new type of virus from the Corona family, namely the Novel Coronavirus (2019-nCoV) which had never been previously identified in humans, later known as Coronavirus disease 2019 (Covid-19). Early symptoms in people with Covid-19 include fever, cough and shortness of breath, similar to flu and cough symptoms in general, making it difficult to detect early. The certainty factor method can measure a certainty and uncertain thing. Research with certainty factor methods has been carried out to diagnose a disease based on the symptoms experienced. Types of diseases are focused only on types of diseases with almost the same symptoms, namely Upper Respiratory Tract Infection, Pneumonia, and Covid-19. The purpose of this study is to build an expert system application that can be accessed online to detect early symptoms experienced by sufferers. Based on the results and discussion, the expert system application can run well and the results of manual CF calculations are the same as the results of CF calculations on the system.
Optimization of Support Vector Machine and XGBoost Methods Using Feature Selection to Improve Classification Performance Doni - Andriansyah; Eka Wulansari Fridayanthie
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8373

Abstract

Breast cancer still ranks first in the number of cancers in Indonesia and is one of the contributors to the death rate caused by cancer. Generally occurs in women, but does not rule out it can occur in men. Globocan data in 2020 shows the number of new cancer cases in Indonesia reached 65,858 or 16.6% of the total 396,914 new cancer cases, with the number of deaths reaching 22,430 cases. Early detection can allow patients to get the right therapy and increase their chances of survival. The purpose of this study is to implement a machine learning algorithm to detect breast cancer in women, the algorithms that will be used are Support Vector Machine (SVM) and XBGoost by implementing feature selection to obtain better accuracy. The classification results of the two algorithms will be compared to find out which algorithm has the best performance. The dataset used is from the SEER NCI program in November 2017 involving 4024 patients. The research shows that of the 16 attributes contained in the dataset, there are 3 attributes (features) that have a significant effect on the classification results, namely 6th stage, reginol node positive, and tumor size. XGBoost with feature selection has a better performance of 91.4% compared to SVM which is only 89.8%.
Co-Authors - STMIK Nusa Mandiri, Ahmad Bayzuri Anwar Khamidi Ardian Dwi Praba Eka Wulansari Fridayanthie, Eka Wulansari Elly Mufida Elly Mufida, Elly Eva Rahmawati Fatmawati . Firda Devia Putri Herlambang Brawijaya Hylenarti Hertyana Irma Soraya Jenie Sundari Lukman Nulhakim Lukman Nulhakim M. Indra Setiawan mufida, ely Nandang Iriadi Nulhakim, Lukman Syahriani Veti Apriana, Veti