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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jutsi: Jurnal Teknologi dan Sistem Informasi Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat JBSI: Jurnal Bahasa dan Sastra Indonesia
Muthia Dewi
Prodi Sistem Informasi, STMIK Royal Kisaran, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
THE EARLY DETECTION OF RESPIRATORY SYSTEM DISEASES BY USING THE CERTAINTY FACTOR METHOD Siti Nuraisyah Suci Dewi Maharani Sianipar; Jeperson Hutahaean; Muthia Dewi
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.249

Abstract

Respiration or commonly referred to as breathing is the process of inhaling free air containing O2 (oxygen) and expelling air containing CO2 (carbon dioxide) as the rest of the oxidation out of the body. In detecting diseases of the respiratory system, it takes the role of a doctor as an expert in the field of health to detect it. To consult a doctor, one must come to the hospital and undergo a series of procedures, so that it takes time to queue and is not effective if the patient has to get treatment quickly. To overcome this problem, an expert system is needed that can be accessed easily, provides accurate, fast and accurate information on detection results and provides education on early treatment of disease detection results. The method used to detect respiratory system diseases is the certaity factor method. From the calculations that have been inputted by the patient, the results obtained are 93% confidence in Pneumonia. The application of an expert system with the Certaint Factor method in early detection of web-based respiratory system diseases based on the patient's symptom history provides convenience for patients and provides education about respiratory system diseases and how to treat them.
IMPLEMENTATION OF WEB-BASED NAIVE BAYES ALGORITHM FOR DETERMINING DEPARTMENTS AT SMK 10 MUHAMMADIYAH KISARAN Nurlaili Sabila; Herman Saputra; Muthia Dewi
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.605

Abstract

Determination of majors is very important for the convenience of prospective students in the process and continuation of education so that they do not experience difficulties in the teaching and learning process in the future. SMK 10 Muhammadiyah Kisaran is one of the private vocational schools in Asahan that provides 3 majors including Audio Video Engineering (TAV), Computer and Network Engineering (TKJ), and Motorcycle Engineering and Business (TBSM). SMK 10 Muhammadiyah Kisaran does not yet have a special system for selecting majors so that prospective students are welcome to choose majors according to their own wishes, not a few students find it difficult because the students themselves do not understand their abilities.so that it’s not uncommon for students to choose majors in a random way or follow their friends' choices. Therefore we need a system that can help prospective students in selecting majors that match their interests and talents and reduce mistakes in choosing majors. The technique used for the classification data mining model in this study is the Naïve Bayes Algorithm. The dataset that will be used as training data and test data is data for new students for the 2021/2022 school year, to be precise, for class X SMK 10 Muhammadiyah Kisaran obtained from the results of documentation and questionnaires. The criteria used were school origin, gender, interests, major, influence of friends, parental suggestions, math scores, English grades, and science grades. The results of the classification modeling with the Naïve Bayes Algorithm produce an accuracy value of 89%.
Co-Authors Akmal Akmal Anita Purba Cecep Maulana Dailami Dailami - Dailami Dailami Dailami Dailami Dewi Anggraeni Dewi Maharani Herman Saputra Indah Risfia Jeperson Hutahaean Krisma Yogi Nopiana Muhammad Al Amri Nurlaili Sabila Nurwati Nurwati Parwan Harahap Rizaldi, Rizaldi Sari Bunga Tiara Manurung Siti Nuraisyah Suci Dewi Maharani Sianipar Sri Rezeki Maulina Azmi Sri Rezki Maulina Azmi Ulfah Syuhada Nasution Ulfah Syuhada Nasution