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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Ethos Proceeding of the Electrical Engineering Computer Science and Informatics ELECTRANS Proceeding of the Electrical Engineering Computer Science and Informatics Sains Manajemen:Jurnal Manajemen UNSERA Wikrama Parahita : Jurnal Pengabdian Masyarakat ILKOM Jurnal Ilmiah JSiI (Jurnal Sistem Informasi) Jurnal Pengembangan Riset dan Observasi Teknik Informatika TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Bantenese Jurnal Pengabdian Masyarakat Journal of Machine Learning and Soft Computing Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Keuangan dan Akuntansi Terapan (KUAT)
Sumiati, Sumiati
Universitas Serang Raya
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule Sumiati Sumiati; Hoga Saragih; Titik Abdul Rahman; Agung Triayudi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp43-50

Abstract

Limited public health services in remote areas, where the lack of transportation infrastructure, facilities, communication facilities and minimal medical personnel, especially for areas with underdeveloped, foremost, and regular (3T) status. The limitation of medical personnel is one of the factors for the high mortality rate of heart disease. On the other hand, the development of information technology, especially in the field of computing, is very fast in the era of the industrial revolution 4.0, but not yet used optimally, especially in the health sector. This study aims to develop a system or software that can replace a doctor for the process of identifying heart defects based on an expert system. Expert system developed with the certainty factor with multiple rule approach. System testing is carried out from the results of the system validity with experts, so that the system test results produce a certainty factor value for a normal heart of 0.95 and an accuracy level of 95%, while the certainty factor (CF) value for an abnormal heart is 0.99 and produces an accuracy rate of 99%.
Measure the effectiveness of information systems with the naïve bayes classifier method Agung Triayudi; Sumiati Sumiati; Saleh Dwiyatno; Dentik Karyaningsih; Susilawati Susilawati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp414-420

Abstract

Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.
CLG clustering for dropout prediction using log-data clustering method Agung Triayudi; Wahyu Oktri Widyarto; Lia Kamelia; Iksal Iksal; Sumiati Sumiati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp764-770

Abstract

Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those student based on this log-data, this study being more focused on detecting them who experienced a difficulty or unable to take programming classes. We propose CLG clustering methods that can predict a risk of being dropped out from school using cluster data for outlier detection.
Co-Authors Agung Rahmad Fadjar Agung Triayudi Apriliana Dwijayanti Arief Nurrohman Dentik Karyaningsih Desmira Desmira Dwiyatno, Saleh Febriasari, Arifina Haris Triono Sigit Hoga Saragih Iksal Iksal Ilham Hidayat lia kamelia Mada, Ratu Dea Media Sucahya Melisa Theresia Mohamad Asro Laili Muhar Dizani Nurhadiyan, Thoha Prakosa Trinorosimo Raden Muhammad Firzatullah Ratu Erlina Gentari Retno Wulandari Rosalina, Vidila Shodik Nuryadin Shohifah Annur Sigit Sasongko Suherman Suherman Sulasno Sulasno Suryaman Suryaman Susilawati . Tb. Ai Munandar Thoha Nurhadiyan Titik Abdul Rahman Wahyu Oktri Widyarto Wyke Kusmasari, Wyke