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International Journal of Artificial Intelligence
Published by Lamintang Education & Training Centre
ISSN : 24077275     EISSN : 26863251     DOI : https://doi.org/10.36079/lamintang.ijai
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
The aim is to publish high-quality articles dedicated to Artificial Intelligence. IJAI published in biannual, and in Indonesian, Malay and English.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 10 No 1: June 2023" : 5 Documents clear
Selecting Favourite Majors at Sari Mulia University Using SAW Method Rusidah; Risdianti; Susanto, Jessika Kindly
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.482

Abstract

A decision support system, also known as a decision support system (DSS), is an interactive information system that offers data, models, and information. DSS is used as a decision aid in semi-structured and unstructured situations where there is no clear decision-making procedure. Determining the preferred major is one of the challenges in universities. The purpose of determining the most popular major is to improve the quality and services provided to students in each department, which is a crucial objective for universities. Currently, Universitas Sari Mulia determines the most popular majors based on qualitative data, which makes the determination of the most popular majors themselves inaccurate; therefore, a method capable of managing data on the selection of the most popular majors is necessary. In this study, the Simple Additive Weighting (SAW) technique will be utilized. This method is used to compare each criterion with one another in order to determine the most popular majors at Sari Mulia University and to evaluate each department.
Dietry Monitoring System Using Decision Tree to Control Human Obesity Mat Baseri, Mohamad Faiz; Saad, Ahmad Fadli
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.485

Abstract

Nowadays, obesity is one of the dangerous diseases in the world. Lack of dietary monitoring system will make it difficult for people with obesity to reduce their weight problems. The main objective of this project is to develop a dietary monitoring system that can be used by everybody especially for obesity’s people. The method used in this study is to identify the strength and weaknesses of the existing system which involves reviewing some articles, journals, magazines, and books. The survey was conducted which involves 10 people answering the questionnaire. Respondent answered was used to improve the quality of the system. Next method is utilizing a waterfall model as a method to develop a dietary monitoring system. The system applied the decision tree technique to a classified food calorie. Therefore, the decision tree technique was used in developing this system. The last method used in this study involves the participation of three respondents to evaluate the usability of the system. Respondents need to answer whether they satisfied with the system or they can give suggestions for future improvement. The results of this study show that obesity is a public health issue that is rapidly increasing and must be addressed seriously by developing the system. Significant by developed this system such as helping obesity’s people to diet by giving them the guideline. In conclusion, this system will help people to diet using the decision tree technique for classifying food calories.
Investigation and Development of a Data Acquisition System for Blood Bank Azman, Nurfidhah; Subramaniam, Siva Kumar; Esro, Mazran
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.488

Abstract

Blood is a vital fluid where required for saving human’s life. Blood is stored in a blood bank which is a bank of blood components, gathered as a result of blood donations that are responsible for collecting, storing and preserved for the use of medical purpose. Investigation of the existing blood collection and tracking system is essential to efficiently manage, control and monitor on all aspect of a blood bank. A comprehensive data acquisition system from collection location to a cloud-based system enables a paperless system with minimum human intervention to oversee the entire collection to dispatch process in a blood bank. A research has been made that most blood banks practicing stand-alone which may contribute to wastage of donated blood. For that matter, this collected data system allows connectivity between the blood banks to effectively conduct and systematically manage their daily activities within one integrated system. This application helps blood donation center receives the registered donated blood from any hospitals easily as it records the donated blood information in cloud immediately.
Expert System for Diagnosis Coronavirus Disease Elmie Dansy, Sholestica; Yani, Achmad; Manaf, Abdi; Abdulbaqi, Azmi Shawkat; Iksan, Nur
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.537

Abstract

The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this research, the researcher will held an interview with the doctors to collect data and information about those diseases’ symptoms and also search for the related articles to make sure this research going successfully. The method that will be used in this research is Certainty Factor. To conclude, this system will be useful to the healthcare department as it will give earlier detection when the patient are positively exposed to the disease that is known by Corona.
Career Finder System using Rule-Based Filtering for University Student Candidates Amir Hamzah, Fikri Nur Izzudin; Saad, Ahmad Fadli; Panessai, Ismail Yusuf
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.539

Abstract

As a current reality, students are frequently questioned about a suitable career path for the future, but they are unaware of the jobs offered by current industries. Moreover, students seeking university admission frequently encounter difficulties selecting courses and educational programs, and they are confronted with a variety of available courses. This research aims to make a mobile application for students to obtain employment career options appropriate to their educational qualifications because student is often asked about a suitable career for their future but have no idea about the available career path that appropriate. The methodology that implements in this research is Mobile Application Development Life Cycle (MADLC) that have four phases which is identification, design, development, and testing. The Visual Studio Code with Flutter plugin is used to develop the mobile application and its function. Firebase is used to get the database to store all the data and works as backend function of the application. The finished system was tested accordingly based on the functionality that listed all available function of the system. The system considers students' educational qualifications and academic achievements to provide personalized recommendations. This system can assist students in making career decisions and pursuing the right career path, saving them time, and reducing the risk of making wrong choices. This research indicates understanding the importance of career decision-making for students before continuing their university studies. In conclusion, this research seeks to enhance the ability of students to make decision of the available career path provided through recommendation system.

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