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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 4 Documents
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Search results for , issue "Vol 5, No 2 (2020): Journal of Applied Intelligent System" : 4 Documents clear
Android Base Rapid Application Development for Learning Yanbu'a Teguh Tamrin; Syamsul Ma’arif
Journal of Applied Intelligent System Vol 5, No 2 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i2.4375

Abstract

Learning to read the Al Quran is a very important basic knowledge, especially for early childhood, because it serves as a foundation to increase love for the Qur'an and an increase in the sense of faith in Allah Subhanahu Wata'ala. The current learning model is still very conventional. The method of learning the Yanbu'a book in general still uses the rote method by reading the book and listening to it from the teacher. The impact of learning like that causes the child to be less enthusiastic about learning so it is easy to bo-san. Seeing these problems, it is necessary to make mobile-based applications that can make it easier and fun for children to learn the Yanbu'a book. In this study, we will discuss the development of the Yanbu'a learning method with the Rapid Application Development Method on Android which was built using the Construct 2 application. Students are increasingly facilitated in learning and interest in learning is increasing. 
Particle Swarm Optimization For Improved Accuracy of Disease Diagnosis Suamanda Ika Novichasari; Iwan Setiawan Wibisono
Journal of Applied Intelligent System Vol 5, No 2 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i2.4242

Abstract

The increasing number of patients suffering from various diseases and the impact and high cost of medical treatment for the community has made the government or health communities seek solutions for prevention from an early age. This valuable information can be found using artificial intelligence and data mining. Most diseases are dangerous; if detected early and adequate diagnosis and treatment are available, there will be a chance for a cure. The main objective of this study was to use Particle Swarm Optimization (PSO) to improve the accuracy of several classification methods, namely Naive Bayes, C4.5, Support Vector Machine (SVM), and Neural Network (NN) to detect heart disease, hepatitis, kidney, and breast cancer. The method used in this research is the CRISP-DM model, with 5 stages. The data used were four disease data from UCI Machine Learning. The result of this research is that PSO can improve the accuracy of Naive Bayes, C4.5, SVM, and NN.
IMPLEMENTATION OF DIVIDE AND CONQUER IN THE HANOI TOWER GAME Bonifacius Vicky Indriyono; Zudha Pratama
Journal of Applied Intelligent System Vol 5, No 2 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i2.4291

Abstract

Artificial Intelligence (AI) is a technology in the field of computer science that simulates human intelligence into computers to solve various problems and jobs as well as humans do. Games / games are an implementation of the field of computer science which also embraces the concept of AI. In the midst of the rampant types of games available, the author chose the Hanoi Tower which is a mathematical game / puzzle that requires logic. Players are challenged to complete in a short time with a certain number of discs. The benefit of this game is that it can train how to think with certain patterns so as to improve the memory of players. To make it easier to solve it, the Divide and Conquer Algorithm can be used which can solve problems in the Tower of Hanoi game by breaking them down into sub-problems which will later be able to help speed up finding solutions. From the results of testing the application of the Divide and Conquer Algorithm in the hanoi tower game application by solving the disk arrangement problem. Players can finish the game in a large number of plates in a short time. Keywords – Artificial Intelligence, Divide and Conquer Algorithm, Games, Tower of Hanoi.
Whale Optimization Algorithm Bat Chaotic Map Multi Frekuensi for Finding Optimum Value Nur Wahyu Hidayat; . Purwanto; Fikri Budiman
Journal of Applied Intelligent System Vol 5, No 2 (2020): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v5i2.4432

Abstract

Optimization is one of the most interesting things in life. Metaheuristic is a method of optimization that tries to balance randomization and local search. Whale Optimization Algorithm (WOA) is a metaheuristic method that is inspired by the hunting behavior of humpback whales. WOA is very competitive compared to other metaheuristic algorithms, but WOA is easily trapped in a local optimum due to the use of encircling mechanism in its search space resulting in low performance. In this research, the WOA algorithm is combined with the BAT chaotic map multi-frequency (BCM) algorithm. This method is done by inserting the BCM algorithm in the WOA search phase. The experiment was carried out with 23 benchmarks test functions which were run 30 times continuously with the help of Matlab R2012a. The experimental results show that the WOABCM algorithm is able to outperform the WOA and WOABAT algorithms in most of the benchmark test functions. The increase of performance in the average of optimum value of WOABCM when compared to WOA is 2.27x10 ^ 3.

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