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JAIA - Journal of Artificial Intelligence and Applications
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Amik Riau
ISSN : -     EISSN : 27467821     DOI : -
Core Subject : Science,
Computer Science & IT
This journal publishes research results in the form of research articles, literature studies and articles in the form of concepts and policies in the field of computers in general: Machine Learning and Deep Learrning Clustering and Classification Prediction Document Mining and Text Mining Sentiment Analysis Spatial Data Mining Multi-Agent Systems Biologically Inspired Intelligence Intelligent control systems Complex Systems and Applications Computational Intelligence Soft Computing Image and Speech Signal Processing Computer Vision Pattern Recognition Numerical Computational Knowledge Based Systems and Knowledge Networks Knowledge discovery and Database Machine Learning Neural Networks and Applications Optimization and Decision Making Rough sets and Granular Computing Self-Organizing Systems Fuzzy Logic Decision Support and Expert System Business Intelligence Intelligence System Hybrid Algorithm Social Intelligence Social Media Analytic Stochastic Systems Support Vector Statistic and Matematic Modeling Web and mobile Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
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Search results for , issue "Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications" : 6 Documents clear
IOS-Based Mobile Service Ordering Application Using Wireframe and Figma Yoyon Efendi; Ilham Perdana; Muhammad Raihan; Rometdo Muzawi; Nurul Utami; Tashid
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.331 KB) | DOI: 10.33372/jaia.v2i2.817

Abstract

Technological developments are increasingly growing rapidly so that everyone cannot avoid these technological advances. The technology in question covers all aspects of life, therefore here we create an application that is expected to help the community in finding tenants for IOS-based services, with the aim of helping people who are difficult in terms of finding service providers who want to be hired to help with their daily homework day.
Fuzzy Floor Dust Cleaning Robot Prototype Based On Arduino Sudarso; Mhd Arief Hasan; Muhamad Sadar
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.839

Abstract

The design of a Floor Cleaning Robot Based on Arduino Uno R3 aims to make it easier for humans to clean the dust on the floor efficiently. This robot consists of several series of components including Arduino Uno r3: Arduino Uno r3, sensor ultrasonic, motor servo, This robot consists of several components including Arduino Uno R3, ultrasonic sensor, servo motor, motor shield driver, dc motor, and vacuum blower motor. The entire system synergizes in the process of cleaning the dust on the floor and all of them are connected to a power source in the form of a dc battery, which supplies a voltage of 7 volts for the Arduino circuit and 12 volts for the vacuum blower. The working principle of this robot starts when the ultrasonic sensor can measure and distinguish the closest distance between the robot and an obstacle, at the same time the servo motor will move up to 180° to assist the ultrasonic sensor in detecting obstacles, both the front, right, and left sides of the robot. This Floor Cleaning Robot is programmed by adapting fuzzy logic artificial intelligence, the fuzzy rules used in this robot aim to control the speed of the robot based on the obstacle distance detected by the ultrasonic sensor. Fuzzy logic executes the data and continues the command to drive the motor so that the robot can work efficiently cleaning dust on the floor by minimizing the occurrence of collisions against obstacles. Dust suction carried out by the vacuum blower motor is not included in the Arduino circuit, because the vacuum blower motor requires an input power of 12 volts. It is not possible to unite it to the Arduino circuit because the power it has is only 7 volts, but these two separate circuits can still synergize well in the process of cleaning the dust on the floor. The results of observations and experiments show that the fuzzy logic embedded in the Arduino as the brain of the dust-cleaning robot on the floor works quite well.
Detection of Palm Fruit Maturity Using Convolutional Neural Network Method Ade Kurniawan kurniawan; Andi Sunyoto; Alva Hendi Muhammad
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.859

Abstract

Palm oil has an important role as a source of foreign exchange in the economy in Indonesia. Oil palm is one of the vegetable oil-producing plants that has the highest economic value compared to other crops such as soybeans, olives, coconuts or sunflowers. Palm oil quality is also influenced by water content, dirt content, free fatty acid content and the level of maturity of the palm fruit. Maturity of palm fruit is a very important factor in determining the quality of crude oil produced by palm fruit. In determining the maturity of oil palm, sorting is necessary to get quality palm fruit with the appropriate level of maturity. The use of image processing technology (ImageProcessing) can facilitate the process of analyzing objects. Meanwhile, the implementation of deep learning using the Convolutional Neural Network method can help identify the maturity level of oil palm fruit with a high level of accuracy. The results showed a very good effectiveness with an accuracy reaching 99% and a precision level reaching 99.8%.
Expert System to Detect the Level of Parental Stress in Online Learning by Using Forward Chaining Method Susi Erlinda M. Kom; Rika Desmalita; Liya Astarilla Dede Warman
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.879

Abstract

This research was aimed to make an expert system application to detect and identify the parental stress level in online learning by using the Forward Chaining method. The system was designed and implemented based on Desktop by using basic programming language. The data were collected through questionnaire consisted of 30 questions. The final score would determine the stress level of the respondents. The respondents were chosen by using random sampling technique. XAMPP data with the results obtained from 150 respondents who have educational backgrounds; elementary school (4 respondents), junior high school (6 respondents), high school (120 respondents), associate degree (11 respondents), bachelor degree (9 respondents).There were 123 respondents do not work, and 27 respondents were working parents. The findings showed that from 150 respondents there were 22% or 33 respondents were at normal level, 6% or 9 respondents were at mild level of stress, 8% or 12 respondents were at moderate level of stress, and 64% or 96 respondents were at severe level of stress. It was concluded that this application can be used to detect the parental stress level in online learning. It was also expected through the use of this application can help parents to detect their stress level earlier and find solution of it in order to avoid the negative impact of their problems.
Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method Muhammad Oase Ansharullah; Wirta Agustin; Lusiana; Junadhi; Susi Erlinda; Fransiskus Zoromi
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.888

Abstract

Marketplace is an electronic product marketing platform that brings together many sellers and buyers to transact with each other. The large variety of products sold on Shopee is one of the reasons this application is in great demand by all walks of life. However, the weakness of the large variety of products sold in a marketplace causes buyers who have no potential to buy these products. To overcome this problem, it is necessary to do a classification to determine which products are most in demand by customers. Product categories consist of: Clothing, Beauty Products, Daily Goods, Electronics, and Accessories. The classification method used is Naïve Bayes and the software used is WEKA. The next data collection is done by distributing questionnaires to the existing customers on social media namely, Whatsapp and Instagram, the distribution of the questionnaire is conducted through Google form. There are 90 questionnaires that will be distributed in this study. Some of the indicators asked in the questionnaire namely, do you like shopping online? And what marketplaces are commonly used. These results will be the training data. Interest categories are divided into 4 categories, namely: Very interested, Interested, Not interested, Very not interested. The results obtained in this study are clothing products (72 respondents) are products that are in great demand, daily goods products (7 respondents) are products of interest, beauty and electronic products (5 respondents) are products that are not in demand, and accessories (1 respondents ) is a product that is not very attractive to customers on the Shopee marketplace
Detection Of Malaria Parasites In Human Blood Cells Using Convolutional Neural Network Lusiana Efrizoni; Rais Amin; Ahmad Rizali
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.947

Abstract

Malaria is a blood disease caused by the Plasmodium parasite which is transmitted by the bite of the female Anopheles mosquito. The diagnosis of malaria is carried out by a microscopist through examination of human blood cells. Their level of accuracy depends on the quality of the tool, expertise in classifying and counting infected and uninfected parasite cells. The disadvantages of examining this way include the difficulty in making a diagnosis on a large scale and the poor quality of the results. The dataset used in model evaluation is a dataset developed by LHNVBC which contains 27,558 cell image data. The malaria dataset will be processed through data science processing using a Convolutional Neural Network with the ResNet architecture. The model will conduct training on the dataset and then the model will be able to recognize malaria parasites in human blood cells. The model will be trained by optimizing multinomial logistic regression using Stochastic Gradient Descent (SGD) and Nesterov momentum values. The results of training data validation accuracy from model training with 50 epochs were obtained at 96.23% and 97% after being tested on data testing.

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