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International Journal of Artificial Intelligence Research

IJAIR
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Published by STMIK Dharma Wacana

ISSN : - EISSN : 25797298 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.

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Articles 8 Documents

Search results for , issue "Vol 4, No 2 (2020): December 2020" : 8 Documents clear

Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data Wisesty, Untari Novia; Sthevanie, Febryanti; Rismala, Rita
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression. Â In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.

Determination of Overall Equipment Efectiveness Superflex Machine Using Fuzzy Approach Santosa, Sesar Husen; Irawan, Suhendi; Ardani, Ilham
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

This study aimed to present a Fuzzy logic approach in determining the value of OEE Superflex machine for producing nuggets. The effectiveness value of Superflex machine in producing nugget raw materials was determined by calculating the Availability, Performance and Quality Yield values. Fuzzy approach in determining the value of OEE can be used because this approach is able to describe the value of the effectiveness of thr machines based on the condition of the company's actual capacities. The Fuzzy OEE approach uses the Trapezoidal Membership Association because the maximum value of the membership degree has more than one value in each parameter. The Fuzzy OEE value shows that Superflex machine had an OEE value with bad parameters so that the company has to improve its machine performance

Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification Hayaty, Mardhiya; Muthmainah, Siti; Ghufran, Syed Muhammad
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

High accuracy value is one of the parameters of the success of classification in predicting classes. The higher the value, the more correct the class prediction.Â One way to improve accuracy is dataset has a balanced class composition. It is complicated to ensure the dataset has a stable class, especially in rare cases. This study used a blood donor dataset; the classification process predicts donors are feasible and not feasible; in this case, the reward ratio is quite high. This work aims to increase the number of minority class data randomly and synthetically so that the amount of data in both classes is balanced. The application of SOS and ROS succeeded in increasing the accuracy of inappropriate class recognition from 12% to 100% in the KNN algorithm. In contrast, the naÃ¯ve Bayes algorithm did not experience an increase before and after the balancing process, which was 89%.Â

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction Sari, Yuita Arum; Hapsani, Anggi Gustiningsih; Adinugroho, Sigit; Hakim, Lukman; Mutrofin, Siti
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor Â (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting Amir Mahmud Husein; Jefri Poltak Hutabarat; Jeckson Edition Sitorus; Tonazisokhi Giawa; Mawaddah Harahap
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : STMIK Dharma Wacana

The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of deaths

Dependable flow modeling in upper basin Citarum using multilayer perceptron backpropagation Ika Sari Damayanthi Sebayang; Muhammad Fahmia
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : STMIK Dharma Wacana

To determine the amount of dependable flow, a hydrological approach is needed where changes in rainfall become runoff. This diversification is a very complex hydrological phenomenon. Where this is a nonlinear process, with time changing and distributed separately. To approach this phenomenon, an analysis of the hydrological system has been developed using a model which is a simplification of the actual natural variables. The model is formed by a set of mathematical equations that reflect the behavior of parameters in hydrology. Modeling in this case uses artificial neural networks, multilayer perceptron combined with the backpropagation method is used to study the rainfall-runoff relationship and verify the model statistically based on the mean square error (MSE), Nash-Sutcliffe Efficiency (NSE) and correlation coefficient value (R2). Of the three models formed, model 3 provides optimum results with correlation levels using NSE per month as follows, in Cikapundung Sub-Basin NSE = 0,990703, R2 = 0,995008, and MSE = 0,00014443, while in Citarik Sub-Basin NSE = 0.9500, R2 = 0.97592, and MSE = 0.0010804 . From these results it can be seen that ANN has a fairly good ability to replicate random discharge fluctuations in the form of artificial models that have almost the same fluctuations and can also be applied in rainfall runoff modelization even though the results of the test results are not very accurate because there are still irregularities

A Novel Method for L Band SAR Image Segmentation Based on Pulse Coupled Neural Network Harwikarya, Harwikarya; Rudiarto, Sabar; Sebastian, Glorin
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Pulse Coupled Neural Network (PCNN) is claimed as a third generation neural network. PCNN has wide purpose in image processingÂ such as segmentation, feature extraction, sharpening etc. Â Not like another neural network architecture, PCNN do not need training. The only weaknes pointÂ of PCNN is parameter tune due toÂ seven parameters in its five equations. In this research we proposed a novel method for segmentation based on modified PCNN. Â In order to evaluate the proposed method, we processed L Band MultipolarisationÂ Synthetic Apperture Radar Image. The Results showed all area extracted both by using PCNN and ICM-PCNN from the SAR image are match to the groundtruth. There fore the proposed method is work properly.Copyright Â© 2017 Â International Journal of Â Artificial Intelegence Research.All rights reserved.

Texton Based Segmentation for Road Defect Detection from Aerial Imagery Prahara, Adhi; Akbar, Son Ali; Azhari, Ahmad
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.

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Contact Name
Heri Nurdiyanto

Contact Email
Heri Nurdiyanto

Phone
-

Journal Mail Official
internationaljournalair@gmail.com

Editorial Address
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Location
Kota metro,

Lampung

INDONESIA