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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.

Arjuna Subject : -

Articles 621 Documents

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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

Information systems Estimated demand for certified seed Jaja, Jaja; Agnes, Siska; Purwanti, Santi; Ayoub, Tawseef Shaikh
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

The availability of certified seeds is a very important strategy to maintain food security. When farmers plant their farms with certified seeds, it can increase the production yields grown by farmers. To answer the availability of sources according to the needs of farmers or consumers, it is necessary to design an information system for forecasting the demand for certified seeds, with a methodology Rational Unified Process (RUP) so that this method can be useful to identify the system that is running and can describe the system to be built. Meanwhile, to produce an estimate of the demand for certified seeds, a linear regression approach will be used which will be included in the design of the system. The design of this system will produce a function to assist producer farmers in estimating certified seed production, assisting the availability of certified seed information for consumers, and assisting the PSBTPH Installation in the Subang Region in carrying out evaluation and monitoring.

Data Warehouse Design With ETL Method (Extract, Transform, And Load) for Company Information Centre Fana, Wulan Stau; sovia, rini; Permana, Randi; Islam, Md Ataul
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Data Warehouse is a technology use to analyze, extract and evaluate data into information which produce knownledge in the form of analysis to provide an advice in decision making process. Designing a Data Warehouse using ETL (Extract, Transformation and Load) process serves as the collection of data from different data sources into a multitude of integrated data sets. By using snowflake scheme for the design of the data warehouse make data prepare well and ready for analyze on Data Warehouse. The result of Â this reseach is to applied Data Warehouse that use to support company decision making progress make easier and has a good decision since its come from Data Warehouse

Extractive Text Summarization of Student Essay Assignment Using Sentence Weight Features and Fuzzy C-Means Suwija Putra, I Made; Adiwinata, Yonatan; Singgih Putri, Desy Purnami; Sutramiani, Ni Putu
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.

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.

Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Ramadhanu, Agung; Defit, Sarjon; Kareem, Shahab Wahhab
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).

Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization Fernando, Hendra; Nugroho, Soewignjo Agus; Suryanita, Reni; Kikumoto, Mamoru
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Artificial neural networks (ANN) are now widely used and are becoming popular among researchers, especially in the geotechnical field. In general, data normalization is carried out to make ANN whose range is in accordance with the activation function used. Other studies have tried to create an ANN without normalizing the data and ANN is considered capable of making predictions. In this study, a comparison of ANN with and without data normalization was carried out in predicting SPT values based on CPT data and soil physical properties on cohesive soils. The input data used in this study are the value of tip resistance, sleeve resistance, effective soil overburden pressure, liquid limit, plastic limit and percentage of sand, silt and clay. The results showed that the ANN was able to make predictions effectively both on networks with and without data normalization. In this study, it was found that the ANN without data normalization showed a smaller error value than the ANN with data normalization. In the network model without data normalization, RMSE values were 3.024, MAE 1.822, R2 0.952 on the training data and RMSE 2.163, MAE 1.233 and R2 0.976 on the test data. Whereas in the ANN with data normalization, the RMSE values were 3.441, MAE 2.318, R2 0.936 in the training data and RMSE 2.785, MAE 2.085 and R2 0.963 in the test data. ANN with normalization provides a simpler architecture, which only requires 1 hidden layer compared to ANN without normalization which requires 2 hidden layer architecture.

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.

IBC Tracer: Web-Based Application for Online Tracing the Spread of Covid-19 in Indonesia Using BFS Algorithm Pratama, I Putu Agus Eka
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

In the case of handling the Covid-19 pandemic in Indonesia, there is a 3T (Testing, Tracing, Treatment) movement promoted by the government to reduce the impact of the spread and transmission of Covid-19. For tracing, there are currently no Information Technology-based applications or services that can assist the public in simulating the tracing of the spread of Covid-19 from one location to another location and providing disaster mitigation education to users through suggestions provided by the application after the tracking process. For this reason, this study was designed and implemented using a web-based Artificial Intelligence (Breadth-First Search) algorithm called Indonesia BFS Covid-19 (IBC). This research uses Design Science Research Methodology (DSRM) and tested using BlackBox Testing. From the testing results, it is concluded that the application can simulate the process of tracing the spread of Covid-19 in Indonesia well based on the starting point and destination, and users can gain an understanding of disaster mitigation education from the advice given by the post-tracing application, as part of 3T, to help decide the impact of the spread of Covid-19 in Indonesia.

Social Awareness and Safety Assistance of COVID-19 based on DLN face mask detection and AR Distancing Tenriawaru, Andi; Basori, Ahmad Hoirul; Mansur, Andi Besse Firdausiah; Al-Qurashi, Qusai; Al-Muhaimeed, Abdullah; Al-Hazmi, Majid
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v5i2.230

The outbreak of coronavirus disease (COVID-19) has forced major countries to apply strict policy toward society. People must wear a facemask and always keep their distance from each other's to avoid virus contamination. Government employ officers to monitor citizen and warn them if not wearing a face mask. The warning message also spread through SMS and social media to ensure people about safety and awareness. This paper aims to provide face mask detection using the Deep Learning Network(DLN) and warning system through video stream input from CCTV or images then analyzed. If people not wearing a mask are detected, they will alert them through the speaker and remind them about a penalty. AR distancing very useful to give position toward violator location based on the detected person in a certain area. The system is designed to work intelligently and automatically without human intervention. With the accuracy of 99% recognition, it's expected that the system can help the government to increase people awareness toward the safety of themselves and people around them.

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Heri Nurdiyanto

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Kota metro,

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