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International Journal of Artificial Intelligence Research
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 5 Documents
 1 
Search results for , issue "Vol 3, No 2 (2019): December 2019" : 5 Documents clear
An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Setyadi, Hario Jati; Wong, Kelvin; Alfred, Rayner
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1806.11 KB) | DOI: 10.29099/ijair.v3i2.112

Abstract

This article aims to predict the inflation rate in Samarinda, East Kalimantan by implementing an intelligent algorithm, Backpropagation Neural Network (BPNN). The inflation rate data was obtained from the Provincial Statistics Bureau of Samarinda https://samarindakota.bps.go.id/ for the period January 2012 to January 2017. The method used to measure accuracy algorithm prediction was the mean square error (MSE). Based on the experiment results, the BPNN method with architectural parameters of 5-5-5-1; the learning function was trainlm; the activation functions were logsig and purelin; the learning rate was 0.1 and able to produce a good level of prediction error with an MSE value of 0.00000424. The results showed that the BPNN algorithm can be used as an alternative method in predicting inflation rates in order to support sustainable economic growth, so that it can improve the welfare of the people in Samarinda, East Kalimantan.
Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal  Nurfadila, Piska Dwi; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (231.173 KB) | DOI: 10.29099/ijair.v3i2.99

Abstract

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.
Solution of class imbalance of k-nearest neighbor for data of new student admission selection Siti Mutrofin; Ainul Mu'alif; Raden Venantius Hari Ginardi; Chastine Fatichah
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (354.75 KB) | DOI: 10.29099/ijair.v3i2.92

Abstract

The objective of this research is to correct the inconsistencies associated with the response differences by each examiner with respect to the assessment of each hafiz candidate. To carry out this research, 259 students were selected within a week using 4testers. However, the examiners are also tasked with another essential mandate which must be immediately fulfilled asides testing candidates for hafiz. In order to overcome this problem, the Educational Data Mining (EDM) system is applied during classification. The problems associated with the use of this technique however, is the limited number of attributes and the imbalance data class. This study was proposed to apply the kNN (k-Nearest Neighbor) technique. The results obtained indicates that kNN can provide recommendations to testers who are students and it is suitable for the solving the problem associated with class imbalance as indicated by the application of Shuffled and Stratified sampling techniques which has values of accuracy, precision, recall and AUC > 0.8%.
Design of a-based smart meters to monitor electricity usage in the household sector using hybrid particle swarm optimization - neural network Yunus, Muhammad Yusuf; Marhatang, Marhatang; Pangkung, Andareas; Djalal, Muhammad Ruswandi
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.283 KB) | DOI: 10.29099/ijair.v3i2.82

Abstract

The procedure is training and testing the nerves that will be made. Matlab software has a Neural Network tool, which in this study will be used. Load sampling data is used as input data for neural network training. As output / target load classification is used. Load classification method, which is 1 for TV load classification, 2 for fan load, 3 for iron load, 4 for water pump load, 5 for lamp load, 6 for dispenser load, and 7 for fan iron load combination. The total load is 6 single loads and 1 combination load. One load combination was chosen because, on the combination load characteristics after the fan has characteristics that are not the same as the others. Data sampling of the current of each load will be used as neural network training. Load data used is 30 samples or for 30 seconds, with every minute the data is taken. From the results of the training, it can be seen that the biggest training error is in the seventh data, namely the identification of the load on the classification of the fan-iron load. This is because the current pattern on the iron and fan with the iron or fan itself has almost the same characteristics. However, for this process networks will be used and then the PSO optimization method is used to reduce the error, in the next study. From the test results, it is shown that by varying the input current data of each load, the network has been able to identify well, even though in the data classification load 7, the load of the iron-fan combination still has a large error. This will be corrected in subsequent studies with Particle Swarm Optimization (PSO) algorithm optimization.
A protocol for Enhanced imaging and Quantification of Cervical Cell Under Scanning electron Microscope Jusman, Yessi; Jamal, Agus; Valzon, May; Hasikin, Khairunnisa; Cheok Ng, Siew
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2476.735 KB) | DOI: 10.29099/ijair.v3i2.98

Abstract

The application of Field Emission Scanning Electron Microscopy and Energy Dispersive X-Ray (FE-SEM/EDX) for the characterization of biological samples can produce promising results for classification purpose. The limitations of the established sample preparation technique of cervical cells for FE-SEM/EDX study that differentiate between normal and abnormal cells prompted the development of a proposed protocol for the preparation of cervical cells. The proposed protocol was conducted by a McDowell-Trump fixative prepared in 0.1M phosphate buffer without osmium tetroxide at 4°C for 2 h in the fixation process. Morphologically, the cervical cells scanned under the FE-SEM/EDX did not present blackening effects, and the structure of the cells was not broken based on the FE-SEM images. Quantitatively, the possible elemental distributions in the cells, such as carbon, nitrogen, oxygen, and sodium, are detected in samples prepared by the proposed protocol. The analysed elements were validated using the Attenuated Total Reflection and Fourier Transform Infrared (ATR/FTIR) spectroscopy. Moreover, by avoiding osmium tetroxide fixation, the time required for sample preparation decreased significantly. This sample preparation protocol can be used for normal and abnormal cervical cells in achieving better results in terms of morphological, detected elemental distribution, and rapid in time.

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