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International Journal of Advances in Intelligent Informatics
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
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Articles 10 Documents
Search results for , issue "Vol 8, No 1 (2022): March 2022" : 10 Documents clear
An extended approach of weight collective influence graph for detection influence actor Galih Hendro Martono; Azhari Azhari; Khabib Mustofa
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.800

Abstract

Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time.
An approach for linguistic multi-attribute decision making based on linguistic many-valued logic Anh Phuong Le; Hoai Nhan Tran; Thi Uyen Thi Nguyen; Dinh Khang Tran
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.820

Abstract

There are various types of multi-attribute decision-making (MADM) problems in our daily lives and decision-making problems under uncertain environments with vague and imprecise information involved. Therefore, linguistic multi-attribute decision-making problems are an important type studied extensively. Besides, it is easier for decision-makers to use linguistic terms to evaluate/choose among alternatives in real life. Based on the theoretical foundation of the Hedge algebra and linguistic many-valued logic, this study aims to address multi-attribute decision-making problems by linguistic valued qualitative aggregation and reasoning method. In this paper, we construct a finite monotonous Hedge algebra for modeling the linguistic information related to MADM problems and use linguistic many-valued logic for deducing the outcome of decision making. Our method computes directly on linguistic terms without numerical approximation. This method takes advantage of linguistic information processing and shows the benefit of Hedge algebra.
Machine learning for the prediction of phenols cytotoxicity Latifa Douali
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.748

Abstract

Quantitative structure-activity relationships (QSAR) are relevant techniques that assist biologists and chemists in accelerating the drug design process and help understanding many biological and chemical mechanisms. Using classical statistical methods may affect the accuracy and the reliability of the developed QSAR models. This work aims to use a machine learning approach to establish a QSAR model for phenols cytotoxicity prediction. This issue concern many chemists and biologists. In this investigation, the dataset is diverse, and the cytotoxicity data are sparse. Multi-component description of the compounds has then been considered. A set of molecular descriptors fed the deep neural network (DNN) and served to train the DNN. The established DNN model was able to predict the cytotoxicity of the phenols at high precision. The correlation coefficient at the fitting stage was higher than other statistical methods reported in the literature or developed in the present work, specifically multiple linear regression (MLR) and shallow artificial neural networks (ANN), and was equal to 0.943. The predictive capability of the model, as estimated by the coefficient of determination on an external predictive dataset, was significantly high and was about 0.739. This finding could help implement many molecular descriptors relevant to describing the compounds, representing the effects governing the phenols' cytotoxicity toward Tetrahymena pyriformis, avoiding overfitting and outlier exclusion.
Prediction of player position for talent identification in association netball: a regression-based approach Nur Hazwani Jasni; Aida Mustapha; Siti Solehah Tenah; Salama A Mostafa; Nazim Razali
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.707

Abstract

Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models.
Portfolio optimization based on self-organizing maps clustering and genetics algorithm Fajri Farid; Dedi Rosadi
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.587

Abstract

In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory.
The mortality Modelling of COVID-19 Patients using SARIMA-PARCD Combinations Imam Tahyudin; Rizki Wahyudi; Wiga Maulana; Hidetaka Nambo
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aimed to construct the model of COVID-19 patients’ mortality and to obtain less of prediction errors. We performed the combination of the SARIMA-PARCD method to predict male patients who died because of COVID-19 in the USA. Considering several previous studies, the SARIMA method has the advantage of solving linear datasets while the PARCD method is superior in solving nonlinear datasets. We proposed that SARIMA-PARCD has a powerful utility for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e. SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049.
Rayleigh quotient with bolzano booster for faster convergence of dominant eigenvalues M Zainal Arifin; Ahmad Naim Che Pee; Sarni Suhaila Rahim; Aji Prasetya Wibawa
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.718

Abstract

Computation ranking algorithms are widely used in several informatics fields. One of them is the PageRank algorithm, recognized as the most popular search engine globally. Many researchers have improvised the ranking algorithm in order to get better results. Recent research using Rayleigh Quotient to speed up PageRank can guarantee the convergence of the dominant eigenvalues as a key value for stopping computation. Bolzano's method has a convergence character on a linear function by dividing an interval into two intervals for better convergence. This research aims to implant the Bolzano algorithm into Rayleigh for faster computation. This research produces an algorithm that has been tested and validated by mathematicians, which shows an optimization speed of a maximum 7.08% compared to the sole Rayleigh approach. Analysis of computation results using statistics software shows that the degree of the curve of the new algorithm, which is Rayleigh with Bolzano booster (RB), is positive and more significant than the original method. In other words, the linear function will always be faster in the subsequent computation than the previous method.
An Improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem Samar Bashath; Amelia Ritahani Ismail; Ali A Alwan; Amir Aatieff Amir Hussin
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.818

Abstract

Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performance. PSO is regarded as one of the numerous researchers' most well-studied algorithms. However, two of its most fundamental problems remain unresolved. PSO converges onto the local optimum for high-dimensional optimization problems, and it has slow convergence speeds. This paper introduces a new variant of a particle swarm optimization algorithm utilizing Lévy flight-McCulloch, and fast simulated annealing (PSOLFS). The proposed algorithm uses two strategies to address high-dimensional problems: hybrid PSO to define the global search area and fast simulated annealing to refine the visited search region. In this paper, PSOLFS is designed based on a balance between exploration and exploitation. We evaluated the algorithm on 16 benchmark functions for 500 and 1,000 dimension experiments. On 500 dimensions, the algorithm obtains the optimal value on 14 out of 16 functions. On 1,000 dimensions, the algorithm obtains the optimal value on eight benchmark functions and is close to optimal on four others. We also compared PSOLFS with another five PSO variants regarding convergence accuracy and speed. The results demonstrated higher accuracy and faster convergence speed than other PSO variants. Moreover, the results of the Wilcoxon test show a significant difference between PSOLFS and the other PSO variants. Our experiments' findings show that the proposed method enhances the standard PSO by avoiding the local optimum and improving the convergence speed.
Temperament detection based on Twitter data: classical machine learning versus deep learning Annisa Ulizulfa; Retno Kusumaningrum; Khadijah Khadijah; Rismiyati Rismiyati
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.692

Abstract

Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007.
Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review Jayson Rogelio; Elmer Dadios; Argel Bandala; Ryan Rhay Vicerra; Edwin Sybingco
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.819

Abstract

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools.

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