cover
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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.
Arjuna Subject : -
Articles 11 Documents
Search results for , issue "Vol 7, No 3 (2021): November 2021" : 11 Documents clear
Adjusting cyber insurance premiums based on frequency in a communication network Sapto Wahyu Indratno; Yeftanus Antonio; Suhadi Wido Saputro
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.698

Abstract

This study compares cyber insurance premiums with and without a communication network effect frequency. As a cybersecurity factor, the frequency in a communication network influences the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. The node-based model uses probability distributions for the communication mechanism to generate the data. It adopts the co-purchase network formation in market basket analysis for building weighted edges and nodes. Simulations are used to compare the initial and modified infection rates. This paper considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. The premium can reach 20.99% lower than the initial network premium for a network. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price.
Development of marker detection method for estimating angle and distance of underwater remotely operated vehicle to buoyant boat Muhammad Qomaruz Zaman; Ronny Mardiyanto
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.455

Abstract

The paper proposes a Marker Detection Method for Estimating the Angle and Distance of Underwater Remotely Operated Vehicle (ROV) to Buoyant Boat. To keep the ROV aligned with the boat, a marker and visual recognition system are designed. The marker is placed facing down under the boat and a method is developed to recognize the angle and distance of the marker from a facing up camera on the ROV. By considering space, payload, heat dissipation, and buoyancy in a micro class ROV, there are limited options for computing power that can be utilized. This challenge demands a lightweight visual recognition technique for small computers. The proposed method consists of two steps. The marker designing step explains how the marker is constructed of simple components. The marker recognizing step is based on image processing that uses threshold and blob filtering. They are blob size and blob circularity filters which are used to eliminate unwanted information. The real-time orientation and distance estimation by using one camera are the superiority of this method. The proposed method has been tested by using an 11x11 cm2 marker size. The detection rate of the marker is 90% and can be detected up to 120 cm from the camera. The marker can be tilted up to 50° and still has an 80% detection rate. The method can estimate marker rotation angle accurately with a 1.75° average error. The method can estimate the distance between the marker and camera with a -0.62 cm average error. The blob filter is also proven to be superior to a regular dilating and eroding method.
Korean popular culture analytics in social media streaming: evidence from YouTube channels in Thailand Wirapong Chansanam; Kulthida Tuamsuk; Kanyarat Kwiecien; Sam Oh
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.769

Abstract

This research aimed to study and analyze the influence and impact of Korean popular culture (K-pop) on Thai society. In this study, we used Social Network Analysis (SNA) to analyze streaming data obtained from a variety of YouTube channels belonging to YouTubers across the world, text analytics to analyze demographic characteristics, YouTuber's presentation techniques, as well as subscriber behavior, and multiple correlations analysis to analyze the relationship between factors affecting YouTube Channels in Thailand. The findings revealed that five Thai YouTube Channels were influencing Thai society. Furthermore, there were robust positive correlations between the number of dislikes and the number of comments (0.79), and the number of likes and comments (0.65). Additionally, there was a positive correlation between the number of views and the number of dislikes and one between the number of likes and dislikes. Future research can supplement the present findings with other social media sources to yield an even more diverse and comprehensive analysis. These analytics can be applied to various situations, including corporate marketing strategies, political campaigns, or disease/symptom analysis in medicine. This research extends to social computing by revealing intelligent trends in social networks.
Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning Wandercleiton Cardoso; Renzo di Felice
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.771

Abstract

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.
A machine learning approach for the identification and classification of the schizophrenia disorder using EEG signals Chioson, Francheska B.; Tolentino, Jolo Gerard Miel F.; Baldovino, Renann G; Bugtai, Nilo T.
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021 (Issue in Progress)
Publisher : Universitas Ahmad Dahlan

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

Abstract

Schizophrenia is a mental disorder that causes a person to hallucinate and lose touch of reality. The mental disorder is complex and difficult to assess, therefore, multiple tests are done to validate if a person has developed schizophrenia. Prolonged diagnosis may lead to debilitating effects on how a person thinks, feels or reacts. With this in mind, researchers are looking into the difference in brainwave patterns between schizophrenic and healthy patients. Brainwaves are measured using EEG electrodes which are placed across the surface of the head. EEG signals are known to fluctuate heavily when sensory receptors are stimulated. In one study, Roach et al. correlated the lack of N1 suppression in schizophrenic patients when exposed to auditory stimuli. The research aims to further the study of Roache et. Al by testing different machine learning algorithms and determining the model with the best accuracy and computational time. The paper utilizes the lack of N1 suppression to classify schizophrenic patients from healthy patients. Each patient is exposed to different conditions that prompt their auditory receptors. The following machine learning algorithms: support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), decision tree (DT) and DT-Adaboost were able to yield an accuracy above 90%. The research indicates that the difference in N1 signals can be used as a viable parameter when diagnosing schizophrenia.
Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning Retno Kusumaningrum; Iffa Zainan Nisa; Rizka Putri Nawangsari; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.737

Abstract

Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.
Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem Hartono Hartono; Erianto Ongko; Dahlan Abdullah
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.515

Abstract

Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay attention to the performance classifier, has led to an understanding of the importance of selecting an instance that will be used as a classifier. In the classic Hybrid Approach Redefinition method classifier selection is done randomly using the Random Under Sampling approach, and it is interesting to study how performance is obtained if the sampling process is based on Cluster-Based by selecting existing instances. The purpose of this study is to apply the Hybrid Approach Redefinition method with Cluster-Based Instance Selection (CBIS) approach so that it can obtain a better performance classifier. The results showed that Hybrid Approach Redefinition with cluster-based instance selection gave better results on the number of classifiers, data diversity, and performance classifiers compared to classic Hybrid Approach Redefinition.
Comparative analysis of classification techniques for leaves and land cover texture Azri Azrul Azmer; Norlida Hassan; Shihab Hamad Khaleefah; Salama A Mostafa; Azizul Azhar Ramli
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.706

Abstract

The texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.
An improved K-Nearest neighbour with grasshopper optimization algorithm for imputation of missing data Nadzurah Zainal Abidin; Amelia Ritahani Ismail
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.696

Abstract

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. Our GOA is designed to find the best value of k and optimize the imputed value from KNN that maximizes the imputation accuracy. Experimental evaluation for different types of datasets collected from UCI, with various rates of missing values ranging from 10%, 30%, and 50%. Our proposed algorithm has achieved promising results from the experiment conducted, which outperformed other methods, especially in terms of accuracy.
A data mining approach for classification of traffic violations types Nor Aqilah Othman; Cik Feresa Mohd Foozy; Aida Mustapha; Salama A Mostafa; Shamala Palaniappan; Shafiza Ariffin Kashinath
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.708

Abstract

Traffic summons, also known as traffic tickets, is a notice issued by a law enforcement official to a motorist, who is a person who drives a car, lorry, or bus, and a person who rides a motorcycle. This study is set to perform a comparative experiment to compare the performance of three classification algorithms (Naive Bayes, Gradient Boosted Trees, and Deep Learning algorithm) in classifying the traffic violation types. The performance of all the three classification models developed in this work is measured and compared. The results show that the Gradient Boosted Trees and Deep Learning algorithm have the best value in accuracy and recall but low precision. Naïve Bayes, on the other hand, has high recall since it is a picky classifier that only performs well in a dataset that is high in precision. This paper’s results could serve as baseline results for investigations related to the classification of traffic violation types. It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning, or an ESERO (Electronic Safety Equipment Repair Order).

Page 1 of 2 | Total Record : 11