cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
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 330 Documents
 12   13   14   15   16 
Magic cube puzzle approach for image encryption Achmad Fanany Onnilita Gaffar; Rheo Malani; Arief Bramanto Wicaksono Putra
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.422

Abstract

In principle, the image encryption algorithm produces an encrypted image. The encrypted image is composed of arbitrary patterns that do not provide any clues about the plain image and its cipher key. Ideally, the encrypted image is entirely independent of its plain image. Many functions can be used to achieve this goal. Based on the functions used, image encryption techniques are categorized into: (1) Block-based; (2) Chaotic-based; (3) Transformation-based; (4) Conventional-based; and (5) Miscellaneous based. This study proposes a magic cube puzzle approach to encrypt an 8-bit grayscale image. This approach transforms a plain image into a particular size magic cube puzzle, which is consists of a set of blocks. The magic cube puzzle algorithm will diffuse the pixels of the plain image as in a Rubik’s Cube game, by rotating each block in a particular direction called the transposition orientation. The block’s transposition orientation is used as the key seed, while the generation of the cipher key uses a random permutation of the key seed with a certain key length. Several performance metrics have been used to assess the goals, and the results have been compared to several standard encryption methods. This study showed that the proposed method was better than the other methods, except for entropy metrics. For further studies, modification of the method will be carried out in such a way as to be able to increase its entropy value to very close to 8 and its application to true color images. In essence, the magic cube puzzle approach has a large space for pixel diffusion that is possibly supposed to get bigger as a series of data has transformed into several magic cubes. Then, each magic cube has transposed with a different technique. This proposed approach is expected to add to a wealth of knowledge in the field of data encryption.
ModCOVNN: a convolutional neural network approach in COVID-19 prognosis Ahmed Wasif Reza; Jannatul Ferdous Sorna; Md. Momtaz Uddin Rashel; Mir Moynuddin Ahmed Shibly
International Journal of Advances in Intelligent Informatics Vol 7, No 2 (2021): July 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i2.604

Abstract

COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.
Cross-domain sentiment analysis model on Indonesian YouTube comment Agus Sasmito Aribowo; Halizah Basiron; Noor Fazilla Abd Yusof; Siti Khomsah
International Journal of Advances in Intelligent Informatics Vol 7, No 1 (2021): March 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i1.554

Abstract

A cross-domain sentiment analysis (CDSA) study in the Indonesian language and tree-based ensemble machine learning is quite interesting. CDSA is useful to support the labeling process of cross-domain sentiment and reduce any dependence on the experts; however, the mechanism in the opinion unstructured by stop word, language expressions, and Indonesian slang words is unidentified yet. This study aimed to obtain the best model of CDSA for the opinion in Indonesia language that commonly is full of stop words and slang words in the Indonesian dialect. This study was purposely to observe the benefits of the stop words cleaning and slang words conversion in CDSA in the Indonesian language form. It was also to find out which machine learning method is suitable for this model. This study started by crawling five datasets of the comments on YouTube from 5 different domains. The dataset was copied into two groups: the dataset group without any process of stop word cleaning and slang word conversion and the dataset group to stop word cleaning and slang word conversion. CDSA model was built for each dataset group and then tested using two types of tree-based ensemble machine learning, i.e., Random Forest (RF) and Extra Tree (ET) classifier, and tested using three types of non-ensemble machine learning, including Naïve Bayes (NB), SVM, and Decision Tree (DT) as the comparison. Then, It can be suggested that the accuracy of CDSA in Indonesia Language increased if it still removed the stop words and converted the slang words. The best classifier model was built using tree-based ensemble machine learning, particularly ET, as in this study, the ET model could achieve the highest accuracy by 91.19%. This model is expected to be the CDSA technique alternative in the Indonesian language.
Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation Ronnie Sabino Concepcion II; Jonnel Dorado Alejandrino; Sandy Cruz Lauguico; Rogelio Ruzcko Tobias; Edwin Sybingco; Elmer Pamisa Dadios; Argel Alejandro Bandala
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.435

Abstract

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization.
Similarity measure fuzzy soft set for phishing detection Rahmat Hidayat; Iwan Tri Riyadi Yanto; Azizul Azhar Ramli; Mohd Farhan Md. Fudzee
International Journal of Advances in Intelligent Informatics Vol 7, No 1 (2021): March 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i1.605

Abstract

Phishing is a serious web security problem, and the internet fraud technique involves mirroring genuine websites to trick online users into stealing their sensitive information and taking out their personal information, such as bank account information, usernames, credit card, and passwords. Early detection can prevent phishing behavior makes quick protection of personal information. Classification methods can be used to predict this phishing behavior. This paper presents an intelligent classification model for detecting Phishing by redefining a fuzzy soft set (FSS) theory for better computational performance. There are four types of similarity measures: (1) Comparison table, (2) Matching function, (3) Similarity measure, and (4) Distance measure. The experiment showed that the Similarity measure has better performance than the others in accuracy and recall, reached 95.45 % and 99.77 %, respectively. It concludes that FSS similarity measured is more precise than others, and FSS could be a promising approach to avoid phishing activities. This novel method can be implemented in social media software to warn the users as an early warning system. This model can be used for personal or commercial purposes on social media applications to protect sensitive data.
Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis Zuhaira Muhammad Zain; Mona Alshenaifi; Abeer Aljaloud; Tamadhur Albednah; Reham Alghanim; Alanoud Alqifari; Amal Alqahtani
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.462

Abstract

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
Performing image confusion and diffusion using two dimensional triangle functional chaotic maps Prasetyo, Heri; Rosiyadi, Didi; Horng, Shi Jinn; Setiawan, Iwan; Basuki, Akbari Indra
International Journal of Advances in Intelligent Informatics Vol 7, No 2 (2021): July 2021 (Issue in Progess)
Publisher : Universitas Ahmad Dahlan

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

Abstract

Currently, with a malicious attacker against the authorization of an image it is necessary to have a method to secure the content of the image. Therefore this paper presents a new technique for performing image confusion and diffusion using Two Dimensional Triangle Fractional Combination Discrete Chaotic Map (2D-TFCDM). The image confusion requires a set of 2D-TFCDM chaotic random numbers to relocate the pixel locations of plain or secret images. At the same time, image diffusion utilizes the chaotic numbers to modify the pixel values of confused image. The proposed method performs well in terms of visual investigation and objective quality assessment on the confused and diffused images. In addition, it offers a promising result for the grayscale and color image with simple computation, which the experiment results demonstrate that the proposed method outperforms other existing methods under several types of visual observation, correlation coefficients, entropy, UACI, NPCR.
Evaluation of texture feature based on basic local binary pattern for wood defect classification Ibrahim, Eihab Abdelkariem Bashir; Hashim, Ummi Raba'ah; Salahuddin, Lizawati; Ismail, Nor Haslinda; Choon, Ngo Hea; Kanchymalay, Kasturi; Zabri, Siti Normi
International Journal of Advances in Intelligent Informatics Vol 7, No 1 (2021): March 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i1.393

Abstract

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.
An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems Ahmad Dardiri; Felix Andika Dwiyanto; Agung Bella Putra Utama
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.581

Abstract

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.
Feature selection to increase the random forest method performance on high dimensional data Maria Irmina Prasetiyowati; Nur Ulfa Maulidevi; Kridanto Surendro
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.471

Abstract

Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not necessarily informative. So it is necessary to select features in the Random Forest. The purpose of choosing this feature is to select an optimal subset of features that contain valuable information in the hope of accelerating the performance of the Random Forest method. Mainly for the execution of high-dimensional datasets such as the Parkinson, CNAE-9, and Urban Land Cover dataset. The feature selection is done using the Correlation-Based Feature Selection method, using the BestFirst method. Tests were carried out 30 times using the K-Cross Fold Validation value of 10 and dividing the dataset into 70% training and 30% testing. The experiments using the Parkinson dataset obtained a time difference of 0.27 and 0.28 seconds faster than using the Random Forest method without feature selection. Likewise, the trials in the Urban Land Cover dataset had 0.04 and 0.03 seconds, while for the CNAE-9 dataset, the difference time was 2.23 and 2.81 faster than using the Random Forest method without feature selection. These experiments showed that the Random Forest processes are faster when using the first feature selection. Likewise, the accuracy value increased in the two previous experiments, while only the CNAE-9 dataset experiment gets a lower accuracy. This research’s benefits is by first performing feature selection steps using the Correlation-Base Feature Selection method can increase the speed of performance and accuracy of the Random Forest method on high-dimensional data.

Page 14 of 33 | Total Record : 330

 12   13   14   15   16 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol 12, No 1 (2026): February 2026 Vol 11, No 4 (2025): November 2025 Vol 11, No 3 (2025): August 2025 Vol 11, No 2 (2025): May 2025 Vol 11, No 1 (2025): February 2025 Vol 10, No 3 (2024): August 2024 Vol 10, No 2 (2024): May 2024 Vol 10, No 1 (2024): February 2024 Vol 9, No 3 (2023): November 2023 Vol 9, No 2 (2023): July 2023 Vol 9, No 1 (2023): March 2023 Vol 8, No 3 (2022): November 2022 Vol 8, No 2 (2022): July 2022 Vol 8, No 1 (2022): March 2022 Vol 7, No 3 (2021): November 2021 Vol 7, No 3 (2021): November 2021 (Issue in Progress) Vol 7, No 2 (2021): July 2021 (Issue in Progess) Vol 7, No 2 (2021): July 2021 Vol 7, No 1 (2021): March 2021 Vol 6, No 3 (2020): November 2020 Vol 6, No 2 (2020): July 2020 Vol 6, No 1 (2020): March 2020 Vol 5, No 3 (2019): November 2019 Vol 5, No 2 (2019): July 2019 Vol 5, No 1 (2019): March 2019 Vol 4, No 3 (2018): November 2018 Vol 4, No 2 (2018): July 2018 Vol 4, No 1 (2018): March 2018 Vol 3, No 3 (2017): November 2017 Vol 3, No 2 (2017): July 2017 Vol 3, No 1 (2017): March 2017 Vol 2, No 3 (2016): November 2016 Vol 2, No 2 (2016): July 2016 Vol 2, No 1 (2016): March 2016 Vol 1, No 3 (2015): November 2015 Vol 1, No 2 (2015): July 2015 Vol 1, No 1 (2015): March 2015 More Issue