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
 7   8   9   10   11 
Modified balanced random forest for improving imbalanced data prediction Zahra Putri Agusta; Adiwijaya Adiwijaya
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.255

Abstract

This paper proposes a Modified Balanced Random Forest (MBRF) algorithm as a classification technique to address imbalanced data. The MBRF process changes the process in a Balanced Random Forest by applying an under-sampling strategy based on clustering techniques for each data bootstrap decision tree in the Random Forest algorithm. To find the optimal performance of our proposed method compared with four clustering techniques, like: K-MEANS, Spectral Clustering, Agglomerative Clustering, and Ward Hierarchical Clustering. The experimental result show the Ward Hierarchical Clustering Technique achieved optimal performance, also the proposed MBRF method yielded better performance compared to the Balanced Random Forest (BRF) and Random Forest (RF) algorithms, with a sensitivity value or true positive rate (TPR) of 93.42%, a specificity or true negative rate (TNR) of 93.60%, and the best AUC accuracy value of 93.51%. Moreover, MBRF also reduced process running time.
AncDE with gaussian distribution for numerical optimization problem Siti Khadijah Mohd Salleh; Siti Azirah Asmai; Zuraida Abal Abas; Abdul Samad Shibghatullah; Diarmuid O'Donoghue
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.258

Abstract

This work is introducing an enhanced Differential Evolution (DE) called AncDE. This proposed algorithm is using an additional population from the current generation and located it as ancestor. There are two parameter controllers to manage the selection of ancestor vector; aup for selection frequency and arp for age of selection. In this work we were applying Gaussian distribution on aup and we tested it on CEC 2015 Numerical Optimization Problem. Standard Differential Evolution will act as the benchmark. The result shows that AncDE with Gaussian approach has produced better result than standard DE.
Estimating the function of oscillatory components in SSA-based forecasting model Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami; Muhammad Hisyam Lee
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.312

Abstract

The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with those obtained by nonlinear least square based on Levenberg Marquardt (NLM) method. A simulation study based on the time series data which has a linear amplitude modulated sinusoid component is conducted to investigate the error of estimated parameters of the model obtained by the proposed method. A real data series was also considered for the application example. The results show that in terms of forecasting accuracy, the SSA-based model where the oscillatory components are obtained by iterative OLS is nearly the same with that is obtained by the NLM method.
Fast and stable direct relative orientation of UAV-based stereo pair Martinus Edwin Tjahjadi; Fransisca Dwi Agustina
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.327

Abstract

Coplanarity-based relative orientation (RO) is one of the most crucial processes to obtain reliable 3D model and point clouds in Computer Vision and Photogrammetry community. Whilst a classical and rigorous procedure requires very close approximate values of five independent parameters, a direct method needs additional constraints to solve the parameters. This paper proposes a new approach that facilitates a very fast but stable and accurate solution from five point correspondences between two overlapping aerial images taken form unmanned aerial vehicle (UAV) flight. Furthermore, if 3D coordinates of perspective centers are available form geotagged images, rotational elements of the RO parameters can be quickly solved using three correspondences only. So it is very reliable for a provision of closed-form solutions for the rigorous methods. Our formulation regards Thompson’s parameterizations of Euler angles in composing a coplanarity condition. Nonlinear terms are subsequently added into a stereo parallax within a constant term under a linear least squares criteria. This strategy is considered new as compared with the known literatures since the proposed approach can find optimal solution. Results from real datasets confirm that our method produces a fast, stable and reliable linear solution by using at least five correspondences or even only three conjugate points of geotagged image pairs.
Hierarchical multi-label news article classification with distributed semantic model based features Ivana Clairine Irsan; Masayu Leylia Khodra
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.168

Abstract

Automatic news categorization is essential to automatically handle the classification of multi-label news articles in online portal. This research employs some potential methods to improve performance of hierarchical multi-label classifier for Indonesian news article. First potential method is using Convolutional Neural Network (CNN) to build the top level classifier. The second method could improve the classification performance by calculating the average of the word vectors obtained from distributed semantic model. The third method combines lexical and semantic method to extract documents features, which multiplied word term frequency (lexical) with word vector average (semantic). Model build using Calibrated Label Ranking as multi-label classification method, and trained using Naïve Bayes algorithm has the best F1-measure of 0.7531. Multiplication of word term frequency and the average of word vectors were also used to build this classifiers. This configuration improved multi-label classification performance by 4.25%, compared to the baseline. The distributed semantic model that gave best performance in this experiment obtained from 300-dimension word2vec of Wikipedia’s articles. The multi-label classification model performance is also influenced by news’ released date. The difference period between training and testing data would also decrease models’ performance.
Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods Murinto Murinto; Nur Rochmah Dyah Puji Astuti; Murein Miksa Mardhia
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.311

Abstract

High dimensional problems are often encountered in studies related to hyperspectral data. One of the challenges that arise is how to find representations that are accurate so that important structures can be clearly easily. This study aims to process segmentation of hyperspectral image by using swarm optimization techniques. This experiments use Aviris Indian Pines hyperspectral image dataset that consist of 103 bands. The method used for segmentation image is particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO) and fractional order Darwinian particle swarm optimization (FODPSO). Before process segmentation image, the dimension of the hyperspectral image data set are first reduced by using independent component analysis (ICA) technique to get first independent component. The experimental show that FODPSO method is better than PSO and DPSO, in terms of the average CPU processing time and best fitness value. The PSNR and SSIM values when using FODPSO are better than the other two swarm optimization method. It can be concluded that FODPSO method is better in order to obtain better segmentation results compared to the previous method.
VIKOR multi-criteria decision making with AHP reliable weighting for article acceptance recommendation Aji Prasetya Wibawa; Juwita Annisa Fauzi; Seno Isbiyantoro; Rahmat Irsyada; Dhaniyar Dhaniyar; Leonel Hernandez
International Journal of Advances in Intelligent Informatics Vol 5, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i2.172

Abstract

DSS is built to support the solution recommendation of a problem. AHP and VIKOR are examples of DSS method. Due to VIKOR’s subjective weighting, this study combines the AHP and VIKOR approach to create a better and more reliable decision support system. The DSS is used to recommend article acceptance using five criteria: originality, quality, clarity, significance, and relevance. The results showed that AHP-VIKOR outperforms the performance of VIKOR. AHP weighting reliably replaces the subjective VIKOR’s initial weighting. The AHP-VIKOR result is more accurate and steadier than VIKOR. Thus, AHP-VIKOR can be presented as a proposed approach for creating a recommendation of scientific article acceptance.
Medoid-based shadow value validation and visualization Weksi Budiaji
International Journal of Advances in Intelligent Informatics Vol 5, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i2.326

Abstract

A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed.  Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV.
Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets A’inur A’fifah Amri; Amelia Ritahani Ismail; Omar Abdelaziz Mohammad
International Journal of Advances in Intelligent Informatics Vol 5, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i2.350

Abstract

Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.
Reversible data hiding method by extending reduced difference expansion Zainal Syahlan; Tohari Ahmad
International Journal of Advances in Intelligent Informatics Vol 5, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i2.351

Abstract

To keep hiding secret data in multimedia files, such as video, audio, and image considers essential for information security. Image, for instance, as the media aids data insertion securely. The use of insertion technique must ensure a reliable process on retaining data quality and capacity. However, a trade-off between the resulted image quality and the embedded payload capacity after the embedding process often occurs. Therefore, this research aims at extending the existing method of integrating confidential messages using the Reduced Difference Expansion (RDE), transform into a medical image by changing the base point, block size, and recalculating of difference. The results display that the proposed method enhances the quality of the stego image and capacity of the hidden message.

Page 9 of 33 | Total Record : 330

 7   8   9   10   11 

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 (Issue in Progress) Vol 7, No 3 (2021): November 2021 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