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All Journal Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA SABDAMAS Science in Information Technology Letters International Journal of Robotics and Control Systems Applied Engineering and Technology Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence in Architecture
Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : International Journal of Advances in Intelligent Informatics

 1 
Parallel mathematical models of dynamic objects Roman Voliansky; Andri Pranolo
International Journal of Advances in Intelligent Informatics Vol 4, No 2 (2018): July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i2.229

Abstract

The paper deals with the developing of the methodological backgrounds for the modeling and simulation of complex dynamical objects. Such backgrounds allow us to perform coordinate transformation and formulate the algorithm of its usage for transforming the serial mathematical model into parallel ones. This algorithm is based on partial fraction decomposition of the transfer function of a dynamic object. Usage of proposed algorithms is one of the ways to decrease calculation time and improve PC usage while a simulation is being performed. We prove our approach by considering the example of modeling and simulating of fourth order dynamical object with various eigenvalues. This example shows that developed parallel model is stable, well-convergent, and high-accuracy model. There is no defined any calculation errors between well-known serial model and proposed parallel one. Nevertheless, the proposed approach’s usage allows us to reduce calculation time by more than 20% by using several CPU’s cores while calculations are being performed.
Generated rules for AIDS and e-learning classifier using rough set approach Sarina Sulaiman; Nor Amalina Abdul Rahim; Andri Pranolo
International Journal of Advances in Intelligent Informatics Vol 2, No 2 (2016): July 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i2.74

Abstract

The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model
GPU Accelerated Number Plate Localization in Crowded Situation Adhi Prahara; Andri Pranolo; Rafał Dreżewski
International Journal of Advances in Intelligent Informatics Vol 1, No 3 (2015): November 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i3.46

Abstract

Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The test result shows good results, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plate
CAE-COVIDX: automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder Hanafi Hanafi; Andri Pranolo; Yingchi Mao
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.v7i1.577

Abstract

Since the first case in 2019, Corona Virus has been spreading all over the world. World Health Organization (WHO) announced that COVID-19 had become an international pandemic. There is an essential section to handle the spreading of the virus by immediate virus detection for patients. Traditional medical detection requires a long time, a specific laboratory, and a high cost. A method for detecting Covid-19 faster compared to common approaches, such as RT-PCR detection, is needed. The method demonstrated that it could produce an X-ray image with higher accuracy and consumed less time. We propose a novel method to extract image features and to classify COVID-19 using deep CNN combined with Autoencoder (AE) dubbed CAE-COVIDX. We evaluated and compared it with the traditional CNN and existing framework VGG16 involving 400 normal images of non-COVID19 and 400 positive COVID-19 diseases. The performance evaluation was conducted using accuracy, confusion matrix, and loss evaluation. Based on experiment results, the CAE-COVIDX framework outperforms previous methods in several testing scenarios. This framework's ability to detect Covid-19 in various nonstandard image X-rays could effectively help medical employers diagnose Covid-19 patients. It is an important factor to decrease the spreading of Covid-19 massively.
IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism Hanafi Hanafi; Andri Pranolo; Yingchi Mao; Taqwa Hariguna; Leonel Hernandez; Nanang Fitriana Kurniawan
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.942

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.
Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT Ismail, Amelia Ritahani; Azlan, Faris Farhan; Noormaizan, Khairul Akmal; Afiqa, Nurul; Nisa, Syed Qamrun; Ghazali, Ahmad Badaruddin; Pranolo, Andri; Saifullah, Shoffan
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1529

Abstract

Accurate segmentation of radiolucent lesions in dental Cone-Beam Computed Tomography (CBCT) is vital for enhancing diagnostic reliability and reducing the burden on clinicians. This study proposes a privacy-preserving segmentation framework leveraging multiple U-Net variants—U-Net, DoubleU-Net, U2-Net, and Spatial Attention U-Net (SA-UNet)—to address challenges posed by limited labeled data and patient confidentiality concerns. To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. Among the evaluated architectures, U2-Net demonstrates superior segmentation performance with a Dice coefficient of 0.833 and an Intersection over Union (IoU) of 0.881, showing less than 2% reduction under privacy constraints. To mitigate data annotation scarcity, a pseudo-labeling approach is implemented within an MLOps pipeline, enabling semi-supervised learning from unlabeled CBCT images. Over three iterative refinements, the pseudo-labeling strategy reduces validation loss by 14.4% and improves Dice score by 2.6%, demonstrating its effectiveness. Additionally, comparative evaluations reveal that SA-UNet offers competitive accuracy with faster inference time (22 ms per slice), making it suitable for low-resource deployments. The proposed approach presents a scalable and privacy-compliant framework for radiolucent lesion segmentation, supporting clinical decision-making in real-world dental imaging scenarios.
GAN-Enhanced multimodal fusion and ensemble learning for imbalanced chest X-Ray classification Snani, Aissa; Khadir, Mohammed Tarek; Pranolo, Andri; Abdalla, Modawy Adam Ali
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2092

Abstract

Chest X-ray (CXR) classification tasks often suffer from severe class imbalance, resulting in biased predictions and suboptimal diagnostic performance. To address this challenge, we propose an integrated framework that combines high-fidelity data augmentation using Generative Adversarial Networks (GANs), ensemble learning via hard and soft voting, and multimodal feature fusion. The method begins by partitioning the majority class into multiple subsets, which are individually balanced through GAN-generated synthetic images. Deep learning models, specifically DenseNet201 and EfficientNetV2B3, are trained separately on each balanced subset. These models are then combined using ensemble voting to improve robustness. Additionally, features extracted from the most performant models are fused and used to train traditional classifiers such as Logistic Regression, Multilayer Perceptron, CatBoost, and XGBoost. Evaluations on a publicly available CXR dataset demonstrate consistent improvements across key metrics, including accuracy, precision, recall, F1-score, AUROC, AUPRC, MCC, and G-mean. This framework shows superior performance in multiclass scenarios.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agus Dianto Agus Salim Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Dani Fadillah Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Ismail, Amelia Ritahani Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Rafal Drezewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Sularso Sularso, Sularso Suparman Supriadi Supriadi Taqwa Hariguna Tedy Setyadi Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zhou, Xiaofeng