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All Journal Science in Information Technology Letters Signal and Image Processing Letters
Dewi Pramudi Ismi, Dewi Pramudi
Universitas Ahmad Dahlan
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Advanced product review summarization in e-commerce marketplaces: elevating beyond tf-idf and lexrank method Anggraeni, Rita Melina; Ismi, Dewi Pramudi
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1225

Abstract

In the fiercely competitive domain of online product sales, wherein engendering trust among prospective buyers assumes paramount significance, the role of product reviews cannot be understated. However, a prevailing issue in online marketplaces resides in the presence of product reviews that do not consistently align with the overall product rating. Furthermore, the sheer abundance of comments often leads potential consumers to confine their scrutiny to the initial comments, thus leaving a substantial volume of reviews unexplored. To rectify this challenge, this study introduces an automated text summarization system for product reviews, leveraging the LexRank methodology. This system underwent rigorous evaluation using the Rouge metric, with results manifesting substantial promise. At a threshold of 0.1, Rouge-1 exhibited an accuracy of 16.67%, while Rouge-2 scored 3.01%, and Rouge-L reached 16.50%. At a threshold of 0.2, Rouge-1 yielded a score of 16.08%, Rouge-2 registered 2.64%, and Rouge-L scored 16.57%. The second evaluation, performed with a distinct test dataset, notably excelled, emphasizing the system's competence. Specifically, at the 0.2 threshold, the system displayed superior performance, underscoring its efficacy in refining product review summarization within online marketplaces
Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV Ariwibowo, Fuad Izzudin; Ismi, Dewi Pramudi
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1216

Abstract

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement efforts
Enhancing the performance of heart arrhythmia prediction model using Convolutional Neural Network based architectures Ismi, Dewi Pramudi; Khoirunnisa, Ninda
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1794

Abstract

Heart disease is one of the diseases that exposes high mortality worldwide. This conventional way of predicting heart disease is usually expensive, time-consuming, and prone to human error. Early detection of heart disease is important as it helps to prevent deaths caused by this disease.  Machine learning utilization as the non-invasive means for predicting heart disease is considered as a fast and affordable method to prevent the fatality of heart disease. This work aims at utilizing  Convolutional neural network (CNN)  to enhance the performance of an Arrhythmia prediction model. We have built an Arrythmia prediction model using neural networks comprising multiple convolutional layers and maxpooling layers. Our proposed model is trained using the MIT-BIH Arrhythmia dataset. The model performance has been evaluated and the model achieves  98.43% of performance  accuracy
K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining Normawati, Dwi; Ismi, Dewi Pramudi
Signal and Image Processing Letters Vol 1, No 2 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i2.3

Abstract

Coronary heart disease occurs when atheroclerosis inhibits blood flow to the heart muscle in the coronary arteries. This disease is often the cause of human death. The method for diagnosing coronary heart disease that is often a doctor's referral is coronary angiography, but it is invasive, expensive, and high-risk. This study aims to analyze the effect of k-Fold Cross-Validation (CV) on the dataset to create features based on the rules used to diagnose coronary heart disease. This study uses the Cleveland heart disease dataset, where feature selection is performed using a medical expert-based method (MFS) and a computer-based method, Variable Precision Rough Set (VPRS). Evaluation of the classification performance using the k-fold 10-fold, 5-fold and 3-fold methods. The results showed the number of different attribute selection results in each fold, both for the VPRS and MFS methods, with the highest accuracy score in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3.
Classification of coronary heart disease using the multi-layer perceptron neural networks Ikhwandoko, Fatih; Ismi, Dewi Pramudi
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2186

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide. The complexity of risk factors such as blood pressure, cholesterol, smoking history, and unhealthy lifestyles often makes the diagnosis process less effective. With the increasing need for fast and accurate heart disease prediction systems, the use of artificial intelligence-based methods such as Neural Networks is a promising solution. This study aims to evaluate the ability of the Multi-Layer Perceptron (MLP) algorithm to classify CHD risk using the Framingham Heart Study dataset, while comparing it with other commonly used classification methods. This research used the collection of Framingham heart disease data containing 15 medical features. The data was then processed through cleaning, normalization, and class balancing using the SMOTE method. An MLP model was designed with two hidden layers using 200 and 128 neuron architectures, and tested in three training and testing data split scenarios (70:30, 75:25, and 80:20). The model was trained for 100 epochs and evaluated using accuracy, precision, and recall metrics to assess its classification performance. The experiment results show that MLP is able to produce high performance with 86.20% accuracy. 84.40% precision, and 88.56% recall. Compared to other methods such as Decision Tree and SVM, the experiment results show that MLP demonstrated superior classification accuracy. Thus, MLP has the potential to be an effective tool for supporting early diagnosis of coronary heart disease more intelligently and efficiently
Co-Authors Anggraeni, Rita Melina Ariwibowo, Fuad Izzudin Dwi Normawati, Dwi Ikhwandoko, Fatih Khoirunnisa, Ninda