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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Kurniadi, Dede Rahmi, Murni Lestari Mulyani, Asri Nashrulloh, Muhammad Rikza Septiana, Yosep Tri Julianto, Indri Institut Teknologi Garut Department of Computer Science Garut Indonesia Institut Teknologi Garut Informatics Study Program Garut Indonesia

ISBN: (纸本)9798350389654

Visual impairment is a condition that affects the sense of sight. In Indonesia, around 1.5% of the 4 million people are blind. In their daily activities, the visually impaired rely on their sense of touch to recognize the value of money by feeling the double lines on the right and left sides of the rupiah banknote. However, if the money is in bad condition, they are unable to recognize it, so they have to involve the help of others with the risk of fraud. Research is underway to develop a paper currency detection tool to assist the visually impaired in everyday transactions and reduce the risk of fraud. The research in this paper focuses on creating a classification model for detecting Indonesian rupiah banknotes. The method approach used in this research involves using a custom architecture Convolutional Neural Network model that combines elements from the AlexNet and VGGNet architectures, along with the RMSProp Optimizer. Before training the model, the private dataset that has been collected is augmented to increase the dataset and then split data. Next, the model is trained and tested using a private dataset of 800 images of rupiah banknotes for 2016, 2020, and 2022 emissions years, divided into eight classes of banknotes with denominations of IDR 1000, IDR 2000, IDR 5000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000. Finally, the model's performance is evaluated using loss function, accuracy, precision, recall, and AUC-ROC evaluation metrics. The model evaluation results show a value loss function of 1.28, an accuracy of 95.31 %, a precision of 95.24%, a recall of 93.75%, and an AUC-ROC value of 0.99. This evaluation result indicates that the model is highly effective for use in the rupiah currency nominal detection system for the visually impaired. © 2024 IEEE.

关键词： Convolutional neural networks

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New Student Prediction Using Algorithm Naive Bayes and Regression Analysis in Universitas Potensi Utama 4

New Student Prediction Using Algorithm Naive Bayes and Regre...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Aditya, Elsa Situmorang, Zakarias Hayadi, B. Herawan Zarlis, Muhammad Wanayumini Universitas Potensi Utama Computer Science Postgraduate Study Program Medan Indonesia Saint Thomas Catholic University Computer Science Postgraduate Study Program Medan Indonesia University of Sumatera Utara Computer Science Postgraduate Study Program Medan Indonesia

ISBN: (纸本)9781665453950

Universitas Potensi Utama has various study programs, and also has various facilities to support student learning activities. However, the main obstacle in higher education is the uncertainty of the interest of students who register, so that sometimes the facilities are inadequate. To overcome these problems, there must be activities to predict prospective new student applicants to improve facilities and interest of prospective students to choose to study at the Universitas Potensi Utama. Forecasting is the most important thing that must be applied to a company. With this forecasting, companies can see the opportunities that exist to generate sales predictions in the future based on the results of past sales data. Naive Bayes is a classification using probability and statistical methods, the Naive Bayes algorithm can be used in scientific fields, one of which is predicting future opportunities based on previous experience. Linear regression method can be used for forecasting with the assumption that the correlation between variables will continue in the future. Linear Regression is a Regression Method where the resulting equation is linear. Based on the resulting equation, predictions can be calculated by entering the values of the predictor variables in the equation. Based on this process, the predictive value of the response variable can be generated. In this study, the author explains more about how to determine new student predictions using a combination of the two algorithms where the Naïve Bayes algorithm is used to state which study programs have a lot of interest in the coming year, and the Linear Regression algorithm is used to show which study programs are most in demand based on the number of students who registered in the previous year, In the description that has been stated above, the author makes a paper with the title: 'Predictions of New Student Registration Using the Naive Bayes Algorithm and Regression Analysis at the Universitas Potensi Utama'. ©

关键词： Forecasting

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Feature Learning using Deep Variational Autoencoder for Prediction of Defects in Car Engine

Feature Learning using Deep Variational Autoencoder for Pred...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Susanto, Nanang Pardede, Hilman Ferdinandus Universitas Nusa Mandiri Computer Science Study Program Jakarta Indonesia National Research and Innovation Agency Indonesia Universitas Nusa Mandiri Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Machine learning has been employed to automatically detect the defects on car engines in several studies. One of crucial challenges on applying machine learning is on the amount of defect data collected is often large with high dimensional data, making manual detection inefficient and inaccurate. The other problem is on the missing data as oftentimes the collected data are incomplete. In this paper, we employ machine learning frameworks for engine defect detection. It comprises the data pre-processing stage which includes imputing missing value data, then performing feature correlation using the Pearson method, and selecting the features to use. After that, the label encoder and standard scaler are carried out. The experimental process begins with creating a baseline, then continues with improving imbalance data using SMOTE, and feature reconstruction using variational autoencoder (VAE). Afterwards, for classification, we employ convolutional neural networks (CNN). The proposed method achieved precision 99.63%. We collect engine quality dataset of 224,239 data with 90 features from major automobile manufacturing in Indonesia. This showed that SMOTE and Variational Autoencoder dimensional reconstruction method can overcome defect predictions in car engine defect data with data imbalance conditions. This novel methodology distinguished our study from prior methods and shows considerable increases in precision and recall matrix. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements in Deep Learning Optimization for Post-Earthquake Building Damage Detection A Systematic Review

Advancements in Deep Learning Optimization for Post-Earthqua...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Sonang, Sahat Yuhandri, Y. Tajuddin, Muhammad Universitas Putra Indonesia Yptk Information Technology Study Program Padang Indonesia Universitas Bumigora Department of Computer Science Mataram Indonesia

ISBN: (纸本)9798350392036

This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collect, analyze, and summarize the findings of various studies published between 2014 and 2024. This research identifies and evaluates optimization methods such as hyperparameter tuning, transfer learning, and data augmentation, highlighting their effectiveness and implementation. Challenges such as data quality, computational costs, and model interpretability were also discussed. The findings indicate that while deep learning techniques significantly enhance the accuracy and efficiency of damage detection, further research is required to address existing challenges and improve model robustness in diverse earthquake scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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Simulation of Electromagnetic Wave Propagation in Graphene-Coated 1D Photonic Crystals Using the Finite Volume Method

Simulation of Electromagnetic Wave Propagation in Graphene-C...

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2024 International Conference on Interdisciplinary Physics, ICIPs 2024

作者： Negara, Teguh Puja Hardhienata, Hendradi Alatas, Husin Study Program of Computer Science Faculty of Mathematics and Natural Science Universitas Pakuan Bogor Indonesia Theoretical Physics Division Department of Physics Faculty of Mathematics and Natural Science IPB University Bogor Indonesia

A numerical study on the interaction of electromagnetic waves with photonic crystals with one-dimensional periodicity has been carried out using the Finite Volume method. The photonic crystals with one-dimensional periodicity consists of 7 alternating layers of and with thicknesses of and, respectively. Electromagnetic field components have been simulated to propagate through the photonic crystal structure, experiencing reflection and transmission at each layer of the structure. The simulation results show the occurrence of absorption precisely at the Bragg frequency by the photonic crystal, which is referred to as the photonic band gap (PBG). A comparison of the simulation results has been conducted using different fluxes, such as the Lax-Friedrichs and upwind schemes, under PBG conditions. The results show smoother absorption using the Lax-Friedrichs scheme. Additionally, the use of a single-layer graphene on the front side of the photonic crystal has shown more perfect absorption compared to without the single-layer graphene. Therefore, the integrated graphene-photonic crystal system can be used as a perfect reflector. The simulation runs well using effective absorbing boundary conditions (ABCs) to reduce wave reflections at the edges of the computational window. Based on the simulation results, the Finite Volume method has been proven to be a powerful tool in describing electromagnetic wave propagation through photonic crystal systems with or without graphene. These findings provide a foundational model for fabrication approaches and holds potential for future advancements in sensor technologies, moreover two-dimensional and three-dimensional structures. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Photonic band gap

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Eye Disease Detection Model Using Convolutional Neural Network with RMSprop, SGD, and Adam Optimizers 11

Eye Disease Detection Model Using Convolutional Neural Netwo...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Kurniadi, Dede Mulyani, Asri Riefky Rafliana Suwandy, Moch Institut Teknologi Garut Department of Computer Science Kabupaten Garut Indonesia Institut Teknologi Garut Informatics Study Program Kabupaten Garut Indonesia

ISBN: (纸本)9798350389654

Eye disorders such as cataracts, glaucoma, and diabetic retinopathy can cause abnormalities in eye function, including blindness. This research aims to develop a machine-learning model and find the best model for detecting eye diseases. Deep Learning methods based on the Convolutional Neural Network (CNN) model will be used to create eye disease detection models by adding the RMSProp, SGD, and Adam optimizers techniques. The dataset used consists of 4 classes, namely normal (1074 data), glaucoma (1007 data), cataract (1038 data), and diabetic retinopathy (1098 data), totaling 4217 fundus images. The evaluation results reveal the CNN model with the RMSProp optimizer has better model performance with a value of 88% accuracy, 88% precision, 87% recall, and 88% f1-score, and based on the AUC value on the ROC-AUC curve for each class is above 0.90, which means that the model can differentiate data between classes well and is included in the Excellent classification category. © 2024 IEEE.

关键词： Convolutional neural networks

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Sujatmiko, Dhiki Sari, Christy Atika Rachmawanto, Eko Hari Krismawan, Andi Danang Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Animation Semarang Indonesia Mosul University Presidency University of Mosul Department of Computer Science Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches. © 2023 IEEE.

关键词： Audition

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Improvement of PID Based on Fuzzy Logic Controller for Controlling Attitude of a Quadcopter 1

Improvement of PID Based on Fuzzy Logic Controller for Contr...

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1st International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics, ICAAEEI 2024

作者： Rizki Dalimunthe, Ernando Sibuea, Horas Nico L. P. Rama Setiadi, Dirga Jayadi, Akhmad Persada Sembiring, Jaka Pranita, Elka Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia Politenik Negeri Lampung Informatic Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9798331507930

The quadcopter has a fairly simple mechanical design because it does not require other drives to perform various movements such as roll, pitch, and yaw. The simplicity of this mechanical design has a considerable impact on the complexity of its control. Therefore, it is necessary to have a good control method to maintain the stability of the quadcopter. The research aims to maintain stability of quadcopter by using a combination of the Fuzzy Logic Controller (FLC) with the Proportional-Integral-Derivative (PID) method, where FLC is used to initialize the input of the PID method to find the quadcopter stability point. The application of fuzzy methods for the initial tuning of PID parameters has proven effective in enhancing the system's fundamental performance, such as overshoot, rise time, and settling time, compared to the conventional PID approach. The proposed method can reduce overshoot by over 20%, with reductions reaching up to 88% in yaw motion. Additionally, the Fuzzy-PID approach significantly improves stability, shortening the time to achieve steady-state by less than 1 seconds for all movements while PID did more than 2s for pitch and roll motions and over 1s for yaw motion. © 2024 IEEE.

关键词： Two term control systems

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