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

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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Radiation Pattern Characteristics of 1x5 and 1x6 Planar Microstrip Antennas with Edge Weighting at A Frequency of 2.6 Ghz

Radiation Pattern Characteristics of 1x5 and 1x6 Planar Micr...

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2023 International Conference on Computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Vistalina Simanjuntak, Imelda Uli Yunita, Trasma Chairunnisa, Chairunnisa Kurniawan, Adit Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Mercu Buana University Electrical Engineering Study Program Jakarta Indonesia

ISBN: (纸本)9798350320954

One of the methods used to increase channel capacity is the Non-Orthogonal Multiple Access (NOMA) methods. The increase in channel capacity in PD-NOMA multiple access means that the maximum power used by each user in each narrow band achieves increased power efficiency. Therefore, this research focuses on facilitating the increase in channel capacity by designing an antenna that produces a radiation pattern with many sharp main lobes and high gain. The multi-beam radiation pattern is obtained by using edge weighting. Therefore the antenna is designed with odd and even numbers represented by 1x5 and 1x6 antennas on the microstrip patch rectangular antenna at a frequency of 2.6 GHz. The effect of parasitic and non-parasite effects on the radiation pattern of a planar antenna with edge modeling is manageable in the main lobe, only in the side level lobe, which is not too significant. However, the number of main lobes on the 1x6 antenna has seven beam patterns, and the 1x5 antenna has five. The highest gain value obtains in the 1x6 design with a parasitic of 6.01 dB. This value meets the Gain requirements for 5G cellular systems. © 2023 IEEE.

关键词： Microstrip antennas

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Automated Test on Multiple Platform Framework Development 7

Automated Test on Multiple Platform Framework Development

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Ubaidilah, Muhammad Fadhli Permatasari, Desy Intan Hardiansyah, Fadilah Fahrul Informatics Engineering Study Program Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9781450397117

Software testing is a phase in software development to ensure software quality. As more and more software is developed for web and mobile platforms, Software Quality Assurance (SQA) Engineer is responsible to test software on both platforms to ensure functionality on each platform. While automated test can be implemented to reduce the workload of SQA Engineer, it has its setbacks as automated test needs a huge effort to maintain it and available tools to develop automated test currently only support one platform. In this research, an automated test framework is proposed and developed that can test both web and mobile platforms. Compared to an automated test project that uses separate tools, a project that uses the proposed framework has 2.57% higher maintainability index. A user test to 3 SQA Engineers has also been conducted and the proposed framework has generally been given high maintainability rating. © 2022 ACM.

关键词： Software testing

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Djafar, Imran Indrabayu Syarif, Syafruddin Hasanuddin University Dipa Makassar University Doctoral Program of Electrical Engineering Department Makassar Indonesia Hasanuddin University Makassar Department of Informatics Indonesia Hasanuddin University Department of Electrical Engineering Makassar Indonesia

ISBN: (纸本)9798331513320

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities. © 2025 IEEE.

关键词： Convolutional neural networks

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Revealing a Country’s Government Discourse Through BERT-based Topic Modeling in the US Presidential Speeches 11

Revealing a Country’s Government Discourse Through BERT-bas...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Zain, Rafi Mahmud Anggai, Sajarwo Tukiyat Musyafa, Ahmad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

This paper analyzes presidential speeches from the United States, delivered by the current president, Joe Biden, from his inauguration until April 3, 2024, using BERT-based topic modeling techniques. Since the role and actions of the United States affect global affairs, inferring latent topics from these speeches are important for making informed decisions and also useful to researchers. We provide experiments combining different embedding methods and clustering algorithms for dimensionality reduction. Our experiment results demonstrated that using DistilBERT embeddings followed by HDBSCAN clustering and UMAP dimensionality reduction consistently produced meaningful and interpretable topics, and this paper introduces the UMASS and NPMI metrics for evaluating topic coherence, with the best values of these metrics among the other schemes investigated. Some of the dominant themes conveyed in the American president’s state of the union speech that were successfully revealed in this study include international cooperation, alongside significant attention to environmental, community, health, and economic issues. © 2024 IEEE.

关键词： Embeddings

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Development of Urban Data Sensing for Smart City Platform 10

Development of Urban Data Sensing for Smart City Platform

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Prabowo, Okyza Maherdy Supangkat, Suhono Harso Mulyana, Eueung Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung STMIK AMIK Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Smart City and Community Innovation Center Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350339543

The idea of a 'smart city' is one in which cities may effectively and efficiently manage their resources to raise the standard of living of its residents. Cities are already capable of performing sensing, understanding, and acting at a certain degree because to the existence of information technology as an enabler. Several studies have created various physical sensors for sensing in order to produce data. However, urban data is not limited to factual information on the environment and infrastructure. In order to collect data from the digital world, including social media, online news, and other digital social sensors, this research aims to create an urban data collecting system. The creation of digital urban sensor data can enhance the accuracy of the information that can help decision-makers. © 2023 IEEE.

关键词： Smart city

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Smart Electrical Devices Control with Intrusion Detection Alert

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第5期16卷 139-152页

作者： Achmad, Andani Muliadi Areni, Intan Sari Palantei, Elyas Hadis, Muhammad Sabirin Department of Electrical Engineering Faculty of Engineering Universitas Hasanuddin Gowa Indonesia Department of Electrical Engineering Education Faculty of Engineering Universitas Negeri Makassar Indonesia Study Program of Informatics STMIK AKBA Makassar Indonesia

The consumption profile of electrical energy always increases every year due to the highly consumptive and inappropriate lifestyles of modern society. A bad lifestyle such as forgetting to turn off the unused electronic devices when they are leaving home is one of the factors influenced the increasing consumption of electrical energy. This is off course contributing in wasting energy. In the paper, an IoT-based electronic device control system has been successfully constructed. Monitoring and control tasks could be performed remotely and in real time mode using an interface application installed on a smartphone. The system built is equipped with a device operational condition monitoring featured by placing light, temperature, thermal and current sensors, respectively. Through the utilization of the implemented system, users can monitor and control electronic devices easily and can figure out the condition of the device remotely in a real time mode. The control test on the system was successful with an average response time of approximately 3.32 seconds and the accuracy of sensor readings above 89%. With the implementation of this system, users can easily monitor and control electronic devices anywhere and anytime so that the opportunities for efficient and effective use of electrical energy could be further optimized. © 2022. All Rights Reserved.

关键词： Energy efficiency

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory 10

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Sugiarto, Dedy Utami, Ema Yaqin, Ainul Raharjo, Suwanto Universitas Trisakti Faculty of Industrial Technology DKI Jakarta Indonesia Universitas Amikom Yogyakarta Master Program of Informatics Engineering Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350329919

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as 'mafia', 'mahal', 'langka', 'tangkap', 'bongkar', and 'mendag' could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions. © 2023 IEEE.

关键词： Sentiment analysis

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