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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Yakub, Sy. Yuliani Wowor, Rivo Juicer Agustriawan, David Irmawati Kristiyanti, Dinar Ajeng Winarno, P.M. Hassan, Rosilah Mohamad, Roshayu Razzaq, Randy Zahran Universitas Multimedia Nusantara Department of Informatics Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Uin Sultan Maulana Hasanuddin Department of Informatics Banten Indonesia

ISBN: (纸本)9798331506995

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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Detect Areas of Upward and Downward Fluctuations in Bitcoin Prices Using Patterned Datasets

Detect Areas of Upward and Downward Fluctuations in Bitcoin ...

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Information Technology International Seminar (ITIS)

作者： Rizky Parlika Mustafid Basuki Rahmat Department of Doctoral Program of Information System Pascasarjana School Diponegoro University Semarang Indonesia Department of Informatics Faculty of Computer Science University of Pembangunan Nasional “Veteran” JawaTimur Surabaya Indonesia

Many researchers have proposed various theories and techniques to estimate the value of Bitcoin. However, there is still a great opportunity to suggest a new approach to Bitcoin forecasting. This research will examine the concept of forming a patterned dataset to prove the hypothesis regarding the start of a bitcoin price fluctuation until the end of the fluctuation using patterned dataset pairs in conditions of price decline (moon) and in conditions of price decline (crash). Patterned datasets were initially built from data on the movement of all cryptocurrency assets on a digital cryptocurrency trading market. The Patterned Dataset is programmed based on the response of all crypto assets to changes in the price of bitcoin over time. Patterned datasets are better able to describe the conditions of the digital cryptocurrency market, along with patterns of potential increases and decreases in Bitcoin prices in particular and cryptocurrencies in general. To deepen the observations, resampling of the patterned dataset was carried out with a 4-hour timeframe using the mean, median, min, max, and sum filter functions on pairs of crash and moon conditions per month. The results show that filter functions other than sum, namely mean, median, min, and max, provide markings of fluctuation area patterns that are easier to recognize. Two hypotheses related to fluctuations were also successfully proven in this research.

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Catur Supriyanto Abu Salam Junta Zeniarja Adi Wijaya Bachelor Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Health Information Management Universitas Indonesia Maju Jakarta Indonesia

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset’s size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection.

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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On the Evaluation of Access-point Handovers for UAVs in Long-distance Missions

On the Evaluation of Access-point Handovers for UAVs in Long...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Fagundes, Leonardo A. Souza, Vitor Barbosa Brandao, Alexandre S. Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa Brazil Federal University of Viçosa Graduate Program on Computer Science Department of Informatics Viçosa Brazil

ISBN: (纸本)9780738131153

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Control Stations (GCS). Due to the complexity and difficulty of performing long distance experiments with these robots, simulation becomes a viable and necessary alternative. However, focusing either on the modeling of UAV operations or communication dynamics, available simulation tools are not able to capture the complex interdependencies between these two aspects, especially in applications where extensive communications are required. The main contribution of this work is the modeling and implementation of strategies for enhancing connectivity of a UAV performing long-distance navigation tasks, taking into account applications' requirements. The proposed strategies aim at assessing the impact of frequent handovers between wireless access points in terms of packet loss and available bandwidth. The first strategy prioritizes maintaining a connection until the signal-to-noise ratio (SNR) observed by the robot reaches a predefined threshold. For this, it is assumed that the data to be transmitted is small enough so that the robot may change its modulation to obtain smaller bandwidths while improving connection time. The second one prioritizes bandwidth availability, thus, it always searches for a network that is able to offer data rates higher than a minimum threshold. A hybrid model is validated by considering the characteristics of each individual strategy. The capabilities of the simulation tool designed for this work are illustrated through some case-study scenarios. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest

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IEEE Access 2025年 13卷 84713-84726页

作者： Dias, Jean A. C. Guimarães, Pedro H. Do V. Pereira, Williane G. S. Tamasauskas, Leonardo De O. Gomes, Marivan S. Correa, Alan B. S. Figueiredo, Karla Costa, Gilson Costa, Gabriel Brito Costa, Fernando A. R. Seruffo, Marcos C. Da R. Operational Research Laboratory Belém66075-110 Brazil School of Technology Amazonas 69850-000 Brazil Belém66075-110 Brazil Institute of Mathematics and Statistics Department of Informatics and Computer Science Rio de Janeiro20550-013 Brazil Institute of Biological and Health Sciences II Department of Genetics Rio de Janeiro20550-013 Brazil Santarém68035-110 Brazil Federal University of Rio Grande do Norte Climate Sciences Post-Graduate Program Rio Grande do Norte Natal59078-970 Brazil Pará Santarém68035-110 Brazil Post-Graduate Program in Forest Science Technology and Innovation-PPGCTIF Pará Santarém68035-110 Brazil Pará Santarém68035-110 Brazil Pará Belém66075-110 Brazil Pará Castanhal68746-630 Brazil

This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and R2 =0.39 , whereas the LSTM alone yielded r =0.26 and R2 = –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon. © 2013 IEEE.

关键词： Carbon sequestration

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Scientometric Analysis of Artificial Intelligence Research in Agriculture 4

Scientometric Analysis of Artificial Intelligence Research i...

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Pramono, Andi Hamim, Miftahul Prihatin, Sukeipah Yuli Arifuddin, Rahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Politeknik Kota Malang Informatics Study Program Malang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ($\mathrm{N}=287$), followed by the United Kingdom at second ($\mathrm{N}=139$) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index. © 2022 IEEE.

关键词： Agriculture

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Forecasting Raw Material Inventory Using the Single Moving Average and Supplier Selection Using the Analytical Hierarchy Process

Forecasting Raw Material Inventory Using the Single Moving A...

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2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021

作者： Sinaga, Ardiles Astuty, Eriana School of Computer Science Bina Nusantara University Informatics Department Bandung Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Bandung Indonesia

ISBN: (纸本)9781665424820

This study aims to help companies to be able to estimate the procurement of raw materials for production, not having stock of raw materials in the warehouse, and determine which suppliers can send goods quickly with affordable prices and good quality. Thus, the company is not optimal in the procurement of raw materials and the selection of appropriate raw material suppliers. In addition, the research aims to optimize the procurement of raw materials that can be used in forecasting in controlling raw materials and decision support systems for the selection of raw material suppliers. The method used to estimate the availability of raw materials is the Simple Moving Average and Analytical Hierarchy Process (AHP) method to determine suppliers. The results of this study indicate that the average percentage of forecast error is 4.17%. Whereas for supplier selection, the AHP method that is used can recommend which suppliers are eligible to be chosen based on predetermined criteria, then the results are sorted by highest ranking. It is expected that the results of this study can be used as a reference for further development, for example by creating information systems that can help companies. © 2021 IEEE.

关键词： Forecasting

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Automatic Aircraft Shadow Removal from Remote Sensing Images Using Mask-ShadowGAN 8

Automatic Aircraft Shadow Removal from Remote Sensing Images...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Ganyaporngul, Sirapavee Cooharojananone, Nagul Kruachottikul, Pravee Trakulwaranont, Donnaphat Satoh, Shin'ichi Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781665428958

Objects with shadow may cause a problem for image classification. For example, it can separate one object into many objects. It can also alter the size or shape of the object resulting in misclassification. In this paper, we focus on removing aircraft shadow from remote sensing images where the shadows occur on wings, bodies, and tails. Since it is very difficult to get shadow-free aircraft images and a shadow aircraft image of the same type for the training part, we adopted Mask-ShadowGAN for solving this issue. The benefit of the Mask-ShadowGAN algorithm is that, in the training part, the technique does not require the same images that have both shadow and shadow-free. In the experiment, we evaluated our proposed technique using RMSE and Jaccard similarity index for measurement. The experimental result shows that our technique shows promising results. We present both best and worst result based on sorted similarity index. © 2021 IEEE.

关键词： Remote sensing

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5S towards sustainable competitive advantage in franchise retail business 2

5S towards sustainable competitive advantage in franchise re...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Astuty, E. Sinaga, A. Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Bandung Indonesia Informatics Department School of Computer Science Bina Nusantara University Bandung Indonesia

This study aims to find empirical evidence in filling the "how-to"gap of a service company in order to have a sustainable competitive advantage by adopting lean system or known as a workplace organization. This study surveyed 150 customers from three largest franchise minimarts in Bandung City in order to determine the characteristics and interrelations that occur in the three research objects observed, namely 5S or workplace organization, service's waste, and service quality. The method used is descriptive method and structural equation modeling based on the least squares method or PLS-SEM. The statistical tools used are SPSS20 and SmartPLS3. The results reveals that the three largest franchise minimarts in Bandung City, had a high level of service quality, low service's waste, and well-managed workplace organization. The relationship between them shows that the three franchise minimarts continue to survive and continue to grow after operating for more than 3 decades, because they implemented a workplace organization (5S, seiri-seiton-seiso-seiketsu-shitsuke), so that this can reduce the occurrence of service's waste by 82.4 %, where even this has an impact on increasing service quality by 27.9% with a very high model strength of 73.5%. © Published under licence by IOP Publishing Ltd.

关键词： Quality of service

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