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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Hasan, Mhd Arief Sarno, Riyanarto Ardani, M. Syauqi Hanif Intitut Teknologi Sepuluh Nopember Informatics Department Surabaya Indonesia Universitas Lancang Kuning Informatic Engineering Study Program Pekanbaru Indonesia

ISBN: (纸本)9798350399615

In this study, we built an Electronic Nose to detect pineapple ripeness. We designed a prototype E-Nose using a wooden crate. Inside the box, we built the E-Nose using 9 MQ sensor circuits connected to the Arduino Microcontroller. In the crate, we put an object, namely a pineapple with three levels of ripeness (ripe, half ripe, and unripe), each weighing 1kg placed in three different positions (5cm, 20cm, and 35cm) from the position of the sensor array (E-nose). We converted the signal output results into ppm units. We compared the value of each fruit signal based on the level of ripeness and distance using E-Nose. We used several signal processing methods (wavelets) for signal processing. Then, we improved the signal generated using wavelet. The wavelet methods used are Moving Average, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT). The contribution of this research is to see the influence of the signal valuegenerated from the sensor (MQ Sensor) to the position (distance) of the object (fruit) and its influence on the level of maturity (fruit). Then signalgenerated from the sensor we make improvements with the mva, fft, and dwt methods. © 2022 IEEE.

关键词： DWT E-Nose FFT MVA Pineapple

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Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning 2

Effective preprocessed thin blood smear images to improve ma...

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2nd Annual Conference of Science and Technology, ANCOSET 2020

作者： Swastika, W. Kristianti, G.M. Widodo, R.B. Informatics Engineering Study Program Universitas Ma Chung Malang Indonesia

Malaria can be difficult to detect from thin blood smears. Image recognition methods such as convolutional neural network can be used to detect malaria, but the training process takes a long time. Previous research created a new architecture and compares it to several other architectures such as VGG-16 and ResNet. The effect of preprocessing is analyzed in this research. VGG-16, ResNet, and the custom architecture created by the previous research are being used in this study. The preprocessing methods being analyzed in this research include gray-world normalization and comprehensive normalization. The highest accuracy improvement per epoch (0.5256% using ResNet-50 and 0.0352% using custom architecture) is achieved through gray-world normalization, that also improves final accuracy (90.1% using ResNet-50 and 93.1% using custom architecture) when compared to other methods with the same epochs for ResNet and custom architecture. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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Design Technology of Geographical Information System of Rice Pandanwangi Cianjur Prediction Factors Products 1

Design Technology of Geographical Information System of Rice...

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1st Paris Van Java International Seminar on Computer, Science, Engineering, and Technology, PVJ ISComSET 2020

作者： Musrifah, Ai Adikusuma, Andri Sutono Informatics Engineering Study Program Faculty of Engineering University of Suryakancana Indonesia

Cianjur is one of the renowned rice producers, particularly with the presence of Pandanwangi rice that can be grown in the Cianjur area. Pandanwangi has become one of Cianjur City's icons, the rice supremacy created by Pandanwangi Cianjur's tenacity and distinctive scent (pandanus fragrance). The Interest of pandanwangi was very high, both within the town of Cianjur and outside the town of Cianjur, but this was inversely proportional to the desires of the groups of farmers who appeared to prefer to plant rice other than Pandanwangi, on the grounds that the planting of Pandanwangi had a long harvest and that the likelihood of crop failure was also very high. Problems with information flows that are not well integrated between farmers' groups, farmers' group organizations, and the Agriculture Department in adjusting land details, planting time, planting location, estimating total harvest and harvesting time. To solve this problem, a framework must be built that can integrate the information needed for Pandanwangi Cianjur Harvest Prediction with Pandanwangi farmers' regional analysis based on geographic information system technology, using Google Maps Service. © Published under licence by IOP Publishing Ltd.

关键词： Harvesting

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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

作者： Naufal, Muhammad Rizky Sari, Christy Atika Rachmawanto, Eko Hari Handoko, Lekso Budi Isinkaye, Folasade Olubusola Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Ekiti State University Department of Computer Science Ekiti Nigeria Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR. © 2023 IEEE.

关键词： Pixels

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Deep Learning for Cyber Security: Deephoax Detection 2

Deep Learning for Cyber Security: Deephoax Detection

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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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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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ML-Agents in Kart Racing Game using Microgame 17

ML-Agents in Kart Racing Game using Microgame

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Ana, Afina Anfa Pratama, Vitradisa Sukoco, Agus Prihatmanto, Ary Setijadi Nurmalasari, Rin Rin Juhana, Agus Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Mayasari Bakti Departement of Informatics Tasikmalaya Indonesia UIN Sunan Gunung Djati Department of Electrical Engineering Bandung Indonesia Universitas Pendidikan Indonesia Multimedia Education Study Program Bandung Indonesia

ISBN: (纸本)9798350309164

This paper presents an innovative approach to integrating ML-Agents into a microgame-based kart racing environment, showcasing the potential for enhancing gameplay experiences through AI-driven opponents. Kart Unity Game is a racing game that uses small motorized vehicles but still uses the concept of racing in the real world. One of the racing game development platforms is Unity. Unity has developed a platform, Unity Karting Microgame, which users can modify. Unity Karting Microgame applies self-driving to ML-Agents trained and designed to drive their karts with suitable sensors and actuators. The method used to develop the game is to modify the racetrack and modify ML-Agents to make the game more exciting and fun. As a result of this study, successful implementation of ML-Agents in a microgame-based kart racing environment. The study highlights the potential of using Unity ML-Agents to create AI-driven opponents that enrich gameplay experiences. Games can run well and seem varied. ML-Agents can also control their karts on their lanes. Thus, kart racing games can be implemented. © 2023 IEEE.

关键词： Software design

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Artificial Intelligence-Based System ‘SiMoniK’ for MSMEs

Artificial Intelligence-Based System ‘SiMoniK’ for MSMEs

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Information Technology International Seminar (ITIS)

作者： Sri Lestari Yulmaini Aswin Sulyono Ruki Rizal Nul Fikri Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Economics Business Management Study Program Bandar Lampung Indonesia

Artificial intelligence was recently implemented in various fields. One of these applied artificial intelligence systems was the Performance Monitoring System “SiMoniK” for MSMEs. This system was designed through the C4.5 algorithm and the Porat-Rank method. The C4.5 algorithm was applied to classify the MSMEs into three classifications, e.g. the micro enterprise classification, the small enterprise classification, and the medium enterprise classification. Moreover, the Porat-Rank method was also applied to distribute the solving-problem recommendations and to improve the independence of MSMEs. The result of this study was that the C4.5 algorithm could classify the MSME data correctly in the form of an accuracy value of 99.2%. Besides, the Porat-Rank method could also provide recommendations in the form of outstanding quality with an NDCG mean value of 0.87.

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Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

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arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

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Augmented Reality Application for Book Detection with Google Colaboratory and SnapML Lens Studio 8

Augmented Reality Application for Book Detection with Google...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Saputri, Galuh Suwamo, Joko Fuadi, Achmad Lutfi Heri Yunial, Agus Universitas Pamulang Program Study Informatics Engineering Faculty of Engineering Jl.Raya uspitek serpong No. 10 Tangerang Selatan Indonesia

ISBN: (纸本)9781665453899

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. Object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. This research was conducted using Cloud Computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares. © 2022 IEEE.

关键词： Augmented reality

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