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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Marthana Yusa, I Made Adi Sudi Anggara, I Gede Darma Yasa, Ngakan Putu Novitasari, Dwi Widhi Adnyana, I Nyoman Institut Bisnis Dan Teknologi Indonesia Visual Communication Design Denpasar Indonesia Multimedia Design Subject Institut Bisnis Dan Teknologi Indonesia Study Program of Informatics Technology Denpasar Indonesia

ISBN: (纸本)9798350365191

This paper examines the integration of Artificial Intelligence (AI) in the digital creation of Balinese dance animations, with a specific focus on the Hanoman dance character as the case study. Hanoman dance as a part of Balinese dance, known for its intricate movements, unique costume and cultural significance, presents a unique challenge for digital artists. AI technologies, such as machine learning and computer vision, offer novel approaches to capturing and animating these complex dance forms. This study examines current AI applications in this domain, assesses their effectiveness, and discusses the potential advancements and limitations. Through the use of rotoscoping and motion capture techniques, this study adeptly examines the intricate movements of the Hanoman dance, an integral part of Balinese dance heritage, employing text-to-animation and AI models for video footage motion capture. The thorough processes of data collection, preprocessing, and model training emphasized in the study highlight the necessity of a meticulous methodology. Furthermore, incorporating expert feedback during the validation and refinement phases ensures that the digital representations are both authentic and expressive. The study concludes that AI technologies can play a crucial role in preserving Balinese dance, offering educational tools and digital archives that uphold the cultural integrity of this traditional art form. © 2024 IEEE.

关键词： Animation

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Natural Language Interface to Database (NLIDB) for Interrogative Sentences with Temporal Elements in Indonesian Language

Natural Language Interface to Database (NLIDB) for Interroga...

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2022 International Conference on Data and Software Engineering, ICoDSE 2022

作者： Daniel Thamrin, Hansel Grady Widagdo, Tricya Esterina Undergraduate of Informatics Study Program Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

Natural Language Interface to Database (NLIDB) is a solution that allows users to retrieve data from databases in natural language. Various studies were conducted to translate Indonesian sentences into database queries, but these studies still have weaknesses. In this study, we propose the translation of interrogative sentences in the temporal database accompanied by the addition of several components that can improve the ability of NLIDB. The translation process is carried out by first masking the input sentence and decomposing the sentence into a collection of syntax trees. Then, the existing parsing tree is selected and the best parsing tree will be selected. Then, the process is continued with the analysis of the query components in the interrogative sentences. The results of the analysis will be translated into parts of database query and parts of database query will be rearranged into database queries. This query will be executed and the result can be evaluated by the user and the user can provide feedback according to the system output. The built NLIDB system can translate interrogative sentences for what, who, when, where, how, if, and how many questions with the types of queries that can be handled are current state, time-slice, sequenced, and non-sequenced. © 2022 IEEE.

关键词： Syntactics

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U-Tapis Melting Words : an Artificial Intelligence Application for Detecting Melt Word Errors in Indonesia Online News

U-Tapis Melting Words : an Artificial Intelligence Applicati...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Kevin Gleneagles Marlinda Vasty Overbeek Niknik Mediyawati Rudi Sutomo Samiaji Bintang Nusantara Informatics Study Program Universitas Multimedia Indonesia Tangerang Indonesia Digital Journalism Study Program Universitas Multimedia Nusantara Tangerang Information System Study Program Universitas Multimedia Nusantara Tangerang

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Rapidly advancing NLP technology can assist journalists in correcting linguistic errors, including melt words, which frequently occurs in news writing. A software application called U-Tapis is being developed to assist in correcting spelling mistakes. However, U-Tapis currently lacks the feature to fully correct misspelled words. Therefore, one of the developments being pursued is a Machine Learning model utilizing the Damerau-Levenshtein Distance algorithm and BERT to detect and rectify these errors with high accuracy. Testing of this model yielded F1 Scores of 96.79%, 96.01%, and 96.01% for evaluations on 100, 150, and 200 Tribunnews articles, respectively. The percentages of correct recommendations provided by the model were 88.89%, 85.23%, and 84.68% for the same sets of articles. Therefore, this research is expected to detect errors in word softening and provide appropriate word recommendations, thereby maintaining the quality of language used by journalists and the credibility of a media outlet, which can serve as a model for society.

关键词： Seminars Accuracy Machine learning algorithms Software algorithms Writing Media Software Natural language processing Softening Testing

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Personality Analysis Based on The Handwriting Shape using Integration of Image Processing and Machine Learning Algorithm

Personality Analysis Based on The Handwriting Shape using In...

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Conference on New Media Studies (CoNMedia)

作者： Rosalina Genta Sahuri Hasanul Fahmi Joe Wesley Moerland Rila Mandala Informatics Study Program President University Indonesia Information System Study Program President University Indonesia Magister of Informatics Study Program President University Indonesia Faculty of Computing President University Indonesia

In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful and peaceful, but research shows that body language and voice tone can reveal a person's inner psychological state. Similarly, handwriting can be analyzed through graphology to reveal personality traits such as honesty, emotional stability, and fears. Graphology is a time-consuming and expensive process, using image processing and machine learning, graphology can be done quickly without human bias. This research aims to predict the personality traits by implement image processing combined with machine learning to analyze handwriting features. Using lazy learner algorithm of K-Nearest Neighbor algorithm, SVM and Naïve Bayes, the application can predict the MBTI personality from handwriting image with an accuracy of 94%, 78%, and 93% respectively. From the experiment, it is shown that the application able to do the analysis of one's handwriting and then predict the personality traits, fully automated using a computer

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. 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 Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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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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Determination of bulog regional sub-division in east java using connected domination number theory 5

Determination of bulog regional sub-division in east java us...

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5th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2021

作者： Umilasari, Reni Saifudin, Ilham Study Program of Informatics Engineering Universitas Muhammadiyah Jember Jl. Karimata 49 Jember68121 Indonesia

Bulog is a state-owned public company engaged in food logistics. There are about 18 Bulog warehouses in East Java Province with an uneven distribution. Because the scope of Bulog's business includes logistics or warehousing, surveys and pest eradication, provision of plastic bags, transportation business, food commodity trading and retail business, there must be good coordination between regional sub-divisions and warehouse complexes. In this study, the minimum regional sub-division recommendations in the East Java region were determined using the theory of connected domination number in graphs. The location of the warehouse complex is described as the vertex and adjacent warehouse complexes are connected by edges. In this research, we also determine the connected domination number of unicyclic graph and vertex amalgamation operations for several classes of graphs. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Metals

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Convolutional Neural Network for Cataract Clasification Using Primary Dataset

Convolutional Neural Network for Cataract Clasification Usin...

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International Conference on Business and Industrial Research (ICBIR)

作者： I Gede Totok Suryawan I Wayan Murmayasa Adi Putra Informatics Engineering Study Program Faculty of Technology and Informatics Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

Cataracts are the main factor causing blindness in Indonesia. Traditional diagnostic methods involving clinical examination by an ophthalmologist using a slit-lamp and visual tests have many challenges such as limited resources, time, and medical expertise may not be available in all locations, especially in less developed areas. Utilizing medical image technology is a wise choice because it allows early detection and monitoring of the development of this cataract disease Technology is needed to identify medical images that have valuable information so that early detection or monitoring of cataract development can be carried out. CNN as a part of deep learning has the ability to perform automatic feature extraction so that it can identify eye images. A CNN model for recognizing cataracts in eye images taken directly from Bhayangkara Hospital in Denpasar Bali has been developed in this research. The model developed is equipped with a dropout layer technique to prevent overfitting. The test results show that the CNN developed in this research produces very good performance with an average accuracy level of 92% on public dataset images, and 94% on primary dataset images. Tested using the primary dataset and public dataset, this CNN model shows very good performance because it can correctly identify an average of 94% of eye images in the primary dataset, and 92% of eye images in the public dataset..

关键词： Cataracts Training Visualization Image recognition Accuracy Convolutional neural networks Medical diagnostic imaging Monitoring Testing Overfitting

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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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Design Enterprise Architecture Using the TOGAFADM Framework to Support Academic Information Systems at Nusa Nipa University 1

Design Enterprise Architecture Using the TOGAFADM Framework ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Darkel, Yohanes Brekmans M. Emanuel, Andi W. R. Mudjihartono, Paulus Informatics Engineering Study Program Universitas Nusa Nipa Faculty of Engineering Indonesia Fakultas Teknologi Industri Universitas Atma Jaya Yogyakarta Departemen Informatika Yogyakarta Indonesia

ISBN: (纸本)9798350306156

Information technology support is desperately needed for higher education. Universities must use information technology to effectively and efficiently manage potential resources to compete with other institutions. Utilization of IT in Higher Education Governance, particularly the academic information system at Nusa Nipa University, lacks a structured architecture. The current IT infrastructure is insufficient to respond promptly and precisely to application and business change solutions. This study aims to simplify the process of developing information system architecture by developing an architectural model and an implementation roadmap. This study's design uses the TOGAF ADM model, The organization's initial condition analysis stage is the preliminary requirement management phase;architectural vision;and business architecture. The enterprise architecture design begins with the second stage's information system architecture and technology architecture phases. The subsequent phases of opportunities and solutions, migration planning, and change management, recommendations for implementing enterprise architecture are presented in the third section. The consequences of this study are IT outlines for each engineering stage given the TOGAF ADM structure for business processes in new understudy affirmations frameworks, scholastic cycles, graduated classes, and libraries to help data frameworks at Nusa Nipa College. This research can create a blueprint for IT development suggestions for expected business techniques at Nusa Nipa College. © 2023 IEEE.

关键词： Blueprints

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