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Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

Regional Classification of Indonesian Folk Songs Based on K-...

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2023 International Conference on computer Science, Information Technology and Engineering, ICCoSITE 2023

作者： Mahardhika, Ferdinand Warnars, Harco Leslie Hendric Spits Nugroho, Anto Satriyo Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Organisasi Riset Elektronika Dan Informatika Jakarta10340 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

One of the biggest challenges in Music Information Retrieval (MIR) is classification. MIR is a subset of Machine Learning (ML), where the methods found in computer science can be used to solve problems in the music domain area. Indonesia is an archipelago country inhabited by around 1,340 ethnic groups spread across 38 provinces. This ethnic group has a variety of cultures, including folk songs, and almost every area in Indonesia has folk songs. In this study, we conducted a regional classification of 500 Indonesian folk songs from 10 regions in Indonesia. We build a model for regional classification tasks using the MIR approach. This model combines feature selection and dimension reduction capabilities. By using hyperparameter tunning and a cross- validation of 5 folds, the result of our proposed model for regional classification of Indonesian folk songs using K-NN with PCC-LDA can obtain an accuracy rate of 84.7%. © 2023 IEEE.

关键词： Music

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Comparison of LSTM and Transformer for Time Series Data Forecasting

Comparison of LSTM and Transformer for Time Series Data Fore...

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International Conference on Informatics and Computational Sciences (ICICoS)

作者： Ilvico Sonata Yaya Heryadi Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather data, stock prices, and sales data are examples of time series data. Time series data analysis can be used to predict future conditions based on patterns and values from previous data through a forecasting process. These forecasting results are useful for identifying trends and data patterns for decision making. The rapid development of deep learning models can now be used as a method for forecasting time series data. Deep learning models that are suitable for forecasting include sequence-to-sequence models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Transformer. LSTM and Transformer are sequence-to-sequence models that are widely used for forecasting time series data. Research to compare the accuracy of the two models is still very limited. This article will discuss the use of the LSTM and Transformer models for the time series data forecasting process using Hewlett Packard stock price data from 1962 to 2022. The accuracy results show that the LSTM model outperforms the Transformer model in forecasting Hewlett Packard stock prices.

关键词： Accuracy Recurrent neural networks Biological system modeling Time series analysis Predictive models Transformers Data models

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Developing an online information portal for enhancing society awareness of RPTRA (A case study RPTRA MAYA ASRI 13) 7

Developing an online information portal for enhancing societ...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Arifin, Yulyani Gunawan, Elizabeth Paskahlia Ohyver, Margaretha Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The long COVID-19 pandemic has limited the activities of Ruang Publik Terpadu Ramah Anak (RPTRA) such as environment cleaning, repair of RPTRA infrastructure, learning and others, as well as the lack of public awareness about the existence and importance of RPTRA to the public and foreign tourists encouraging the RPTRA to make changes in the dissemination of information about the activities carried out in the RPTRA through the use of technology to survive and grow even during the Covid-19 Pandemic. The methodology used in this research is Group Discussion Forum with the administrators of the RPTRA Maya ASRI 13 to learn about the needs and expectations of the RPTRA administrators regarding the web portal to be built. Then, the implementation of the conceptual model, development, and evaluation of the effectiveness of the RPTRA information web portal was carried out. The contribution in this research is to implement a conceptual model on the information portal built on the platform of the Website with the PHP framework, which is then evaluated on the results of the pre-test and post-test about RPTRA the Wilcoxon-Signed Rank Test to test the effectiveness of the information web portal for general users. The results obtained from these tests indicate that the RPTRA Information Web Portal can help the public learn more about the existence of RPTRA Maya Asri 13. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Portals

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E-Govchurch as a Digital Transformation Initiative for Nation-wide Christianity Organization 1

E-Govchurch as a Digital Transformation Initiative for Natio...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Paramita, Adi Suryaputra Prabowo, Harjanto Ramadhan, Arief Sensuse, Dana Indra Graduate Program Universitas Ciputra Surabaya Bina Nusantara University Surabaya Indonesia/Jakarta Information Systems Department/Doctor Of Computer Science Indonesia Doctor Of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The process of digital transformation is part of the 4.0 industrial revolution. Through digital transformation, it is hoped that all processes inside an organization will function more efficiently. Internet technology is the major driver of an organization's digital transformation effort. Information Systems for the statewide scope of the two main religion-based organizations in Indonesia are required to satisfy the Indonesian government's National Medium-Term Development Plan for the years 2020-2024 in accordance with Indonesian President Decree No. 18 of 2020. The regulation mandates that the ministry of religious affairs provide information and data services. E-Govchurch, an abbreviation for Electronic-Governmental Church, is the suggested framework. E-Govchurch is a digital program for Information Systems that incorporates a framework for data governance. E-Govchurch will develop utilizing SSM, RBV, the Zachman Framework, and the DAMA framework. The purpose framework in research will lead the next phase of framework development process. © 2022 IEEE.

关键词： Information systems

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Question Classification of University Admission using Named-Entity Recognition (NER) 10

Question Classification of University Admission using Named-...

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Yossy, Emny Harna Suhartono, Derwin Trisetyarso, Agung Budiharto, Widodo Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Mathematics Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users' most frequent question type is 'What' by 63.3%, and the answer category is 'tuition fees' by 22.4%. It can be concluded that the most frequent question type asked by users is 'What', and the most frequent topic of interest is 'tuition fees'. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees. © 2023 IEEE.

关键词： Natural language processing systems

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer Science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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Spatial Temporal Changes in Mangrove Forests on Tarakan Island, North Kalimantan, Indonesia 3rd

Spatial Temporal Changes in Mangrove Forests on Tarakan Isla...

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3rd International Conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Giofandi, Eggy Arya Sekarjati, Dhanu Arshad, Mazlan Rahman, Henzulkifli Tjahjono, Boedi Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Graduate Program of Coastal and Ocean Resource Management Faculty of Fisheries and Marine Science IPB University Bogor Indonesia Graduate Program of Tropical Biodiversity Conservation Faculty of Forestry and Environment IPB University Bogor Indonesia Department of Soil Science and Land Resources Faculty of Agriculture IPB University Bogor Indonesia

ISBN: (纸本)9789819707393

Mangroves play an important role in providing essential ecosystem services and resources for fisheries, offering protection against climate change and coastal erosion. Understanding the changes in mangrove forests is essential for the sustainability of the coastal ecosystem. The study aims to analyze the spatiotemporal changes of mangrove forests in Tarakan Island using Sentinel-1 and Sentinel-2 images from 2018 and 2023. The analysis shows changes in land cover, with an increase in aquaculture land and a decrease in other land types. The results of the accuracy assessment show good results, with an accuracy value of 0.82 and a Kappa value of 0.78 in the mangrove areas in 2023. These values indicate that the classification of land cover in the study area was reliable and accurate. The conversion of mangrove forests into aquaculture land poses a challenge for their conservation. Therefore, preserving and conserving mangrove forests in the study area is essential for maintaining the ecological balance and supporting the livelihoods of local communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aquaculture

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Seamless High Availability in MySQL Database Systems: An Advanced Implementation of ProxySQL, Orchestrator, and Keepalived

Seamless High Availability in MySQL Database Systems: An Adv...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jeremy Loa Benfano Soewito Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

High availability (HA) in MySQL databases remains a critical challenge, particularly due to risks such as single point of failure (SPOF) and limited scalability in existing setups. Previous studies have explored various approaches to address these issues. One study implemented ProxySQL and Orchestrator for query routing and failover but relied on a single server instance, creating a SPOF that could disrupt client access during failures. Another study implemented HAProxy to balance traffic and couple database servers with specific web applications. While effective for certain use cases, this approach reduced resource efficiency and scalability, because it relies on HTTP protocol and database server are coupled to each application, making it less suitable for dynamic, shared environments requiring high flexibility. These limitations highlight the need for a more robust and scalable HA solution. This paper proposes an improved HA architecture that eliminates SPOFs and enhances scalability by combining redundant ProxySQL instances synchronized with Keepalived managed Virtual IP (VIP) and automated failover using Orchestrator. The architecture allows shared database resources across multiple applications while decoupling them from specific application servers. Test results confirm the system's ability to maintain seamless failover, consistent query routing, and uninterrupted client access through dynamic Virtual IP reassignment to backup server. By addressing the issues of previous studies, this solution provides a practical and scalable framework to achieve high availability in the MySQL cluster by combing ProxySQL, Orchestrator, and Keepalived, making it suitable for modern enterprise environments.

关键词： Costs Databases Scalability Query processing Robustness Servers IP networks Synchronization Stress Testing

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Hyperparameter Tuning of U-Net for Active Fire Segmentation in Peatlands of Indonesia Using Landsat 8 Data

Hyperparameter Tuning of U-Net for Active Fire Segmentation ...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Irsyad Kharisma Sani Muhamad Isa Computer Science Binus Graduate Program Universitas Bina Nusantara Jakarta Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This study focuses on fire segmentation in peatland areas using the U-Net model, involving hyperparameter tuning combined with data masking developed using a modified ToPeCAl algorithm. The data masking process utilizes TIRS-1 band, band 7, and band 6, while the training data consists of composite images from bands 7, 6, and 4 (764) and 7, 6, and 2 (762) for comparison. Input data includes image patches sized 128×128, 256×256, and 512×512, derived from the 764 and 762 composites, respectively. The study reveals that the use of the leakyReL U activation function, a dilation rate strategy, a learning rate schedule, 50% image patch overlap, and a patch size of 128×128, improves the model's performance for fire segmentation compared to the baseline model. The final evaluation metrics demonstrate Precision, Recall, F1 Score, and IoU values of 88.85%, 89.70%, 89.08%, and 77.36%, respectively.

关键词： Measurement Image segmentation Schedules Training data Landsat Data models Informatics Tuning

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Combining DistilBERT and LSTM to Enhanced Detection of Depression in Social Media Text 9

Combining DistilBERT and LSTM to Enhanced Detection of Depre...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Kurniadi, Felix Indra Satyaning Pradnya Paramita, Ni Luh Putu Angela Sihotang, Erna Fransisca Anggreainy, Maria Susan Zhan, Rongfang School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science University of North Texas Department of Rehabilitation and Health Service United States

ISBN: (纸本)9798350390025

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data. © 2024 IEEE.

关键词： Social networking (online)

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