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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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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection 12th

The Effect of Statistical Hypothesis Testing on Machine Le...

引用

12th Brazilian Conference on Intelligent Systems, BRACIS 2023

作者： Gonçalves, Marcel Chacon Silva, Rodrigo Graduate Program on Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil

ISBN: (纸本)9783031453885

Statistical tests of hypothesis play a crucial role in evaluating the performance of machine learning (ML) models and selecting the best model among a set of candidates. However, their effectiveness in selecting models over larger periods of time remains unclear. This study aims to investigate the impact of statistical tests on ML model selection in sequential experiments. Specifically, we examine whether selecting models based on statistical tests leads to higher quality models after a significant number of iterations and explore the effect of the number of tests performed and the preferred statistical test for different experimental time horizons. The study on binary classification problems reveals that the use of statistical tests should be approached with caution, particularly in challenging scenarios where generating improved models is difficult. The analysis demonstrates that statistical tests may impede progress and impose overly stringent acceptance criteria for new models, hindering the selection of high-quality models. The findings also indicate that the dominance of versions without statistical tests remained consistent, suggesting the need for further research in this area. Although this study is limited by the number of datasets and the absence of pre-test assumption verification, it emphasizes the importance of understanding the impact of statistical tests on ML model selection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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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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Simple Fuzzy Decision Support Model for Evaluating the Cryptocurrency's Performance

Simple Fuzzy Decision Support Model for Evaluating the Crypt...

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2022 International Conference of science and Information Technology in Smart Administration, ICSINTESA 2022

作者： Utama, Ditdit Nugeraha Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665472883

Evaluation of cryptocurrency's performance is performed questionably. There is no role for computer-model, make such an evaluation process does not have guidance. In this study, a simple decision support model (DSM) based on fuzzy logic was academically constructed to observe the cryptocurrency's performance. By operating the primary method of fuzzy logic and taking into account three types of parameters (i.e. time-series close price data, daily max-min price, and transaction number), a novel DSM for evaluating the cryptocurrency's performance was fruitfully executed. Based on three types of real cryptocurrency six-month data (i.e. Bitcoin Ethereum, and Dogecoin), the model could irreversibly expose that Bitcoin has the best performance with 36.39 performance points. © 2022 IEEE.

关键词： Fuzzy logic

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Automation testing using silk test workbench for website 7

Automation testing using silk test workbench for website

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

作者： Parapat, Feliks Abedyoga Chandra Kusuma, Gede Putra Majiid, Muhammad Rizki Nur Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Automation testing is essential to carry out functional testing quickly and precisely. Software testing is beneficial for testers doing many testing processes according to the existing scenarios. So, there is an urgency to explore a more advanced solution to replace the traditional approach. Silk Test Workbench is a tool for automation testing that can accelerate the functional testing of complex applications. For this research, we proposed automation testing on website applications with various testing scenarios that have been made. The visual test recording method is used for automation testing with Silk Test Workbench. Silk Test Workbench can speed up testing time, make a good test asset testing scenario, make it easier for users to do testing, and speed up the process to get the result in the form of a screen capture of the testing process. A comparison of testing time has been performed. The automatic testing time with the scenarios is 2 hours 46 minutes. The automatic testing time will be 2 hours 36 minutes using the all-in-one scenario. This study also used questionnaires from colleagues in the Quality Assurance field to get maximum User Acceptance Test results. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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Serverless utilization in microservice e-learning platform 7

Serverless utilization in microservice e-learning platform

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

作者： Nday, Bryan Andreas Kusuma, Gede Putra Fredyan, Renaldy Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances. © 2022 Elsevier B.V.. All rights reserved.

关键词： Infrastructure as a service (IaaS)

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SIS-ASTROS: An Integrated Simulation Environment for the Artillery Saturation Rocket System (ASTROS) 1

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12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2022

作者： Pozzer, Cesar Tadeu Martins, Joao Baptista Fontoura, Lisandra Manzoni Silva, Luis Alvaro Lima Rutzig, Mateus Beck Nunes, Raul Ceretta de Freitas, Edison Pignaton Graduate Program in Computer Science Federal University of Santa Maria Av. Roraima n. 1000 Santa Maria97105-900 Brazil Graduate Program in Computer Science Federal University of Rio Grande do Sul CP 15064 Porto Alegre91501-970 Brazil

ISBN: (数字)9783031438240

ISBN: (纸本)9783031438233

Simulation is an important technique to support military training. There are different training goals depending on the different ranks of the military personnel being trained, which are addressed by different types of simulation (live, virtual, or constructive). However, a trend in current military training is the combination of different types of simulation in an integrated setup. Observing such kind of blended simulation, the Brazilian Army is making efforts to develop integrated simulation solutions. This work describes the conception of an integrated simulation environment for the Brazilian Army Artillery called SIS-ASTROS. Besides integrating different types of simulators, SIS-ASTROS presents a virtual tactical simulator to train ASTROS artillery batteries deployment activities of mid-rank officers. This simulator addresses aspects that can be classified as constructive, but also virtual ones. Due to this particularity in its design, the conception of this simulator represents an innovative contribution. Moreover, its development demanded solutions in the area of artificial intelligence, computer graphics, and distributed systems applied to simulation problems. This paper presents the key components of the integrated simulation system, highlighting the main contributions in the research and development of the virtual tactical simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Personnel training

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Recommendation System Model for Personalized Learning in Higher Education using Content-Based Filtering Method 7

Recommendation System Model for Personalized Learning in Hig...

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

作者： Julianti, M. Ramaddan Heryadi, Yaya Yulianto, Budi Budiharto, Widodo BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

- An online learning system is a learning approach that typically uses a one fit for all approach in which all student abilities are considered the same so that the provision of learning materials provided is the same for all students. In contrast, a personalization learning system is a teaching and learning approach in which students are positioned as the learning centers and the students are motivated and facilitated to overcome their learning needs based on their learning speed and understanding of the learning process carried out to achieve. In the context of learning, learning materials and learning objectives are the main components that are interrelated in achieving the expected learning outcomes. However, the relationship between learning materials, especially in prerequisite courses, must have similarities so that the learning process can continue in accordance with the learning outcomes to be achieved. This paper presents an experiment results of the proposed the recommendation system modeling for personalization learning model implemented using Content-Based Collaborative Filtering model. In the proposed recommender system model, similarities between the learning materials and the description of the course being are computed as basis for developing recommended learning materials to students. The recommendation system modeling for personalized learning is applied as a solution to achieve learning goals and minimize dropouts due to the inability of students to understand the learning process in online learning systems. The results given in the research are modeling of an adaptive learning personalization recommendation system to provide recommendations for students who do not reach the minimum competency standards. © 2022 IEEE.

关键词： Recommender systems

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