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The data warehouse for down payment administration in the Constitutional Court of Republic of Indonesia 2

The data warehouse for down payment administration in the Co...

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2nd Nommensen International Conference on Technology and Engineering, NICTE 2018

作者： Girsang, Abba Suganda Isa, Sani Muhamad Adytiansya, Nanda Utomo, Oei Kurniawan Simarmata, Janner Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Universitas Negeri Medan Medan Indonesia

The Constitutional Court of The Republic of Indonesia is an institution which has authority to judge and examine constitutions. In this institution, each component has responsibility to manage their own finance and budgeting. Based on this case, they need a solution to summarize the amount and absorption of the budget in every component. This paper proposes a solution to design a data warehouse model to process and analyse the down payment administration data. The nine steps design methodology for developing data warehouse is implemented in this work. This paper aims to bring some benefit for The Constitutional Court, especially in form of information about down payment absorption for every component each year. The result is presented in form of pdf report and informative dashboard using data warehouse tools. © Published under licence by IOP Publishing Ltd.

关键词： Data warehouses

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction 9

Examining the impact of attitude, availability, price, and q...

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

作者： Arin, Ikrar Adinata Murad, Dina Fitria Warnars, Harco Leslie Hendric Spits Meyliana Bina Nusantara University Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information Systems Department BINUS Online Learning Jakarta Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information System Department School of Information Systems Jakarta Indonesia

ISBN: (纸本)9798350370126

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions. © 2023 IEEE.

关键词： Quality of service

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Mimboro, Prasetyo Soewito, Benfano Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Effect of Using Grid Search Method on The Performance of Machine Learning Models for Heart Disease Classification 7

Effect of Using Grid Search Method on The Performance of Mac...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Anggreainy, Maria Susan Ayuliana Bina Nusantara University Binus Graduiate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331505530

To get accurate data prediction results, it requires not only the right machine learning model, but also hyperparameters. Hyperparameters play an important role in shaping and optimizing the performance of machine learning models. Determining the right combination of model and hyperparameters is often a challenge. This research will use a hyperparameter search method, namely grid search. The aim of this research is to find out optimization best results from hyperparameters for several machine learning models using the Grid Search method and see the performance impact of using the grid search method for heart disease classification. Heart disease is a disease that has the potential to be fatal for humans if not treated quickly. Early detection can be done by utilizing machine learning models that are able to analyze data patterns from studied data sets. By training machine learning to study data sets related to heart disease, it is hoped that it will be able to carry out early detection of heart disease. © 2024 IEEE.

关键词： Classification Grid Search Method Heart Disease Hyperparameters Machine Learning

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Design of Smart Home Security System using Object Recognition and PIR Sensor 3

Design of Smart Home Security System using Object Recognitio...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Surantha, Nico Wicaksono, Wingky R. Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University JI kebon Jeruk Raya No 27 Jakarta11480 Indonesia

This research aims to design and implement a home security system with the capability for human detection. The traditional home security system, i.e. closed circuit television (CCTV) can only capture and record the video without able to give warning feedback if there is any suspicious object. Therefore, an additional object detection and warning method is required if there is an intruder. The proposed design is implemented using Raspberry Pi 3 and Arduino, which are connected by USB cable. The PIR sensor is installed on the Arduino and the webcam is mounted on Raspberry Pi 3. The Raspberry Pi 3 is used to process inputs from received sensors and process images for human detection. The PIR sensor detects the movement around the sensor to activate the webcam to capture a picture. Then, the object recognition is performed using histogram of gradient (HoG) and support vector machine (SVM) to detect the suspicious object. If the suspicious object is detected, then alarm is activated to warn the house owner about the existence of intruder. The evaluation results show that it takes in average 2 seconds for proposed system to detect the intruder. It also shows that the system can successfully detects the intruder with accuracy of 89%. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Object recognition

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Evaluation of ISO 27001 implementation towards information security of cloud service customer in PT. IndoDev Niaga Internet

Evaluation of ISO 27001 implementation towards information s...

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International Conference on Computation in science and Engineering, ICCSE 2017

作者： Fajar, Ahmad Nurul Christian, Hendy Girsang, Abba Suganda Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Every organization must ensure that information assets are protected and information security system has been implemented well. PT. IndoDev Niaga Internet is a provider of business solutions applications and implementation services that include application Human Resource Information System (HRIS) and Enterprise Resource Planning (ERP). In order to ensure the security of the information, then in 2015 PT. IndoDev Niaga Internet implement ISO 27001: 2013. Through the implementation of ISO 27001: 2013, it is expected that information can be properly maintained, which in turn will affect the business continuity. Companies need to know the extent to which the process has been applied and what actions can be done to improve the performance of the application of ISO 27001: 2013. Factor analysis was conducted first to determine the factors that affect to the information security. After the factors that affect to the information security known, then observation and interview conducted to gather data about PT. IndoDev Niaga Internet ISO 27001:2013 implementation according to the factors that affect. And then recommendation and corrective action developed using gap analysis method. The most influential factor to the security of customer information PT. IndoDev Niaga Internet is a factor access control and security operations. For the audit results IS027001: 2013 from the aspect of access control, they found 11 check items that fit into the category of NC (Non-Conformance) of 33 check items in which 9 of them in the category of major and two remaining categories minor, while for operations security aspects of the 12 check items, found 5 that goes into the category of NC (Non-Conformance) and everything was included into the category of minor. © Published under licence by IOP Publishing Ltd.

关键词： Enterprise resource planning

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Server Cloud Based Security Surveillance Systems: a Design science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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A Comparison of Lexicon-based and Transformer-based Sentiment Analysis on Code-mixed of Low-Resource Languages 1

A Comparison of Lexicon-based and Transformer-based Sentimen...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Tho, Cuk Heryadi, Yaya Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University BINUS Graduates Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Sentiment analysis from code-mixed texts has been gaining wide attention in the past decade from researchers and practicians from various communities motivated, among others, by the increasing popularity of social media resulted in a huge volume of code-mixed texts. Sentiment analysis is an interesting problem in Natural Language Processing with wide potential applications, among others, to understand public concerns or aspirations toward some issues. This paper presents experimentation results aim to compare performance of lexicon-based and Sentence-BERT as sentiment analysis models from code-mixed of low-resources texts as input. In this study, some code-mixed texts of Bahasa Indonesia and Javanese language are used as sample of low-resource code-mixed languages. The input dataset are first translated to English using Google Machine Translation. The Sentiwordnet and VADER are two English lexicon label datasets used in this study as basis for predicting sentiment category using lexicon-based sentiment analysis method. In addition, a pretrained Sentence-BERT model is used as classification model from the translated input text to English. In this study, the dataset is categorized into positives and negative categories. The model performance was measured using accuracy, precision, recall, and F1 score. The experimentation found that the combined Google machine translator and Sentence-BERT model achieved 83 % average accuracy, 90 % average precision, 76 % average recall, and 83 % average F1 Score. © 2021 IEEE.

关键词： Sentiment analysis

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study 13

Pain Classification Using Statistical Feature Extraction Usi...

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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Sudimanto Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia

ISBN: (纸本)9798350323184

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people's psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%. © 2023 IEEE.

关键词： Extraction

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Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria 11

Random Forest Based QSAR Model Analysis for Predicting Drug ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Ekosputri, Alicia Josephine Ardianti, Amanda Anggreainy, Maria Susan Artanto, Brychan Fitriawati, Nora Valentina, Fanny Angelia Santoso, Vincent Kartamulya School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University Binus Online Learning Information Systems Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research. © 2024 IEEE.

关键词： Prediction models

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