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IAENG International Journal of computer science 2021年第1期48卷

作者： Izzah, Imroatul Khuluqi Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Text classification is a process of locating text documents automatically into categories based on the text content. In-text classification, there is a stage that has an important role in giving the value of importance to each document, that is term weighting. In the researchers’ previous study, a new supervised term weighting (TF-Assoc) was introduced with the concept of association to optimize term weighting distribution in a case of multiclass classification. To improve the performance of text categorization, this paper proposes a term weighting scheme with a modified association concept, that is mTF-IDF-Assoc. The proposed term weighting scheme considered Document Length (DL). DL was used to normalize the term frequency by dividing it by the length of the document's vector and then formulting IDF and Assoc in calculating the weight of each word. The results showed that mTF-IDF-Assoc implemented with SVM classifier and 10-fold cross-validation technique could outperform the TF-IDF, TF-ICF, and TF-Assoc weighting scheme with an average accuracy of 82.322%. © 2021, IAENG International Journal of computer science All Rights Reserved.

关键词： Classification (of information)

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Performance of Information Technology Infrastructure Prediction using Machine Learning

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Procedia computer science 2021年 179卷 515-523页

作者： Ignatius Rahardjo Heruwidagdo Suharjito Novita Hanafiah Yanto Setiawan Computer Science Department Binus Graduate Program-Master in Computer Science Bina Nusantara University Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performance. In this study we use a machine learning approach to make predictions about the performance of information technology infrastructure. The experiment took data from several servers in a company to be tested. The performance measure of resources used in this study are CPU Performance, Disk performance, Memory capacity, and Network performance. Several algorithms and machine learning methods are tested, such as Linear Regression, kNN, SVR, Decision Tree and Random Forest, to find the best model fit for the servers. The comparison result shows that Linear regression and kNN perform well in predicting the network performance in those three servers.

关键词： log performance machine learning kNN IT infrastructure performance prediction

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Stemming Algorithm for the Indonesian Language: A Scientometric View

Stemming Algorithm for the Indonesian Language: A Scientomet...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Aries Maesya Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

Stemming is the process of cutting affixes, both prefixes and suffixes from a term to get the root of the word that has affixes. Stemming can be done in any language, especially in Indonesia Language. Indonesian which is rooted in Malay and Sanskrit has a bigger influence on Language. The algorithm of stemming that has developed is the one that is based on the dictionary of a root word. This kind of algorithm was started by Nazief- Adriani and it grew up to be Confix Stripping (CS), and then was perfected to be Enhanced Confix Stripping (ECS). A scientometric analysis is a mathematical method used to identify academic publications related to citations and scientific matters and is intended for use in libraries or other fields. In this research, the publication of which using stemming algorithm selected and collected using keywords related to Nazief Adriani and ECS. The publications are collected from *** (an online database for advance scientific research). From those publication we did the analysis bibliometric help by the VOS Viewer application using the analysis technique of co-authorship and citation technique, and all done. The result are, from 310 publications that have keywords Nazief Adriani and 119 publications containing the keyword ECS, it was found that the owning documents will be the reference sources of the next research in which the most citation and co-authorship are found in Indonesia.

关键词： Text analysis Dictionaries Databases Citation analysis Natural language processing Libraries Standards

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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International Conference on Systems and Informatics (ICSAI)

作者： Sucianna Ghadati Rabiha Iman Herwidiana Kartowisastro Reina Setiawan Widodo Budiharto Computer Science Department BINUS Graduate Program – Computer Science. Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665474498

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture.

关键词： Measurement Analytical models Face recognition Mouth Estimation Feature extraction Behavioral sciences

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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia 2

Lockdown strategy worth lives: The SEIRD modelling in COVID-...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Nurlaila, I. Hidayat, A.A. Pardamean, B. Information Systems Department BINUS Online Learning 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 Bina Nusantara University Jakarta11480 Indonesia

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

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Procedia computer science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

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Deep Learning Based Facial Emotion Recognition using Multiple Layers Model

Deep Learning Based Facial Emotion Recognition using Multipl...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665499699

A Facial Emotion Recognition (FER) system is an important tool to be implemented in any psychology academic field and beyond. This paper aims to show a system of Facial Emotion Recognition that can be done using the multiple layers model of ResN et50 which is an ANN. The dataset is an array that consists of 7 different emotions represented by the numbers 0-6. The emotion included in the dataset is 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'shock', and 'neutrality'. This research found that the highest accuracy in the application of the model was recorded at 65% and the test was 60% to recognize facial emotion from a graphical input. © 2021 IEEE.

关键词： Emotion Recognition

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Threat Modeling for Honeypot Deployment

Threat Modeling for Honeypot Deployment

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Information Technology International Seminar (ITIS)

作者： Kalpin Erlangga Silaen Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes.

关键词： Threat modeling Seminars Firewalls (computing) Filtering Prevention and mitigation Complexity theory Security Information technology Monitoring Cyberattack

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Mobile Application for Haircut Art Community 2

Mobile Application for Haircut Art Community

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Fahmi, Hilal Nugroho, Muhammad Ivan Setiawan, Kristian Daniel Gary, Vittorio Moedjiono, Sardjoeni Warnars, Harco Leslie Hendric Spits School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Budi Luhur University Faculty of Information Technology Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

The haircut art community is a community that is interested in the art of hair or hair-related issues and information. This haircut art community has several members, but they need the application of technology to support their hobby as a community. In this mobile application, a system where users can make transactions connected to the haircut community has been proposed. Users can make online appointments with barbers' haircuts, make online payments, engage with forums, and get information about the barbershop community. In addition, this mobile app will allow people in the community to sell and buy anything related to haircuts. In addition, it is hoped that it can be used as an income opportunity for people who have expertise in the art of barbering. This application is implemented using visual studio and MySQL database for the database. This application is modeled using use case diagrams to describe its business processes and class diagrams to describe the database design. © 2022 IEEE.

关键词： Database systems

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

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

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International Conference on Computing, Engineering, and Design (ICCED)

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

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.

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