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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Annisa Rizqiana Afiahayati Department of Computer Science and Electronics Master Program in Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In this study, we initiated an approach to predict Protein Secondary Structure Prediction (PSSP) by using n-grams modeling, namely $n$ adjacent amino acid sequences, to represent the amino acid sequence and 1-Dimensional Convolutional Neural Network (CNN). We compare n-grams with a one-hot encoding approach and find that n-grams provide better performance. The analysis results show that bigrams are more effective than trigrams in describing protein amino acid sequence patterns. We also combine it with the PSSM profile feature to improve model performance. The results of this study reveal that the n-grams approach, especially bigrams, in combination with PSSM, can produce more accurate protein secondary structure predictions than previous models, with superior accuracy evaluations of Q8 68.1 % and Q3 82.75 % on the CB513 dataset.

关键词： Proteins Accuracy Convolution Neural networks Predictive models Amino acids Encoding Convolutional neural networks Bioinformatics Kernel

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Cancer Classification in Imbalance Data Using Double Filtering Approach

Cancer Classification in Imbalance Data Using Double Filteri...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Almuzhidul Mujhid Jeffrey Junior Tedjasulaksana Nikita Ananda Putri Masaling Abba Suganda Girsang Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Cancer, also known as malignant neoplasm, is a complex and potentially fatal disease characterized by uncontrolled and abnormal cell growth in the body. The main problems with microarray cancer studies are the high curse of dimensionality and small sample size caused by redundant and irrelevant genes. To deal with the number of features that exceed the amount of data, this research purposed double filtering method Lasso-GA, Lasso is used to select the features based on feature correlation while Genetic Algorithm is used to optimize the most important features with accuracy traditional machine learning as its fitness function. The results show how effective the suggested method is; in breast cancer, the linear SVC model achieves excellent accuracy (0.93), precision (0.94), recall (0.94), and F1 score (0.94), while in lung cancer, the linear SVC, random forest, and logistic regression models perform well (accuracy: 0.95, precision: 0.92, recall: 1, F1 score: 0.95). Logistic regression is the most effective method for bladder cancer, with an accuracy of 0.82, precision of 0.77, recall of 1, and F1 score of 0.87.

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Enhancing Efficiency in Yolov8n for Real-Time Detection of Oil Palm Fruits in Plantations Using Mobileone and Cross-Fusion

SSRN

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SSRN 2024年

作者： Hugo, Gregory Mushlik, Muhammad Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Accurate detection of fresh fruit bunches (FFB) ripeness in complex plantation environments is challenging due to occlusion, varying lighting, intricate visual features, and limited computational resources of mobile devices. To address this, a resource-efficient, real-time detection system was developed using an optimized YOLOv8n model. The model integrates MobileOne modules and Cross-Fusion blocks to enhance efficiency without compromising accuracy. Specifically, Conv and C2f modules were replaced with MobileOne modules, reducing parameters by 85% and 81%, respectively. Cross-Fusion improved feature fusion, and its combination with MobileOne (CF-MobileOne) achieved a 51% reduction in parameters while maintaining performance. The dataset comprised images from videos captured in plantations, annotated into five maturity stages: flower, unripe, underripe, ripe, and abnormal, and underwent augmentation with class balancing through manual undersampling. The model outperformed baseline YOLOv8 variants and heavier models like YOLOv9c and Faster R-CNN, achieving an mAP50 of 0.795 and an inference speed of 140.57 FPS. Hyperparameter tuning with a batch size of 64, SGD optimizer, and a learning rate of 0.01 yielded optimal results. Despite challenges in detecting certain classes due to visual similarities and environmental variations, the system effectively enhances productivity and efficiency in precision agriculture for the palm oil industry. © 2024, The Authors. All rights reserved.

关键词： Fruits

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Recommendation News for Joumalist Based on Twitter Data Using CNN 6

Recommendation News for Joumalist Based on Twitter Data Usin...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Harvy, Ikrar Girsang, Abba Suganda Saragih, Melva Hermayanty Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods. © 2022 IEEE.

关键词： classification news convolution neural network deep learning journalist Social media

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A Smart Fuzzy Decision Model for Anticipating Household Expenditure Shifts in Response to Fluctuating Fuel Prices in Indonesia

A Smart Fuzzy Decision Model for Anticipating Household Expe...

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International Conference on ICT For Smart Society (ICISS)

作者： Frans Fericia Jeremy Ansellino Gunawan Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in response to fuel price fluctuations. The model utilizes fuzzy logic as its main method, enabling it to capture the intricate relationships between household spending and fuel prices. In addition, the proposed model incorporates various factors that can potentially influence household spending. By simulating prediction results under different fuel price increments ranging from 0% to 30%, the model provides valuable insights for policymaking concerning fuel pricing and offers strategies to mitigate the impact of fuel price fluctuations on household welfare.

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Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms for Securing Digital Documents

Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms ...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Delisman Hulu Maya Silvi Lydia Mohammad Andri Budiman Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

Symmetric algorithms offer speed but have weaknesses in key distribution, while asymmetric algorithms are secure but less efficient for encrypting and decrypting large text messages. This research aims to secure data and digital documents through the implementation of a hybrid cryptosystem that combines the Cramer-Shoup algorithm and Spritz. This research also analyzes and evaluates the effectiveness and speed of encryption and decryption, as well as the efficiency of the system in protecting various types of data, including docx and pdf document files. The methodology of this research includes a literature review, analysis of the encryption-decryption process, and performance testing of the system using the Python programming language. The speed test results show that this hybrid cryptosystem provides fast encryption-decryption times, with an encryption time of 0.002607 milliseconds for 400 characters and a decryption time of 0.001537 milliseconds, while for digital documents of the pdf file type, the encryption time is 0.424478 milliseconds and the decryption time is 0.404343 milliseconds. In addition, the use of the SHA-3 hash function has proven effective in maintaining data integrity. In conclusion, this hybrid cryptosystem offers an efficient and secure solution for protecting various types of digital data, including text and documents.

关键词： Hash functions Resistance Data integrity Scalability Portable document format Encryption Testing Python Videos Systematic literature review

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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A Fuzzy-based Simple Smart Decision Model for Assessing Information Security Risk in Public Sector Organization

A Fuzzy-based Simple Smart Decision Model for Assessing Info...

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International Conference on ICT For Smart Society (ICISS)

作者： Ayu Artaparamita Religia Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The measurement of information security risk in a public sector organization is of utmost importance. This measurement serves the purpose of taking appropriate actions in response to potential risks or damaging incidents. The objective of this study is to develop a straightforward yet smart decision model capable of evaluating risks, particularly within the public sector organization. The decision support modelling (DSM) concept is employed as a framework to construct a computer model that supports decision-making in a specific case. The study comprises five stages, which are integral parts of the DSM process. These stages include analyzing the case, examining relevant documents, designing the model, constructing the model, and evaluating the finalized model. The object-oriented method serves as a fundamental approach to model design. Additionally, the fuzzy logic method, an intelligent computational technique, plays a central role in the development of this decision model. The proposed model demonstrates an average error value of 0.05 when compared to the actual risk measurement conducted. Furthermore, it reveals average risk values of 0.59 and 0.45 for the pre- and post-remediation scenarios, respectively.

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Software Metrics for Container-Based Applications: Systematic Literature Review

Software Metrics for Container-Based Applications: Systemati...

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International Conference on Information Management and Technology (ICIMTech)

作者： Muhammad Juan Al Qausar Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Containerization has become a popular approach in application development in applications development and deployment, many benefits we can get such as improved scalability, portability, and resource efficiency. Container-based applications, utilizing technologies like Docker and Kubernetes, have transformed the packaging, deployment, and management of software from the desktop environment to the cloud platform. In this context, software metrics approach plays a good role in evaluating the characteristics and performance of container-based applications, ensuring that developers and operators are on the same page. This article explores the importance of software metrics in optimizing the software lifecycle of container-based applications, addressing the unique challenges they present, and highlighting the potential benefits of leveraging metrics to improve performance and efficiency. Our finding Performance Metrics and Availability Metrics is the most metrics that the most measure by applications owner, relevant studies and industry practices, this study aims to provide insights and recommendations to effectively measure and optimize region-based software systems.

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Service Oriented Smart Model for Flood Controlling

Service Oriented Smart Model for Flood Controlling

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

作者： Rachmat Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capital). The study was conducted to develop a smart model for flood controlling based on service or called a service-oriented smart model (SOSM). The water-flow algorithm (WFA), fuzzy logic, object and service-oriented are four main methods operated in the study. The WFA is a central method to model the real water flow in the river coming from Katulampa dam (in Bogor city) until Manggarai dam (in Jakarta). The fuzzy logic functioned to simulate the dam’s water level and the gate open/close decision should be decided by avoiding the bias value. The object-oriented model analysis and design approach, where the unified modelling language (UML) tools are operated to analyze and design the constructed model. Then, the service-oriented conception is used to integrate all sides in implementing the model. Finally, the constructed model can simulate the flood status in Jakarta via status value in decimal numbers with 6 numbers behind the point.

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