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NoSQL Injection Detection Using Deep Neural Network and Principal Component Analysis of Injection Feature Vectors 11

NoSQL Injection Detection Using Deep Neural Network and Prin...

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11th International Conference on computer, Control, Informatics and its Applications, IC3INA 2024

作者： Soegiharto, Irwin Santoso Girsang, Abba Suganda Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program - Master of Computer Science Binus Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model. © 2024 IEEE.

关键词： Dimensionality reduction

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A Review on Machine Learning Methods for Water Quality Prediction 4

A Review on Machine Learning Methods for Water Quality Predi...

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Dharma, Vickenzio Leeputra Nurtanio, Nathanael Kenneth Nugroho, Fakhru Sidik Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Water is an important substance for the human body. Clean water is important for not just the human body, but also for the environment. In this paper, Prisma is used to filter many of the reference paper where the paper left are used for the research. Machine learning is one of many ways to predict water quality. Using algorithms to process large amounts of data and patterns, water quality can be predicted. Prediction on water temperature, pH, and others can be used to find bad quality water and potentially find the cure for it. There are also different kinds of environment in predicting water quality, where depending on the environment certain machine learning method is better than the others. This paper contains machine learning methods, water parameters, and areas of water for water quality prediction. © 2023 IEEE.

关键词： Forecasting

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Implementation of the EfficientNet Model for Identification of Wild Edible Plants 4

Implementation of the EfficientNet Model for Identification ...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350376111

Wild plants are plants that are not cultivated but wild plants can be easily found in the neighborhood. However, not all wild plants can be utilized as food crops by the community. Therefore, it is necessary to know what wild plants can be consumed so that people do not have to worry about food availability. The purpose of this study is to determine whether the EfficientNet model can be used to identify wild plants that can be consumed. Experimental results show that the EfficientNet model has an accuracy of 85% in identifying wild plants that can be consumed. © 2024 IEEE.

关键词： convolutional neural network (CNN) edible wild plant EfficientNet image classifier machine learning

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality 11

A Decision Model for Smart Village in Measuring Socioeconomi...

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

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

ISBN: (纸本)9798350389654

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity. © 2024 IEEE.

关键词： fuzzy logic inequality index object-oriented smart decision model smart village socio-economic

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Comparison of Sound Classification Algorithms on NIGENS Dataset 6

Comparison of Sound Classification Algorithms on NIGENS Data...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Tiomar, Jolin Angela, Stephanie Sudijanto, Justin Anthony Liwe, Gerry Ezekiel Suandi, Dani Saputra, Dany Eka Computer Science Department School of Computer Science Bina Nusantara University Bandung Campus Jakarta Indonesia

ISBN: (纸本)9798331539603

This study examines the performance of several machine learning and deep learning algorithms in classifying sounds from the NIGENS dataset, which consists of 15 unique sound classes. The implementation included five distinct algorithms: Convolutional Neural Networks (CNN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Long Short-Term Memory Networks (LSTM), and Random Forest. The models' performances are evaluated using classification measures such as Accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curves. The Random Forest method demonstrates the best overall accuracy of 86% when compared to other algorithms, with precision, recall, and F1-score values of 0.88, 0.86, and 0.86. © 2024 IEEE.

关键词： Adversarial machine learning

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Time Series Forecasting Using LSTM to Predict Stock Market Price in the First Quarter of 2024

Time Series Forecasting Using LSTM to Predict Stock Market P...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Maharani, Farrah A Ivana, Sharen Fithriyah, Belva Zakiyyah, Alfi Yusrotis Sihotang, Erna Fransisca Angela Bina Nusantara University School of Computer Science Mathematics Department Jakarta Indonesia Bina Nusantara University Jakarta School of Computer Science Statistics Department Indonesia

ISBN: (纸本)9798350364101

Predictions on the stock market are critical because they significantly influence the world economy. The value of share prices usually experiences continuous fluctuations. Therefore, predicting share price growth is very important. Notable stocks dominating global markets include Amazon, Apple, Microsoft, and Google. The paper discusses predictions for developing these four shares for the next two months. The research uses the Long Short-Term Memory (LSTM) Network method to predict stock prices for the next two months using an input sequence of previous stock values. LSTM methods demonstrate the capacity to retain long-term memory while reducing the influx of irrelevant information and superior efficiency in data processing, prediction, and classification. After analysis, the results show that the close price value from Microsoft shows the highest results, reaching 374.736. © 2024 IEEE.

关键词： Long short-term memory

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Security and Privacy Issues in Cloud-Based Databases: A Literature Review 10

Security and Privacy Issues in Cloud-Based Databases: A Lite...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Marpaung, Oliver Sebastian Alvyn, David Adriel William, Vincent Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

Nowadays, technology is getting more sophisticated. It can be seen with the emergence of Cloud Computing. One application of Cloud Computing is Cloud databases. In a cloud environment, there will be privacy and security issues. From the literature results, data leaks and attacks from outsiders are still found when using cloud databases. Cloud service providers already have the technology to maintain data privacy and security. Three procedures can be carried out to maintain data privacy and security: encryption, access control, and third-party audits. Data privacy and security will be maintained with cloud service providers and procedures technology. © 2023 IEEE.

关键词： Cryptography

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Text Vectorization Techniques for Trending Topic Clustering on Twitter: A Comparative Evaluation of TF-IDF, Doc2Vec, and Sentence-BERT 5

Text Vectorization Techniques for Trending Topic Clustering ...

引用

5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Susanto, Alvian Daniel Andrian Pradita, Steven Stryadhi, Caroline Setiawan, Karli Eka Fikri Hasani, Muhammad School of Computer Science Bina Nusantara University Computer Science Department Tangerang Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350369489

In this digital era, where technology is rapidly advancing, social media has become a primary platform for obtaining and disseminating information. Knowing what is being widely discussed and trending on social media is crucial for important aspects such as politics, economic, social and cultural issues. The objective of this research is to perform clustering on texts or sentences, and within each cluster, identify the most influential keywords that can serve as parameters to determine the topics being discussed in each cluster. Twitter was chosen as the social media platform to be analyzed in this research due to its text-based nature. The clustering method used is DBSCAN, considering that the number of clusters is unknown, and three text embedding techniques to be compared, namely TF-IDF, Doc2Vec, and Sentence-BERT. The performance of clustering with different text embedding techniques were evaluated using the silhouette coefficient. Hyperparameter tuning has been done to find the best-performing hyperparameters. From the best-performing technique, topic finding within the resulting clusters was conducted using Latent Dirichlet allocation (LDA). The results of this research indicated that clustering with DBSCAN and TF-IDF, with the highest silhouette coefficient, namely -0.00001, produced one cluster and 3342 outliers. DBSCAN and Doc2Vec, with the highest silhouette coefficient, namely 0.71590, produced one cluster and one outlier. DBSCAN and Sentence-BERT, with the highest silhouette coefficient, namely -0.02425, produced two clusters and two outliers. Based on the research findings, smaller silhouette scores tend to have a more varied number of clusters. DBSCAN with each tested text embeddings showed that the topic for every cluster, except for the first cluster of DBSCAN that use Sentence-BERT, were COVID-19 related topic. The DBSCAN and Sentence-BERT model, despite having a lower silhouette score, successfully identifies two separate clusters with distinct topi

关键词： Embeddings

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Implementing Robotic Arm for Efficient and Reliable Water Quality Monitoring in Aquaculture 6

Implementing Robotic Arm for Efficient and Reliable Water Qu...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Mulyadi, Iska Hazma Christiandinata, Billy Rahmania, Rissa Nurasid, Muhammad Ikhsan Arrahmah, Anissa Istiqomah Computer Science Department School of Computer Science Bina Nusantara University Bandung Campus Jakarta Indonesia

ISBN: (纸本)9798331539603

This study introduces an IoT-enabled robotic arm system designed to improve water quality monitoring in aquaculture. The main goal is to automate the process and enhance the reliability and efficiency of monitoring critical parameters—pH, temperature, and Total Dissolved Solids (TDS)—which are essential for maintaining fish health and sustainability in aquaculture settings. Utilizing a pH sensor, temperature sensor, and TDS sensor, the system integrates with AWS Cloud to facilitate real-time data monitoring accessible on smartphones. The robotic arm selectively immerses the pH sensor, addressing concerns about longevity by reducing continuous exposure to water, while the other sensors remain submerged for consistent readings. Testing was carried out in a controlled fishpond environment, revealing an average daily pH of 8.66, a temperature of 25.22°C, and a TDS level of 268.74 ppm, all demonstrating high measurement precision and consistency. A comparative analysis with manual readings indicated that the robotic arm produces more stable and reliable measurements, highlighting its potential for scalable application in aquaculture management. This innovative approach effectively combines IoT technology with robotic automation, significantly enhancing aquaculture productivity by ensuring optimal water quality. © 2024 IEEE.

关键词： Aquaculture

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Efficiency Analysis of Brute Force and Knuth Morris Pratt Algorithms for Indonesian Keyword Search on KBBI 5

Efficiency Analysis of Brute Force and Knuth Morris Pratt Al...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Alvin, Alvin Ramadhany, Daffa Gusti Insan Rabbani, Raya Suryaningrum, Kristien Margi Amalia Saputri, Hanis Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350369489

Keyword search in Indonesian in the Big Indonesian Dictionary (KBBI) is an important problem in the field of information technology, because KBBI is the main reference source in determining the meaning and significance of words in Indonesian. In the keyword search process, there are several algorithms that can be used, one of which is the Brute Force and Knuth Morris Pratt (KMP) algorithms. In this research, we will analyze the efficiency of both algorithms in searching keywords in KBBI. The Brute Force algorithm will compare each character of the keyword with each character in each word in the KBBI. While the KMP algorithm will use a table to match each character in the keyword with each character in each word in the KBBI. The method used for testing the algorithm is to use the KBBI word data set. This test will analyze the running time, memory usage, precision, accuracy, and keyword related of the two algorithms in completing the keyword search in KBBI. The result of this research is a comparison of the complexity and speed of the Knuth Morris Pratt and Brute Force algorithms. The outcomes demonstrate that while searching for keywords in the KBBI, the Brute Force approach is more effective than the KMP algorithm. The Brute Force algorithm has faster execution time, accuracy, and precision compared to the KMP algorithm, the KMP algorithm can be seeded by using less memory than the Brute Force algorithm. So it can be concluded that the Brute Force algorithm is more efficient in searching keywords in the KBBI compared to the KMP algorithm. As a result, the Brute Force algorithm can be utilized as a substitute to quicken the KBBI's keyword search process. © 2023 IEEE.

关键词： Efficiency

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