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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Dedy Ariansyah Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used.

关键词： Protocols Data security Decision making Organizations Machine learning Encryption Protection Information technology Research and development System analysis and design

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Fuzzy Decision Support Model on Virtual Plant Computational Model of Water Spinach (Ipomoea aquatica. Forssk) in Farming Business

Fuzzy Decision Support Model on Virtual Plant Computational ...

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

作者： Ditdit Nugeraha Utama Ika Diah Agustia Rachmawati Heraldo Yusron Purwantono Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Using soil as a planting medium (conventional system) raises several problems, such as the need for large agricultural land, but the available land is limited. This problem is triggered by an increase in demand for nutrients needed by the body to support daily activities, one of which can be met by consuming green vegetables, such as Water Spinach. To solve this problem, a hydroponic wick system or a wick system can use that is simple and easy to care for. Water Spinach (Ipomoea aquatica. Forssk) vegetables are easy to grow and care for. Nutrients contained in 100 grams of Water Spinach, energy: 29 kcal, protein: 3 grams, Fat: 0.3 grams, carbohydrates: 5.4 grams, calcium: 73 mg, phosphorus: 50 mg, iron: 3 mg, vitamin A: 6300IU,vitamin B1: 0.07 mg, vitamin C: 32 mg. In addition, a combination of disciplines such as mathematics, computer science, plant science, biology, and statistics can be used. This combination of knowledge is known as Plant Computational Modeling (PCM), which uses the Growth Grammar Interactive Modeling Platform (GroIMP) with the Functional Structural Plant Modeling (FSPM) method. The virtual plant model was successfully built, the results were divided into two parts, such as single plant and multi-plant. Plant computational modeling and DSM can help deal with agricultural problems. By using a plant dataset, the GroIMP-FSPM platform, and fuzzy logic, the model could help researchers in making a better decision based on BEP evaluation. The minimum numbers of plants cultivated were 650 plants in one harvest period in order to gain a desirable profit. We recommend to cultivate even more plants in one harvest period to gain even higher profits.

关键词： Proteins Phase change materials Computational modeling Plants (biology) Soil Phosphorus Mathematical models

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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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A Systematic Literature Review of Gamification in Massive Online Open Course 5

A Systematic Literature Review of Gamification in Massive On...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Bangkit Saputra, Jeffri Prayitno Nizar Hidayanto, Achmad Prabowo, Harjanto Meyliana Universitas Amikom Purwokerto Doctor Of Computer Science Graduate Program Bina Nusantara University Faculty Of Computer Science Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781665401968

The development of information and communication technology makes it easy for anyone to be able to take part in online-based learning. Currently, online-based learning has been offered by many institutions or commercial industries. Gamification is one of the strategies to increase students' learning motivation. In this study, we focus on examining the role of gamification and game elements are contained in gamification-based learning. From the results of our research, it shows that gamification in the online-based learning process is growing. Gamification is proven to have a positive impact on students' learning motivation, although it is still dependent on various factors and conditions. © 2021 IEEE.

关键词： Motivation

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The Effectivity of Color For Chroma-Key Techniques

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Procedia computer science 2021年 179卷 281-288页

作者： Carolus Raditya Muhammad Rizky Sergio Mayranio Benfano Soewito Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In this age where almost all facets of life are full of digital objects, because of technology is advancing more and more rapidly, included the fields of economy, the social field, the industrial field, labour, and the entertainment field especially the film industry. In film industry, the backgrounds that were once impossible such as foreign planets and the deep expanses of space are now possible, this is due to the technique known as Chroma Key as well as the usage of green screens and blue screens. In this research we wish to find out if only green screens and blue screens are the only viable colours to use or is there any other colours available to use to Chroma *** use or are other colours available to use to Chroma Key. The methods that we used to conduct research on this topic are case studies. We conduct a case study to see the level of efficiency and fatigue after using various colours as a background to the Chroma Key process. Experiments: We ourselves will try to conduct the Chroma Key process while using various colours as the background and making sure that other variables such as our lighting and spacing are constant on every case we conducted, to measure the efficiency and effectivity of the colours in the Chroma Key process.

关键词： colour chroma-key effectivity of colour background

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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait

Multilabel Machine Learning System for Predicting Gender and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Vincent Yono Dimitar Kazakov Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department University of York United Kingdom

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm.

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Fake News Classifier with Deep Learning

Fake News Classifier with Deep Learning

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Industrial Electrical and Electronics (ICIEE), International Conference on

作者： Michael Febrianto Lu Renaldy Vincent Ciptadi Reyhan Nathanael Kevin Sam Andaria Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665486231

In this decade, social media has become more frequently used because it is easy to see some news on the social media for example Instagram, Facebook, and other social media. However, the problem that often arises in social media problems is the accuracy of the news. There are many fake news circulating on social media. This paper aims to classify the fake news using random forest. This research uses data Kaggle “Fake News Dataset Combined Different Sources”. The accuracy of the Fake News Classifier using this model is is 0.9403, the Classification Report on this model is True Recall Score: 0.88 and the Fake Recall score is 0.98 while the Precision Score is 0.96 and 0.93 for each true and fake result, F1-Score is 0.92 and 0.95. At the end the model using self-inputted data is tested

关键词： Deep learning Social networking (online) Multimedia Web sites Forestry Real-time systems Data models Fake news

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News Classification Based On News Headline Using SVC Classifier

News Classification Based On News Headline Using SVC Classif...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Goldius Leonard Fukriandy Sisnadi Nicholas Vigo Wardhana Muhammad Abdul Aziz Al-Ghofari Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model.

关键词： Training Linear regression Static VAr compensators Data models Telecommunications Decision trees Random forests

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Twitter-based Sentiment Analysis for Indonesian Drug Products using Supervised Feature Engineering

Twitter-based Sentiment Analysis for Indonesian Drug Product...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Kartika Purwandari Nicholaus Hendrik Jeremy Panji Arisaputra Samuel Philip Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Social media communications offer valuable feedback to firms about their products. Twitter users share their opinions about e-commerce products on social networking sites. This paper reports a study in sentiment classification for Indonesian drug products for automatically classifying Twitter data as expressing positive or negative sentiments or opinions. The study investigates the effectiveness of using a Decision Tree, Support Vector Machine, Random Forest, Na rve Bayes, and Logistic Regression, along with the term frequency-inverse document frequency used to classify 531 tweets into recommended (positive sentiment) and not recommended (negative sentiment). Among all traditional machine learning models, the highest average accuracy value is 88.6 in the RF model and the lowest average accuracy value is 80.2 when using the LR model. Judging from the average accuracy obtained, the DT and RF model used has a considerable performance

关键词： Drugs Support vector machines Seminars Analytical models Sentiment analysis Social networking (online) Blogs

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Hate Speech Text Classification Using Long Short-Term Memory (LSTM)

Hate Speech Text Classification Using Long Short-Term Memory...

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International Conference of computer science and Information Technology

作者： Suminar Ariwibowo Abba Suganda Girsang Diana Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech.

关键词： Training computer science Dictionaries Hate speech Text categorization Bit error rate Information technology

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