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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia’s Sugar Industry

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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

作者： Ditdit Nugeraha Utama Muhammad Asrol Suharjito Christian Harito Napthaleni Muhammad Syafrudin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.

关键词： Fuzzy logic Seminars Shape Object oriented modeling Plantations Manuals Predictive models Sugar industry Intelligent systems Information technology

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： computer vision

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Tourism recommender system using hybrid multi-criteria approach 2

Tourism recommender system using hybrid multi-criteria appro...

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

作者： Maru'Ao, M.S.P. Suharjito Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

Traveling has become a part of life at the time of the current urbanization. The growth rate of the internet and the availability of information allows travellers to access tourist information easier and faster. However, the individual's ability to find the information that they want is not proportional to the complexity of the information. This research would like to develop a reliable tourism recommender system that is able to provide destination recommendations according to user preferences with a combination of several methods i.e., Content-based, Collaborative Filtering, Multi-Criteria ratings, Demographic and Ontology-based. This research aims to implement a recommendations system for tourism through mobile devices by using the Android operating system. The results of this research indicate that the Hybrid Multi-Criteria method is able to provide recommendations to the user in accordance with the user's personal preferences and history of previous user rating with the average precision is 0.7. Evaluation metric of each methods indicate the lowest RMSE 0.84 in Multi-Criteria. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Long short-term memory for hate speech and abusive language detection on Indonesian YouTube comment section 11

Long short-term memory for hate speech and abusive language ...

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2021 11th International Workshop on computer science and Engineering, WCSE 2021

作者： Salim, Calvin Erico Rudy Suhartono, Derwin 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)9789811817915

Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than English. This research examines hate speech detection problem in form of Bahasa Indonesia. Millions of comments and text posts are added to various social media and discussion platforms. Manual classification in all of the internet as hate speech and offensive language is a near impossible and time-consuming task. This research uses Long Short-Term Memory (LSTM) and Bidirectional Long Short Term Memory (Bi-LSTM) for the method of classifying hate speech and abusive language. The final accuracy is 88,44% by using 200 neurons with Bi-LSTM method. Most common challenges are different languages, out of vocabulary words, long range dependencies, and sarcasm. © 2021 11th International Workshop on computer science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Long short-term memory

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Recent Trends for Text Summarization in Scientific Documents

Recent Trends for Text Summarization in Scientific Documents

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

作者： Arief Agus Sukmandhani Yulyani Arifin Muhammad Zarlis Widodo Budiharto Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.

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Monitoring Applications for Vehicle based on Internet of Things (IoT) using the MQTT Protocol 8

Monitoring Applications for Vehicle based on Internet of Thi...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Nurudin Sukmandhani, Arief Agus Zarlis, Muhammad Computer Science Department BinusOnline Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Monitoring of vehicle conditions is needed as one of the securities in driving because one of the accident rates is negligence in checking the condition of the vehicle. This study aims to help reduce the level of accidents and increase human awareness for vehicle conditions, especially minibus type vehicles. The system works to provide information on the condition of vehicles sourced from the Engine Control Unit (ECU). ECU data cars are obtained by SNIP traffic broadcast message and ask for data directly at the ECU. The car can provide vehicle information using a Canbus communication protocol through the OBD-II socket. Vehicle information can be seen by users through the website in real time. Based on the evaluation from the user side, this system provides ease of monitoring vehicle conditions because it can be accessed via the internet using various kinds of devices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Blackbox ECU OBD-II Sstem Logging

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

Pain Classification Using Statistical Feature Extraction Usi...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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

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%.

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E-Learning Satisfaction: Analysis of the Support Factors 10

E-Learning Satisfaction: Analysis of the Support Factors

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Siswanto, Boby Alamsyah, Doni Purnama Morika, Doni Othman, Norfaridatul Akmaliah Wijaya, Billiam Christofer Adinda, Putri Giyan Bina Nusantara University Computer Science Study Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University BINUS Business School Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University Interior Design Study Program School of Design Jakarta11480 Indonesia Universiti Teknikal Malaysia Melaka Department of Management Technology Melaka76100 Malaysia

ISBN: (纸本)9798350322729

E-learning satisfaction is an important factor in assessing the effectiveness and efficiency of distance learning. The use of Big Data technology can have a significant impact on e-learning satisfaction. The purpose of this research is to examine the implementation of big data in e-learning and the factors that can increase student e-learning satisfaction. This study uses a quantitative survey method with a questionnaire as a data collection tool. The research respondents were randomly selected e-learning students from several educational institutions that use Big Data technology in their e-learning platforms. There are 663 data collected through online questionnaires, then the data is processed through SmartPLS to test the research hypothesis. The results of the study show that the use of Big Data technology in an e-learning environment has a positive impact on student satisfaction. Several factors determine e-learning satisfaction including usability requirements, quality level, learning competence, and material relatedness. Relatedness to e-learning is more important in increasing student motivation in learning, as can be seen from the correlation value which is more dominant in forming student satisfaction. The e-learning platform must have navigation that is easy for users to understand and use. Learning content must be well-designed and follow established learning standards. The use of Big Data technology in e-learning can have a significant impact on student satisfaction and the quality of learning. Future research can develop a more personal and adaptive e-learning platform, using Big Data technology as the basis for its development. © 2023 IEEE.

关键词： E-learning

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Digital Nudge Evaluation on COVID-19 tracing Application 9

Digital Nudge Evaluation on COVID-19 tracing Application

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9th International Conference on Information and Communication Technology, ICoICT 2021

作者： Sukmaningsih, Dyah Wahyu Abdurachman, Edi Purwandari, Betty Trisetyarso, Agung Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781665404471

Nudge is considered as an intervention to change user behavior and influence decision-making. Mobile apps have become a part of our everyday life. In this pandemic era, governments use mobile apps' technology to control the spread of COVID-19 infection. Many governments implemented COVID-19 tracing apps, and citizens are forced to use the apps to record their contact tracing and alert them if they have contact with an infected person. The key to control the increasing spread of the virus also depends on citizens adhere to health protocol. Government has experience difficulty in enforcing the protocol without strict surveillance. Therefore, building awareness of risk is essential-this study attempt to design nudge intervention into COVID-19 tracing apps. From the evaluation of the original apps, four nudge interventions can be applied in the application. © 2021 IEEE.

关键词： COVID-19

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