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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

Procedia computer science 2021年 179卷 47-54页

作者： Kartika Purwandari Join W.C. Sigalingging Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.

关键词： weather classification twitter machine learning support vector machine multinomial naive bayes logistic regression

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer science and Information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Fuzzy water flow algorithm (FWFA) based ecological decision support model (ecoDSM) for flood control operating system

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AIP Conference Proceedings 2023年第1期2896卷

作者： Ditdit Nugeraha Utama Rachmat Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Intiland Development Jakarta Indonesia 10220

Flood disaster happens periodically in Jakarta. One opportunity that can be maximized to mitigate it is control management of the Manggarai water gate (MWG). The MWG itself is placed in the Ciliwung river in one district of the capital Jakarta. It has a vital role to minimize the impact of flood disasters in Jakarta. The study is a further version of previous research indeveloping a water-flow-like algorithm (WFA) based decision support model (DSM) to mitigate the impact of flood disasters. Theconstructed DSM is practically functioned to control the operation of opening/closing the MWG. The DSM was constructed by combining the previous WFA model with the concept of fuzzy logic (FL). The constructed model is called Fuzzy-WFA (FWFA) ecological DSM (ecoDSM); as it considers several aspects of ecology, e.g. river condition, weather situation, and flood itself. By building the FWFA-ecoDSM, the value of uncertainty can be converted to become crisp logic that is easier to understand. At the end of this research, the FWFA-ecoDSM was implemented in more detail compared with the previous one by producing the decimal numbers with 3 until 6 numbers behind the point; exclusively, in predicting the water level status, where the status used todecide the opening/closing level decision for opening or closing the MWG.

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier

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Procedia computer science 2023年 227卷 482-489页

作者： Albert Verasius Dian Sano Adriel Anderson Stefanus Evaristus Didik Madyatmadja Hendro Nindito Agung Purnomo Corinthias P.M. Sianipar Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Department of Global Ecology Graduate School of Global Environmental Studies (GSGES) Division of Environmental Science and Technology Graduate School of Agriculture (GSA) Kyoto University Kyoto 606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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Corrigendum to “Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches” [Comput. Electron. Agric. 188 (2021) 106359]

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computers and Electronics in Agriculture 2023年 207卷

作者： Gregorius Natanael Elwirehardja Jonathan Sebastian Prayoga Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta 11480 Indonesia

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science 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 Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data

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Procedia computer science 2021年 179卷 202-210页

作者： Arif Budiarto Bharuno Mahesworo Alam Ahmad Hidayat Ika Nurlaila Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

关键词： Population Stratification Ancestry Estimation Gaussian Mixture Model Genomics SNPs

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computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries 3

Computer Vision and Fuzzy Logic for Sustainable Indonesian F...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Saputra, Adi Sakinah, Putri Purwoko, Hery Zulardi, Indra Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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