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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Caraka, R.E. Tahmid, M. Putra, R.M. Iskandar, A. Mauludin, M.A. Hermansah Goldameir, N.E. Rohayani, H. Pardamean, B. Bina Nusantara University Jakarta11480 Indonesia Indonesia Agency for Meteorology Climatology and Geophysics Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Laboratory of Sociology and Extension Universitas Padjadjaran Indonesia Universitas Riau Kepulauan Batam Indonesia Riau University Indonesia Universitas Adiwangsa Jambi Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The transfer of land has an impact on the decreasing of the agricultural land area, so it is necessary to plan the right cropping pattern as an effort to increase the productivity of agricultural cultivation. Also, Climate is one of the factors influencing the determination of cropping pattern in a region. Analysis of climatic characteristics in the determination of cropping pattern not only based on oldeman climatic classification. At the same time, the study of groundwater availability for plants and climogram of climatic elements is needed in determining planting schedule which is suitable for planting and determination of irrigation water supply, either the right amount of time. This study aims to identify and build spatial of the oldeman climate classification. It also determines the cropping pattern for seasonal crops based on the calculation of water balance and climogram (rainfall and air temperature) in each type of climate oldeman district of Cirebon and surrounding areas. Data used monthly rainfall data period 1981-2010 from BMKG Jatiwangi station and air temperature data, plant coefficient and the base map of Cirebon region. The analysis method includes classification of Oldeman climate type, spatial data processing, water balance calculation by Thorntwaite & Matter method and making climogram (rainfall and temperature) with: k = p0 + p1/FC;p0 = 1.000412351;p1 = -1.073807306 and determination of planting pattern of each type of oldeman. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Groundwater

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Harsawardana Bayu Samodro Bharuno Mahesworo Teddy Suparyanto Don Bosco Surya Atmaja Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans.

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Decision support system using data warehouse for retail system

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Journal of Physics: Conference Series 2019年第1期1367卷

作者： Abba Suganda Girsang Geri Arisandi Calista Elysisa Michelle Melva Hermayanty Saragih Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

CMK company is a retail company that sell and distributes miniature vehicle items to small stores. CMK has several branches in various countries that use for operational and storage warehouse. This company faces the problem for analysing sales to decide the useful strategy since this company only uses Online Transaction Processing (OLTP) database. This study implements Online Analysis Processing (OLAP) database and data warehouse to provide useful information for this company by using the nine-step method designed by Kimball & Ross. The data will be performed into dashboard to make easier for CMK Company to analyze data. Furthermore, many useful information can be provided in short and efficient time.

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of Data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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The Matter of Deep Reinforcement Learning Towards Practical AI Applications

The Matter of Deep Reinforcement Learning Towards Practical ...

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IEEE International Conference on Ubi-Media Computing

作者： Tipajin Thaipisutikul Yi-Cheng Chen Lin Hui Sheng-Chih Chen Pattanasak Mongkolwat Timothy K. Shih Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Management National Central University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Faculty of Information and Communication Technology Mahidol University Thailand

ISBN: (数字)9781728128207

ISBN: (纸本)9781728128214

Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods.

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A Whole Crow Search Algorithm for Solving Data Clustering

A Whole Crow Search Algorithm for Solving Data Clustering

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Ze-Xue Wu Ko-Wei Huang Abba Suganda Girsang Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan R.O.C Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

Data clustering is a well-known data mining approach that usually used to minimizes the intra distance but maximizes inter distance of each data center. The cluster problem has been proved to be an NP-hard problem. In this paper, a hybrid algorithm based on Whole optimization algorithm (WOA) and Crow search algorithm (CSA) is proposed, namely HWCA. The HWCA algorithm has the advantages of the search strategy of the WOA and CSA. In addition to, there are two operators used to improve the quality of solution, namely hybrid individual operator and enhance diversity operator. The hybrid individual operator is used to exchanges individuals from the WOA and CSA systems by using the roulette wheel approach. In other hand, the HWCA performs enhance diversity operator to improve the quality of each system. More over, the HWCA is incorporated with center optimization strategy to enhance diversity of each system. In the performance evaluation, the proposed MPGO algorithm was comparison WOA and CSA algorithm with six well-known UCI benchmarks. The results show that the proposed algorithm has a higher measure of accuracy rate with comparison algorithms.

关键词： Clustering algorithms Optimization Whales Search problems Wheels Benchmark testing Random variables

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

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Procedia computer science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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Measurement of QuestDone mobile application using 7 steps use case points method

Measurement of QuestDone mobile application using 7 steps us...

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2017 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCOM 2017

作者： Sutoyo, Rhio Hendric Spits Warnars, Harco Leslie Gaol, Ford Lumban Abdurachman, Edi Soewito, Benfano Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538607831

The rise of mobile application is inevitable. Every year, the number of mobile application is increased. It is important for mobile application project owners to calculate the required resources before building a mobile application. In software metric, Use Case Points method is able to count software size of mobile application based on their functionality. This method utilizes use case diagram as their computation factors in the estimation process. Moreover, two other complexity factors are also considered in this method, which are: Technical Complexity Factor and Environment Factor. In this paper, we present software size calculation of QuestDone Mobile Application using 7 steps use case points method. QuestDone has been implemented, but we do not know its software size (i.e. how big the software, how much it cost, how many people is needed). As the result from use case points method, the Use Case Points value of QuestDone is 126.88 with Effort Estimation equal to 889 hours. The software size estimation process of QuestDone Mobile Application detailed in this paper can give an insight to project owners to count software size of other similar projects. © 2017 IEEE.

关键词： Application programs

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Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era

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Journal of Physics: Conference Series 2021年第1期1722卷

作者： F C Permana Z M Wicaksono C Kurniawan A S Abdullah B N Ruchjana Study Program of Multimedia Education Universitas Pendidikan Indonesia Jl. Dr. Setiabudi No.229 Bandung 40154 Indonesia. Student of Master of Management Program Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Computer Science Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Mathematics Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia.

In the conditions of the Covid-19 Pandemic, the president of Indonesia, Ir Joko Widodo, on May 13, 2020, announced an increase in BPJS Kesehatan contribution. This announcement made everyone in the community boisterous, including Indonesians on social media twitter. Currently, Indonesia is ranked fifth in the world using social media twitter, so data mining on Twitter is a good opportunity to see the public's response to a developing issue. This paper will discuss perceptions analysis of the Indonesian people on social media regarding the issue of increasing the contribution of BPJS Kesehatan. The issue of increasing the contribution of BPJS Kesehatan has been a topic of conversation on Twitter for a long time. After 30 days of data crawling, 145,359 tweets were obtained. This amount of data proves that the Indonesian people are very active in issuing opinions regarding the issue of increasing contribution BPJS Kesehatan. Various kinds of differences of opinion in each conversation are classified into three types of opinion using the Naive Bayes method. The three types of opinions are classified into positive opinions, negative opinions, and neutral opinions. The opinion classification results obtained were 73% containing negative opinions, 18% positive opinions, and 9% neutral opinions. This can serve as an early warning for the government to see the public's response in every policy taken. So that each policy can be evaluated for the better.

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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