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作者： Nguyen, Van Du Truong, Hai Bang Merayo, Mercedes G. Nguyen, Ngoc Thanh Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Viet Nam Department Sistemas Informáticos y Computación Universidad Complutense de Madrid Spain Faculty of Computer Science and Management Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh city Viet Nam

Recently, the use of the wisdom of crowds (WoC) for finding solutions to a wide range of real-life problems has dramatically expanded. Prior studies have revealed that diversity, independence, decentralization, and aggregation are the determinants of collective wisdom. However, these findings are often based on the so-called point estimates - single values are used as the representations of individual predictions on the task of estimating unknown quantities or predicting outcomes of future events. In some situations, interval values, which are often called interval estimates, can be used for such representations. Accordingly, one can provide an individual prediction in the form of an interval value including a lower and an upper bounds. Taking into account this kind of representation, in this paper, we present a case study in which collectives of randomly selected predictions can outperform those of most accurate predictions. Then, we evaluate the WoC level by taking into account diversity and cardinality. The computational experiments have indicated that diversity is positively related to collective wisdom. Finally, we discuss some related theoretical and practical implications for further research. © 2019 - IOS Press and the authors. All rights reserved.

关键词： Forecasting

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A review on cleaning techniques of solar photovoltaic panels

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AIP Conference Proceedings 2023年第1期2847卷

作者： Maryam Nooman Al Mallahi Mamdouh El Haj Assad Oussama Rejeb Haleh Delnava Sara Asaad Yong Chai Tan Industrial Engineering and Engineering Management Department University of Sharjah Sharjah P.O. Box 27272 United Arab Emirates Sustainable and Renewable Energy Engineering Department University of Sharjah Sharjah P.O. Box 27272 United Arab Emirates Sustainable Energy Development Research Group Sustainable Energy and Power Systems Research Center University of Sharjah Sharjah UAE Institute of Manufacturing Information & System National Cheng Kung University Dasyue Rd East District Tainan City Taiwan Centre for Modelling and Simulation Faculty of Engineering Built Environment and Information Technology SEGi University Jalan Teknologi Kota Damansara 47810 Petaling Jaya Selangor Malaysia

Solar energy is increasingly becoming a significant source of clean energy. It harnesses a lot of benefits including the social, environmental, and economic aspects, and increases the progress toward the United Nation’s Sustainable Development Goals (SDGs). Photovoltaic (PV) system installations in rural areas and deserts suffer from dust accumulation on PV surfaces reducing the solar radiation penetration, thus reducing the output generated from the PV system. As a result, in order to maintain consistent performance, PV panels must be cleaned on a frequent and efficient basis. The objective of this paper is to provide a review of dust cleaning methods for solar PV panels with an emphasis on a sustainable perspective for dust separation. There are various cleaning methods, including passive, active, and manual cleaning. This paper shows the progress of current cleaning methods through extensive research. Plenty of research has been done on various cleaning techniques for solar photovoltaic panels. The optimum cleaning method mainly depends on various parameters and the available budget. However, researchers are generally promoting nanofilm coating and robotic systems while considering PV plant size, design, tilt angle, location, water availability at the site, and type of dust.

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Application of Message Security Application Using Vigenere Cipher Algorithm Utilizing One Time Pad (OTP) Algorithm as a Key Generator 1

Application of Message Security Application Using Vigenere C...

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1st Workshop on Environmental Science, Society, and technology, WESTECH 2018

作者： Pardede, A.M.H. Br Sitepu, Lorena Perbina Zarlis, Muhammad Tulus Iskandar, Akbar Sriadhi, S. Manurung, Rosida Tiurma Siregar, Nurintan Asyiah Sari, Ayu Esteka Yunawati, Sri Mursyidah, M. Mursalin, M. Kurniasih, Nuning Kurniawan, Citra Winarno, Edy STMIK Kaputama Sumatera Utara Binjai Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Program Study of Accounting Pasir Pengaraian University Riau Indonesia Departement of Information Technology and Computer Politeknik Negeri Lhokseumawe Aceh Indonesia Department of Mathematics Education Universitas Malikussaleh Aceh Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Cryptography is the science and art of maintaining the security of messages when messages are sent from one place to another. One of the ways securing the form of text message information is by the encryption process using the Vigenere Cipher algorithm and utilizing the One Time Pad (OTP) algorithm as a key generator, where the message will be random when it is opened. The message encryption process used the vigenere cipher algorithm while OTP is used to secure the key with the same formula. After this research has been done, an application was designed to secure text messages by converting the text message into a random message so that the message was unreadable due to a secret message that could not be known by others. The results achieved can secure an encrypted message that cannot be reopened and if those messages were reopened, they must be decrypted. © Published under licence by IOP Publishing Ltd.

关键词： Cryptography

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第05期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Data Mining to Determine Correlation of Purchasing Cosmetics with A priori Method 1

Data Mining to Determine Correlation of Purchasing Cosmetics...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Pardede, A.M.H. Cahayati, Eli Iskandar, Akbar Sari, Ayu Esteka Arifin, Muhammad Sari, Herlinalatipa Nuryana, Ida Siregar, Nurintan Asyiah Surya, Sara Apituley, Lilian G F Wuryani, Eni Kuraesin, Arlis Dewi Erliana, Cut Ita Setiyadi, Didik Department of Informatics UniversitasMalikussaleh Aceh Indonesia STMIK Kaputama Binjai Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti AlamKerinci Jambi Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Departement of Information Technology UniversitasDehasen Bengkulu Indonesia Department of Management Faculty of Economic Universitas Kanjuruhan Malang Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Pharmacy University of Dharma Andalas Padang Indonesia Departement of Law Halmahera University Maluku Utara Indonesia Faculty of Economics Universitas Negeri Surabaya Indonesia STIE Muhammadiyah Jakarta Indonesia Department of Industrial Engineering UniversitasMalikussaleh Aceh Indonesia Department of Informatics STMIK Bina Insani Indonesia

Data mining is the process of analyzing data using software to find patterns and rules in the data set. Data mining can analyze large data to find knowledge to support decision making. In this study the Rule Association will be discussed as one of the data mining functions implemented using the A priori Algorithm. There will also be analyzed two support calculation techniques in candidate generation in the A priori Algorithm, such as: K-way and 2 Group-By in three sample datasets with transaction attributes id and item. In this study the problem of support calculation in candidate generation is the bottleneck of the A priori Algorithm where the improvement of the A priori Algorithm was emphasized on the candidate generation and the effectiveness of the A priori Algorithm. This research was lead on the Oracle RDBMS by utilizing WEKA tools to determine maximum support and confidence and to find out the correlation between products. The results shows the highest confidence value at 93% if you buy DeepClensingMilk and DeepClensingToner then you will buy Whitening Soap with confidence = 93%. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Implementation of Data Mining to Classify the Consumer's Complaints of Electricity Usage Based on Consumer's Locations Using Clustering Method 1

Implementation of Data Mining to Classify the Consumer's Com...

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1st Workshop on Environmental Science, Society, and technology, WESTECH 2018

作者： Pardede, A.M.H. Sembiring, Yusdiana Br Iskandar, Akbar Pitasari, Dyah Retno Sriadhi, S. Rianita, Dian Arifin, Muhammad Ririhena, Mersy Yoslin Siregar, Nurintan Asyiah Supriyono Sari, Ayu Esteka Tondo, Simson Zarlis, Muhammad Winarno, Edy Tulus STMIK Kaputama Sumatera Utara Binjai Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Law Universitas Halmahera Tobelo Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Department of Accounting Universitas Halmahera Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Information System Universitas Muria Kudus Kudus Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Public Administration Universitas Halmahera Tobelo Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

Data collected by PLN staff on customer's complaints in the usage of electricity are very huge and accumulate. Previously, there was no information about these kinds of complaints in all sub-districts included inside the company, PT. PLN Binjai. On the other hand, each year company experienced problems to classify all complaints in order to obtain customers with information to later be used as a basis for making policy/decision. Data mining used clustering method and K-Means algorithm to classify the data so that important information is obtained from customer's complaints on the electricity usage, using variable: types of complaints, power using by consumer and consumer's region. Data was analyzed using Matlab to produce cluster centers and obtained a relationship between variables obtained with groups with high number of complaints. Results revealed that from 500 customers who have complained, cluster 1 had 218 complaints namely NT Fuse Putus with power used was 1300 watt and was located in Binjai. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Usability evaluation of exercise games for medical interactive rehabilitation assistance (MIRA) system for tele-rehabilitation

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AIP Conference Proceedings 2022年第1期2644卷

作者： Norasikin Fabil Waidah Ismail Evi Indriasari Mansor Kavintheran A/L Thambiratnam Rimuljo Hendradi 1Faculty Science and Technology Universiti Sains Islam Malaysia Bandar Baru Nilai 71800 Nilai Negeri Sembilan Malaysia 2Business Analytics Master Program Abu Dhabi School of Management Abu Dhabi United Arab Emirates 3Photonics Research Centre Deputy Vice Chancellor (Research & Innovation) Office University of Malaya 50603 Kuala Lumpur Malaysia 4Information System Study Program Universitas Airlangga Indonesia Kampus C Surabaya Indonesia

Rehabilitation therapy is a fundamental need for patients that suffer from illness, effects after accident or surgery. The patients need to perform traditional rehabilitation for undergo clinical therapy sessions handled by a therapist in monitoring the patient's improvement. Once the patients discharge, they need to perform consistently exercise remotely at home or outside from rehabilitation centre, Telerehabilitation was introduced. One of the exercises to encourage patients is using exercise games (exergames). In this paper, we are concentrating on usability evaluation of exergames named Medical Interactive Rehabilitation Assistance (MIRA) system with Twenty-eight exercises performed by patients. We performed data collection using qualitative and quantitative for 45 patients from SOSCO Rehabilitation Centre in Malacca. The questionnaire consists of three sections - demographic, usage of the MIRA system and lastly participant's opinion. In conclusion, the findings showed positive feedback in using exergames as part of their rehab exercise that can lead into implementation of Telerehabilitation.

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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2019 IEEE International Conference on engineering, technology and Education, TALE 2019

作者： Sulistiyo, Edy Vindy Anwar, Nizirwan Warnars, Diana Teresia Spits Wibawa, Setya Chendra Brotosaputro, Goenawan Kristiadi, Dedy Prasetya Warnars, Harco Leslie Hendric Spits Hashimoto, Kiyota Universitas Negeri Surabaya Electrical Engineering Department Jawa Timur60213 Indonesia School of Computer Science Bina Nusantara University Game Application Technology Program Jakarta11480 Indonesia Esa Unggul University Faculty of Computer Science Jakarta11510 Indonesia Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Budi Luhur Information System Department Jakarta12260 Indonesia Raharja University Computer Science Department Tangerang15117 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Prince Songkla University Faculty of Technology and Environment Phuket Campus Thailand

ISBN: (纸本)9781728126654

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3. © 2019 IEEE.

关键词： Serious games

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data 1

DEA and Fuzzy Simple Additive Weighting for Benchmarking Qua...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Ongko, Erianto Erliana, Cut Ita Irwansyah, Defi Iskandar, Akbar Sari, Ayu Esteka Abdussakir Yuniningsih Siregar, Nurintan Asyiah Kahar, Muhammad Syahrul Waliulu, Raditya Faisal Amalia, Ainna Kurniasih, Nuning Arifin, Muhammad Muzid, Syafiul Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Economic and Business Universitas Pembangunan Nasional Veteran Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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