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International Conference on Electronics Representation and Algorithm 2019, ICERA 2019

作者： Pratama, Bryan Saputra, Dedi Dwi Novianti, Deny Purnamasari, Endah Putri Kuntoro, Antonius Yadi Hermanto Gata, Windu Wardhani, Nia K Sfenrianto, Sfenrianto Budilaksono, Sularso Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program Universitas Mercubuana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Graduate School Master's Degree Computer Science Universitas Persada Indonesia YAI. Jakarta Indonesia

The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword "Brimob" with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%. © Published under licence by IOP Publishing Ltd.

关键词： Law enforcement

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Predicting User Preferences with XGBoost Learning to Rank Method 3

Predicting User Preferences with XGBoost Learning to Rank Me...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781728184067

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation. © 2020 IEEE.

关键词： Recommender systems

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Digitizing Farmers' Land Data Collection Systems in Indonesia with the Use of Tani Millenial Apps

Digitizing Farmers' Land Data Collection Systems in Indonesi...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mohammad Prasanto Bimantio Dian Pratama Putra Teddy Suparyanto Amallia Ferhat Nanda Satya Nugraha Alam Ahmad Hidayat Bens Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia Department of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.

关键词： Decision making Education Government Transforms Data collection Agriculture Recording

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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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Application for sugarcane plantation: Manpower and transportation planning

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AIP Conference Proceedings 2023年第1期2594卷

作者： H. G. Mawandha D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Sugarcane plantations in Indonesia are still managed in a labor-intensive manner, especially plantation-owned by farmers. Labor intensive means that every stage of work in sugarcane plantation management still uses manpower. One of the jobs in sugarcane plantations that requires a lot of labor is sugar cane harvesting. Trend changing in the workforce gender would affect the quantity and quality of jobs, which is determined by the workforce's capacity and the workload of the work performed. The result of this research was a sugarcane agricultural calculator that made it simple for farmers to measure manpower and transportation requirements in estimating harvest costs. Manpower and transportation of harvested sugarcane commodities were estimated through the workload approach, namely, crop production and manpower's ability to carry a load. These two components helped sugarcane farmers to determine manpower and transportation during the harvest. The final yield produced by the projected harvest labor cost would be known immediately.

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Cryptanalysis of quantum private comparison with six-particle maximally entangled states

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Modern Physics Letters A 2023年第7期38卷

作者： Tsai, Chia-Wei Yang, Chun-Wei Wang, Chun-Hsiang Lin, Jason Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung 404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung 406040 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung 40227 Taiwan

The measurement property of the six-particle maximally entangled state was used by Sun [Mod. Phys. Lett. A 37, 2250149 (2022)] to design a quantum private comparison (QPC) protocol. However, this study points out that the entanglement property of the six-particle maximally entangled state is inconsequential as a security measure;that is, participants waste resources to store the entanglement state and perform the Bell measurement without obtaining any additional security advantage. Although a third party (TP) cannot obtain information regarding the participants' secret messages through an attack, an attack renders the six-particle entanglement redundant. Accordingly, this study proposed an improved QPC protocol to prevent third-party attacks. © 2023 World Scientific Publishing Company.

关键词： entanglement state quantum key distribution quantum private comparison Secure multiparty computation

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Identifying Patterns in Dental Visit Attendance Among Pregnant Women: A Retrospective Cohort Study

AJPM Focus

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AJPM Focus 2025年第2期4卷 100322-100322页

作者： Al Jallad, Nisreen Manning, Samantha Mao, Xinyue Mehta, Parshad Wu, TongTong Cacciato, Rita Luo, Jiebo Li, Yihong Xiao, Jin Eastman Institute for Oral Health University of Rochester Medical Center Rochester New York United States Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester New York United States Master of Public Health Program Department of Public & Ecosystem Health Cornell University Ithaca New York United States Department of Computer Science University of Rochester Medical Center Rochester New York United States

Introduction: Understanding the factors influencing dental care utilization is crucial for enhancing treatment adherence and outcomes. This study evaluates dental care–seeking patterns among pregnant women in low-income community. Methods: The authors analyzed data from 311 pregnant patients and 1,111 visits (2019–2022) synchronized from dental and medical records. The primary outcome was showing up for scheduled dental visits. To identify visit-attending patterns, the authors used a model-based clustering method to cluster longitudinal data with categorical outcomes. A penalized generalized linear mixed-effects model was applied to identify relevant variables for the visit attendance trajectories within each cluster. Results: The study participants comprised 49.6% Black, 32.2% White, and 12.5% Hispanic women. The majority (89.07%) were holding Medicaid insurance. Among the 1,111 scheduled visits, 432 resulted in no-shows (38.8%), including failed and canceled appointments. The authors identified 3 distinct clusters of visit-attending patterns on the basis of their show-up rates: low demand/low appointment risk (85% attendance), high demand/high appointment risk (57% attendance despite multiple scheduled visits), and moderate demand/high appointment risk (55% attendance with fewer scheduled visits). Various determinants, such as race;age;inner-city residence;appointment timing;the COVID-19 era;type of scheduled dental treatment;and prior medical visits for conditions such as anxiety, depression, hypertension, and allergies, influenced the visit-attending behaviors within each patient group. Conclusions: The innovative clustering approach of this study successfully identified dental care–seeking patterns among pregnant women, suggesting its applicability to a broader demographic. Identifying potential modifiable factors that could enhance attendance at dental visits is essential for improving oral healthcare outcomes among underserved pregnant patients. © 2025 The Au

关键词： care-seeking patterns Pregnancy prenatal oral healthcare social determinants of health

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine 3

Maintaining the Quality and Aroma of Coffee with Fuzzy Logic...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Samodro, Bayu Mahesworo, Bharuno Suparyanto, Teddy Surya Atmaja, Don Bosco Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

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. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE Engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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