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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

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Adoption of Artificial Intelligence in Response to Industry 4.0 in the Mining Industry 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Information Systems Management Department Master of Information Systems Management BINUS Graduate Program Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Nowadays The Industrial Revolution 4.0 is identified by the growing implementation of technology in every business, one of the technologies it called Artificial Intelligence (AI). AI gives a solution for a company to manage efficient operations by finding solutions, relearning data insight, and implementing action to reduce inefficiencies in Operations. The confluence of information technology with industrial process automation and routine jobs provides for better operations, lower costs, and higher process quality. With the ability to extract and control data in real-time connected home and commercial equipment, technologies like big data, the internet of things, information technologies, and robotic operating systems have become important partners and pillars of both industry and society 4.0. PT Delta Dunia Makmur TBK (BUMA), Optimizes its efficient production cost by using Artificial Intelligence in order to help the company to manage its fuel consumption, however, AI has weaknesses due to high dependency on data integrity the company should improve those issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

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Predicting User Preferences with Neural Network Learning to Rank Method 5

Predicting User Preferences with Neural Network Learning to ...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty Of Computing And Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master Of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their target customers. The user preference is also needed in the movie domain area, as the film companies who produce the movie also need to understand how the customers value their movie products, whether the market like it or not. This can help them to predict the best components for the future movie. In order to produce the recommendation to the customer, we can show a list of item in a correct order, sorted from the most relevant/the most liked one to the most irrelevant item. In this paper, we utilize the Learning to Rank (LTR) approach from information retrieval domain area to learn the user preference in recommender system domain. We applied LTR with pairwise approach from the two most popular algorithms in this area, i.e. RankNet and LambdaRank, on 25, 345 user ratings from 5, 000 movie dataset and evaluate both algorithms. We used a Neural Network architecture as the learning mechanism. We also measuring the speed performance of the two algorithms. © 2021 IEEE.

关键词： Sales

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The Influence of Business Strategy, Leadership Styles and Business Process on Quality of Accounting Information 21

The Influence of Business Strategy, Leadership Styles and Bu...

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Proceedings of the 2021 5th International Conference on Software and e-Business

作者： Meiryani Meiryani Zaidi Mat Daud Jajat Sudrajat Tommy Andrian Suryadiputra Liawatimena Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia BINUS Entrepreneuship Center Management DepartementBINUS Business School Undergraduate Program Bina Nusantara University Indonesia Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Indonesia

ISBN: (纸本)9781450385831

This study aims to determine the effect of business strategy on the quality of accounting information, to determine the effect of leadership style on the quality of accounting information and to determine the effect of business processes on the quality of accounting information. This study uses primary data from a population of State-Owned Enterprises throughout Indonesia with a total sample of 61 state-owned enterprises, and is analyzed using a simultaneous equation estimation model or SEM (Structural Equation Modeling), the sample determination is done by purposive sampling. The results showed that (1) leadership style and business processes affect the quality of accounting information systems (2) business strategy has no effect on the quality of accounting information systems on accounting information systems at State-Owned Enterprises in Indonesia.

关键词： Business Strategy Leadership Style Business Process Accounting Information Systems Accounting Information Quality

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Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Trisetyarso, Agung Ramadhan, Arief Abdurachnan, Edi Lumbangaol, Ford Isa, Sani Muhamad Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499699

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings. © 2021 IEEE.

关键词： Decision trees

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The Classification Model of Learners Based on Diagnostic Assessment Results in E-learning With Machine Learning

The Classification Model of Learners Based on Diagnostic Ass...

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Software Engineering and Information Technology (ICoSEIT), International Conference on

作者： Mesra Betty Yel Muhammad Zarlis Saib Suwilo Syahril Efendi Poltak Sihombing Department of Computer Science Graduate Program of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Binus Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department Of Computer Science Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9781665473040

The development of technology and artificial intelligence, especially in the era of Industrial Revolution 4.0 almost covers all fields, one of which is e-learning. In its development, it is very important to determine the factors to produce the quality of e-learning. Machine learning can help to find various solutions in the real world, especially to solve problems for which a specific algorithm has not been found. The implementation of e-learning is often ineffective and not on target which has an impact on learning achievement that is not achieved, this is due to several things namely the learning model is applied in general, the learning materials and weights are applied equally to all users. Seeing these problems, it is necessary to have a model to classify students according to their abilities, talents, and interests before starting learning to achieve their goals to be achieved. This paper aims to create a machine learning approach model to determine the level of ability and needs of students before starting learning which can reduce disparities between students and follow up learning materials. With the machine learning based on learning model, e-learning can provide teaching materials according to the abilities, and interests of students, sustainably and improve the quality of students.

关键词： Training Electronic learning Machine learning algorithms Clustering algorithms Machine learning Classification algorithms Information technology

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Artificial Intelligence as a New Competitive Advantage in Digital Marketing in the Banking Industry 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Information Systems Management Department BINUS Graduate Program—Master of Information Systems Management Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No. 11 Jakarta Timur Jakarta13220 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Digital disruption has entered the banking industry, especially since the era of the COVID-19 pandemic. To remain competitive, every bank must adjust to the digital age. Reputation has a direct impact on a bank’s performance, capacity to draw in new clients, and capacity to keep hold of current ones. Making choices about how to address the difficulties of adopting artificial intelligence must consider these issues (AI). The financial sector as a whole now has a way to meet client expectations for more creative, practical, and secure solutions thanks to artificial intelligence (AI). The bank can swiftly decide which channel to use at what time and what material to target consumers with by utilizing AI. Of course, this is very important considering the shift in customer behavior in the digital era, so AI is expected to assist the Bank in providing products and services that are right on target according to customer needs and create a new competitive advantage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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