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Improvement Suggestion Performance of Blowing Machine Line 4 with Total Productive Maintenance (TPM) Method at PT. Coca-Cola Amatil Indonesia MedanUnit 1

Improvement Suggestion Performance of Blowing Machine Line 4...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Irwansyah, Defi Harahap, M.R.F. Erliana, Cut Ita Abdullah, Dahlan Sari, Ayuesteka Siregar, Nurintanasyiah Achmaddaengs, G.S. Indahingwati, Asmara Sumartono, Eko Wilujeng, Sri Nurmawati Subekti, Purwo Kurniasih, Nuning Rosalina, Febrianti Hartono, H. Departement of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Departement of Informatics Universitas Malikussaleh Aceh Indonesia Department of Management STIE Sakti AlamKerinci Jambi Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management Universitas 45 Surabaya Indonesia Department of Management School of Economics Indonesia STIESIA Surabaya Indonesia Faculty of Agriculture Universitas Bengkulu Bengkulu Indonesia Department of Management Faculty of Economy Universitas Kanjuruhan Malang Indonesia Program Study of Industrial Engineering University of 45 Surabaya Indonesia Mechanical Engineering Pasir Pengaraian University Riau Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Agrotechnology Universitas Muhammadiyah Sorong Sorong Indonesia Department of Computer Science STMIK IBBI Medan Indonesia

Maintenance is an activity to maintain facilities or equipment of the factory and make repairs or adjustments to the necessary replacement to satisfactory condition of production operations in accordance with what is planned. Companies often experience damage to the Blowing machine so that interconnected machines cannot be operated. The purpose of this study was to improve the performance of Blowing line 4 engines with the Total Productive Maintenance (TPM) method at PT. Coca-Cola Amatil Indonesia Medan Unit, also to determine the level of Blowing machine performance based on the analysis of overall equipment effectiveness (OEE) at PT. Coca-Cola Amatil Indonesia Medan Unit. The method used in this study is the overall equipment effectiveness (OEE) method to measure Ishikawa's treatments and diagrams to compare and propose low engine performance. The results of data processing shows machine maintenance has not comply the OEE standards in July 81.48%, August 81.48%, September 78.07%, October 81.22%, November 78.08%, and those that fill the standards in June amount to 86.48%, with losses that most affect the effectiveness of equipment in line 4 blowroom machine is Equipment Failure Losses of 11.42% based on the results of the study, the proposed improvement in engine performance is to provide training to employees, and increase operator awareness. against engine damage, thus the production process will run smoothly. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis

The Scientific Progress and Prospects of Artificial Intellig...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Puput Dani Prasetyo Adi Dian Lestari Sandy Vikki Ariyanto Agung Purnomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta Indonesia Faculty of Medicine Universitas Airlangga Surabaya Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years.

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Development and Validation of Customer Intention Factors for Live-Stream Shopping

Development and Validation of Customer Intention Factors for...

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Conference on New Media Studies (CoNMedia)

作者： Erick Fernando Desman Hidayat Christian Haposan Pengaribuan Yulius Denny Prabowo Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Management Department BINUS Entrepreneurship Center Bina Nusantara University Jakarta Indonesia Management Study Program Faculty of Business Sampoerna University Jakarta Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables.

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Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Natalina Br Sitepu Sawaluddin M Zarlis Syahril Efendi Hanna Willa Dhany Informatic Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatra Utara Medan - Indonesia

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.

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The Online Test application uses Telegram Bots Version 1.0

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Journal of Physics: Conference Series 2020年第2期1569卷

作者： Rizky Parlika Arista Pratama Informatics Engineering Study Program Faculty of Computer Science Information Systems Study Program Faculty of Computer Science National Development University 'Veteran' East Java Raya Rungkut Madya Road Gunung Anyar Surabaya

The development of the smartphones that rapidly increasing, and high smartphone Users as a tool to make a lot of information, make emergence of instant messaging applications with a variety of options for use. Telegram, as one of the instant messaging application that is fairly new, offering in its various features compared to other instant messaging applications, so it can grow rapidly and much in demand in only 2 years. One of the interesting features is the Bot telegram, a special account without number that can handle Command from the User and gives answers appropriate to Command functionality. The account serves as the interface of the running system that communicate via Telegram Bot API. This study seeks to build a Bot telegram with database to built The Online Test application uses Telegram Bots Version 1.0. In each lecture meeting, a Lecturer can make a short test in the form of multiple choices to review the material at the previous meeting and get information about what percentage of students understand the material that has been taught before. We can use the application to send realtime messages to facilitate the test. Of course, which supports the creation of bots easily to add features that did not yet exist. Of the many we chose Telegram to be an online test environment by utilizing the Bot creation facilities provided by Telegram. This article will be a guide that makes it easier for lecturers to give a simple test at the beginning of the meeting, so that they can attract students' daily grades. And in order to be more interactive there are features of student value progress reports that can also be accessed individually so that students can know the progress of their learning outcomes.

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Use of the Web API as a basis for obtaining the latest data on bitcoin prices at 30 exchange places

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IOP Conference Series: Materials science and engineering 2021年第1期1125卷

作者： Rizky Parlika Arista Pratama Informatics Engineering Study Program Faculty of Computer Science National Development University 'Veteran' East Java Information Systems Study Program Faculty of Computer Science Raya Rungkut Madya Road Gunung Anyar Surabaya

Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.

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Analysis backpropagation methods with neural network for prediction of children's ability in psychomotoric

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Journal of Physics: Conference Series 2018年第1期978卷 012085-012085页

作者： Izhari, F Dhany, H W Zarlis, M Sutarman Informatics Engineering Study Program Master Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.

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Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models

Digital Forensics Investigation and Incident Response in Int...

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International Conference on Wireless and Telematics (ICWT)

作者： Syaiful Ahdan Eki Ahmad Zaki Hamidi Sampurna Dadi Riskiono Ridha Muldina Negara Ratna Mayasari Suci Ramadona Ade Nurhayati Adi Sucipto Jupriyadi Galih Nugraha Nurkahfi Dzata Farahiyah Muchammad Naseer Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Elearical Engineering Study Program Politeknik Caltex Riau Riau Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta Indonesia National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Yogyakarta Indonesia Informatics Department Sekolah Tinggi Teknologi Bandung Bandung Indonesia

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article.

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Villages Status Classification Analysis Involving K-Means Algorithm To Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work programs

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Paska Marto Hasugian Harvei Desmon Hutahaean Bosker Sinaga Sriadhi Saranom Silaban Informatics Engineering Study Program STMIK Pelita Nusantara Medan Indonesia Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan 20221 Indonesia Departement of chemistry Universitas Negeri Medan Medan 20221 Indonesia

Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21.

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