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All Journal Jifosi Nusantara Science and Technology Proceedings
Irwan Afandi, Mohamad
Information System Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology

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Journal : Nusantara Science and Technology Proceedings

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Intelligent Fishcarelab System (IFS) for Remote Monitoring of Koi Fish Farming System Tuhu Agung Rachmanto; Minto Waluyo; Mohamad Irwan Afandi; Basuki Rahmat; Helmy Widyantara; Hariyanto Hariyanto
Nusantara Science and Technology Proceedings International Seminar of Research Month Science and Technology in Publication, Implementation and Co
Publisher : Future Science

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Abstract

Intelligent Fishcarelab System (IFS) is designed as online monitoring fish farming system. IFS hardware consists of mechanical and electronic systems. Mechanical system consists of water tanks and piping systems. While the electronic system comprises sensors temperature, pH and Dissolve Oxygen (DO). These sensors include signal conditioning circuit. Furthermore, by using Analog to Digital Converter (ADC) module the data can be read by the microcontroller circuit. Microcontroller circuit is assigned to conduct sensor readings and sends data to the server to inform water conditions. IFS in the operating system hardware requires microcontroller-based software and web-based software for monitoring water quality and feeding automatically and scheduled. Furthermore, this system apart can work directly in the area of fish farming can also be monitored remotely using an Internet connection.
Designing Business Process Improvement that Concentrates on IT Utilization at LPPM UPN “Veteran” Jawa Timur Mohamad Irwan Afandi; Eka Dyar Wahyuni
Nusantara Science and Technology Proceedings 5th International Seminar of Research Month 2020
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.0911

Abstract

To enhance operational efficiency, several organizations have pursued business process re-engineering initiatives. Inevitably, this initiative involves the modernization and modification of existing information systems to support business processes. UPN "Veteran" Jawa Timur is no exception; as a higher education institution carrying out its tri-dharma mission, UPN needs to improve its efficiency continuously. Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) manages two of UPN "Veteran" Jawa Timur's tri-dharma related to research and community service. LPPM UPN Veteran itself is classified in an "independent" cluster so that independent research grants can be handled internally. This LPPM oversees the processes of administration and the execution of analysis and community service. There are many obstacles to implementing these processes at this time, such as data that is not recorded in a good structure, making it challenging when information is immediately required. And the most critical thing is that these data are still not integrated with performance data for lecturers. Therefore, this research aims to solve these problems by reviewing existing business processes and developing new business processes to improve current business processes. The result of this research is a list of functional and non-functional requirements of applications that will be developed.
Feature Extraction for Sentiment Analysis in Indonesian Twitter Eka Dyar Wahyuni; Amalia Anjani Arifiyanti; Mohamad Irwan Afandi
Nusantara Science and Technology Proceedings 5th International Seminar of Research Month 2020
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.0913

Abstract

Twitter's sentiment analysis is one of the most interesting fields of research lately. It intertwines the natural language processing techniques with data mining. Up to this point, many algorithms have been proposed to better understand sentiment from text. The proposed method can be focused on the preprocessing step, dataset splitting method (training and testing), dataset balancing method (when the data is unbalanced), to the improvement of the existing algorithm. But, the main focus of this paper is on feature extraction from tweets using TF-IDF. The features obtained from this process are expected to improve the accuracy of the classification process. The dataset used in this research is in Indonesian, which has a very different form when compared to English. This dataset consists of 1068 manually labeled tweets related to the "school from home" policy caused by the COVID-19 outbreak, taken from March to July. All steps required to process this data will be implemented using python. To validate its utility, the performance of the proposed method is compared with each other. Finally, the results are summarized by reflecting on the impact of the inclusion of the proposed features for each classification algorithm for sentiment detection
Co-Authors Adian Riswandha Agussalim, Agussalim Amalia Anjani Arifiyanti Basuki Rahmat Hariyanto Hariyanto Helmy Widyantara Minto Waluyo Rizka Hadiwiyanti Tuhu Agung Rachmanto Wahyuni, Eka Dyar Wasenanto, Bergas