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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Artificial Intelligence
Nanda Try Luchia
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Perbandingan Algoritma K-Means Dan K-Medoids Pada Pengelompokan Humidity, Temperature, Dan Voltage Di Data Center Perawang Nanda Try Luchia; Mustakim Mustakim
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.938 KB) | DOI: 10.47065/josh.v4i1.2385

Abstract

A data center is a facility managed by a company for data storage (database) and telecommunications from all computer system components and needs. Monitoring the level of humidity, temperature, and voltage is needed to support the performance of the data center and can be done with AKCP. Because of that, it is necessary to group Humidity, Temperature and Voltage of Perawang DC to optimize the monitoring process. Various methods are used to make it easier for companies to determine the best grouping of performance data found in the data center. In this research, data was obtained from Systemlog AKCP PT. Arara Abadi, Perawang from January 21, 2022 – March 19, 2022. This research is expected to make it easier for companies to determine which algorithm is the right one for grouping air humidity, temperature and voltage levels in the Perawang data center by comparing two algorithms namely K-Means and K-Medoids. Based on the research results, K-Means is better in grouping the Humidity, Temperature and Voltage data of Perawang DC in PT. Arara Abadi, Perawang because it has accurate cluster accuracy compared to K-Medoids with a DBI value of 0.306 in the K=2 experiment and the process time is only 1 minute 22 seconds.
Implementasi Algoritma Support Vector Machine untuk Analisis Sentimen LGBT di Indonesia Mustakim Mustakim; Muhammad Ridwan; Nanda Try Luchia
Bulletin of Artificial Intelligence Vol 1 No 2 (2022): October 2022
Publisher : Graha Mitra Edukasi

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Abstract

LGBT cases began to appear openly in Indonesia in 2016. This case has received a lot of discussion in that year until now because of the number of people who commented agreeing and disagreeing with actions, activities, and the existence of the LGBT gender in Indonesia. The sentiments from the community's comments refer to various aspects of life so as to produce community opinions that are positive, negative and neutral. Seeing this, it is necessary to perform a classification and analysis of tweet sentiments to see the tendency of each community's opinion. Analysis and classification is done with text mining data processing techniques using the Support Vector Machine (SVM) algorithm. The classification process is done in 3 stages with the division of data 90%:10%, 80%:20% and 70%:30% using 3 kernels namely linear, polynominal and Radial Basic Function (RBF). The classification results obtained from the three kernels show that the tendency of society's view of LGBT cases is negative and neutral which is shown with the highest accuracy on the linear and RBF kernels. The SVM experiment produced an accuracy of 74% on the linear kernel with 90%:10% and 74% data experiments and on the RBF kernel with C=100 gamma=0,01. The grouping of this tweet sentiment data resulted in an analysis of the tendency not to support or disagree with the LGBT gender because it is not in accordance with the established basis in the country of Indonesia which prioritizes religious aspects over other aspects
Co-Authors Muhammad Ridwan Mustakim Mustakim