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All Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Anggit Dwi Hartanto
MTI Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Engineering

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ANALISIS SENTIMEN MASYARAKAT TERHADAP PELAKSANAAN P3K GURU DENGAN ALGORITMA NAIVE BAYES DAN DECISION TREE Fitriani Fitriani; Ema Utami; Anggit Dwi Hartanto
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 3 No. 1 (2022): Mei 2022
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v3i1.53

Abstract

The implementation of education in Indonesia is still inseparable from the problems of teacher management, honorary teachers, and bureaucratic reforms that affect the quality of education and the work climate in it. In an effort to improve the quality of public services by the State Civil Apparatus (ASN), the Ministry of Education and Culture agreed with the Ministry of Empowerment of State Apparatus and Bureaucratic Reform and the Ministry of Finance to change the government employee teacher recruitment system from accepting Civil Servant Candidates (CPNS) to Government Employees by Work Agreement (PPPK) which in its implementation still leaves several problems and pros and cons. Therefore, the researchers conducted a sentiment analysis in the field of data mining on the Implementation of Teacher Teacher Training on social media Twitter as many as 871 data which were then filtered and cleaned into 519 data due to duplicate data, empty data and data cleaning. The author uses the Naive Bayes and Decision Tree methods to compare the accuracy of the two methods. The researcher uses RapidMiner version 9.10.1 tools. The results showed that the sentiment analysis of Twitter data on teacher PPPK using the Naive Bayes method achieved an accuracy rate of 100.00%. Where is the class precision for pred. negative is 100.00% and pred positive is 100.00%, in the Decision Tree method the accuracy rate reaches 53.95%. Where is the class precision for pred. negative was 0.00% and pred positive was 53.95%. In this study, it can be seen that the Naive Bayes method is a method that has a higher accuracy rate than other methods with an accuracy rate of 100.00%. Keywords: Sentiment Analysis; PPPK; Indonesia; Data Mining
Comparison Analysis of Fuzzy Sugeno & Fuzzy Mamdani for Household Lights Miftah Alfian Firdausy; Ema Utami; Anggit Dwi Hartanto
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.1

Abstract

The rapid growth of knowledge and technology in the IoT field encourages scientists to make discoveries in facilitating daily activities by utilizing artificial intelligence, one of which is the Smart Home. Smart home technology is needed that has better advantages over existing building materials, one of which is home lighting or the use of lights. If all this time, houses still use manual control to turn the lights on and off, it will potentially cause the lights to turn on still even though they are not needed, for that we need a method used in the implementation of automatic light control. From several studies, the Fuzzy method is widely used in this case. This method has several models, but the author uses the Fuzzy Mamdani method and the Sugeno method to apply in this study. The variable at the input is the LDR sensor, while the output is a lamp. From the trial results of the two methods, and accuracy test was sought as a parameter for the better ana method. It can be concluded that the Sugeno method has a better accuracy rate of 88.25%, compared to Mamdani's, which is only 84.5%.
Co-Authors Ema Utami Fitriani Fitriani Miftah Alfian Firdausy