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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika IJCR (Indonesian Journal of Chemical Research)
Syahputra, Rahmat
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Materials Science & Nanotechnology

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SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter Syahputra, Rahmat; Yanris, Gomal Juni; Irmayani, Deci
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11430

Abstract

With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.
The Effect of Grinzest Bioadditives on BBM Fuel Consumption in Mining Vehicles at PT Arutmin Indonesia Kintap Fitri, Noor; Sulistia, Dona; Abraham, Ali; Aditya Dharma, Irfan; Habibullah, Marhaban; Maulana, Ibnu; Effendi, Fuad; Khofifah; Mauludiyah, Riskiyatul; Sukri, Qomarudin; Syahputra, Rahmat; Ila Nurhuddah, Ika
INDONESIAN JOURNAL OF CHEMICAL RESEARCH Vol. 9 No. 2 (2024): Volume 9, ISSUE 2,2024
Publisher : Chemistry Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/ijcr.vol9.iss2.art13

Abstract

Mining industry activities have a very high dependence on fuel consumption. This dependence on the use of fuel is because diesel fuel is the main energy source to drive vehicle activities in the mining industry. Fuel consumption of PT. Arutmin Indonesia Tambang Kintap is around 40% of production costs. Given this substantial fuel demand, improving fuel efficiency is crucial, making fuel-saving measures essential. One approach to reducing fuel consumption involves adding bio-additives to enhance the fuel combustion process. The purpose of this study was to determine the effect of adding Grinzest bio additives to the fuel used by heavy machinery at PT. Arutmin Indonesia Tambang Kintap. The research steps include: (1) characterization of the fuel and bio additives; (2) blending fuel with Grinzest bio additives; (3) characterization of BBM-Grinzest blending; (4) testing the performance of Grinzest bioadditives on heavy machinery. The results of the study showed that Grinzest bio additives were able to reduce fuel consumption, reduce gas emission levels, and prevent rust (deposits) on the engine. The results of tests conducted by the team in the field showed that the addition of bioadditives to fuel with a ratio of 1:1000 showed a decrease in fuel consumption of around 7.4%.
Co-Authors Aditya Dharma, Irfan Ali Abraham, Ali Effendi, Fuad Habibullah, Marhaban Ila Nurhuddah, Ika Irmayani, Deci KHOFIFAH Maulana, Ibnu Mauludiyah, Riskiyatul Noor Fitri Sukri, Qomarudin Sulistia, Dona Yanris, Gomal Juni