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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Riset Informatika INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Insan Jurnal Pengabdian Literasi Digital Indonesia
Wahyutama Fitri Hidayat
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Journal : Jurnal Riset Informatika

 1 
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol 3 No 4 (2021): Period of September 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.421 KB) | DOI: 10.34288/jri.v3i4.278

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using Bert Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.452

Abstract

News about the war that took place between Russia and Ukraine can not be denied affecting various aspects of life in the world. This affects the writings of every citizen of the world on various social media platforms, one of which is Twitter. Sentiment analysis is a process of identifying and making sentiment categories which is done computationally. Sentiment analysis process is also intended to make computers understand the meaning of sentences written by humans by processing using algorithms. This study uses a deep learning method using a language model, namely BERT (Bidirectional Encoder Representation Form Transformers) as a process of analyzing the sentiments that exist in tweets written about the war in Russia and Ukraine by twetter social media users. These sentiments will be divided into three parts, namely positive, neutral and negative. In this study, the hyperparameters used were 10 epochs, learning rate 2e-5, and batch size 16. The sentiment analysis test used the BERTbase Multilingual-cased-model model and the accuracy value obtained was 97%.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol. 3 No. 4 (2021): September 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i4.97

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using BERT Julianto, Muhammad Fahmi; Malau, Yesni; Hidayat, Wahyutama Fitri
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.169

Abstract

News about the war between Russia and Ukraine can not be denied affecting various aspects of life worldwide. It affects the writings of every world citizen on various social media platforms, one of which is Twitter. Sentiment analysis is a process of identifying and making sentiment categories computationally. The sentiment analysis process is also intended to make computers understand the meaning of human sentences by processing algorithms. This research uses the deep learning method of the BERT (Bidirectional Encoder Representation Form Transform) model language to analyze the sentiments in the tweets written about the wars between Russia and Ukraine by Twitter social media users. The sentiment will be divided into positive, neutral, and hostile. The hyperparameters in this study used ten epochs, with a learning rate of 2e-5 and a batch size of 16. The test used in sentiment analysis was the BERTbase Multilingual-cased-model model, and the accuracy was 97%. Suggestions for further research are the need for a more balanced dataset between positive, neutral, and negative sentiments. They reward the dataset before training so that better results are expected.
Co-Authors Ade Hendini Ahmad Setiadi Ali Mustopa, Ali Annida Purnamawati Bayu Pratama, Eri Destiana Putri Eri Bayu Pratama Fajar Sarasati Fajar Shidiq Fintri Indriyani Hermanto Hermanto Jordy Lasmana Putra Julianto, Muhammad Fahmi Kartika Handayani Khalila, Ayasha Noor Mochammad Imron Awalludin Muhamad Syarif Muhammad Fahmi Julianto Paulus Tofan Rapiyanta Rachmat Adi Purnama Rangga Sanjaya Taufik Asra Wawan Nugroho YANTO YANTO Yesni Malau