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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Andrianingsih, A
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes Wandani, Aprilia; Fauziah, F; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.365

Abstract

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about flash sale events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords "flash sale" and "flash sale shopee", the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword "flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash sale shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.
Sistem Pakar Diagnosa Penyakit dan Hama Tanaman Pepaya Menggunakan Metode Forward Chaining dan Naïve Bayes Prayoga, Aldo Rio; Wahyuddin, M. Iwan; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.376

Abstract

Papaya is an agricultural product that can be grown anywhere. This causes papayas to be susceptible to pests and diseases, which can cause delays in the harvest period. So we need an expert system application that can help diagnose pests and diseases in papaya so that it can make it easier for papaya plant cultivators. The research has a goal to create an expert system application that can provide information on papaya plant diseases and can make it easier to diagnose diseases that exist in papaya and can be accessed easily anywhere by the public. This system is designed using Forward Chaining and Naïvei Bayes Methods. This expert system application is expected to make it easier for users to diagnose diseases and pests on papaya plants without having to require experts directly, based on the discussion and results in this research, the accuracy value of this expert system application has an accuracy value of 95% in diagnosing diseases and pests on papaya plants.
Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes Wandani, Aprilia; Fauziah, F; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.365

Abstract

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about flash sale events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords "flash sale" and "flash sale shopee", the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword "flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash sale shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.
Sistem Pakar Diagnosa Penyakit dan Hama Tanaman Pepaya Menggunakan Metode Forward Chaining dan Naïve Bayes Prayoga, Aldo Rio; Wahyuddin, M. Iwan; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.376

Abstract

Papaya is an agricultural product that can be grown anywhere. This causes papayas to be susceptible to pests and diseases, which can cause delays in the harvest period. So we need an expert system application that can help diagnose pests and diseases in papaya so that it can make it easier for papaya plant cultivators. The research has a goal to create an expert system application that can provide information on papaya plant diseases and can make it easier to diagnose diseases that exist in papaya and can be accessed easily anywhere by the public. This system is designed using Forward Chaining and Naïvei Bayes Methods. This expert system application is expected to make it easier for users to diagnose diseases and pests on papaya plants without having to require experts directly, based on the discussion and results in this research, the accuracy value of this expert system application has an accuracy value of 95% in diagnosing diseases and pests on papaya plants.
Co-Authors Fauziah, F M. Iwan Wahyuddin Prayoga, Aldo Rio Wandani, Aprilia