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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa
Neng Resti Wardani
Universitas Nusa Putra
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Public Health Other

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PEMBERDAYAAN MASYARAKATDAN PEMASARAN OLAHAN HASIL ALAM DESA BENCOY Muhammad Rangga Abdurrasyid; Muhammad Rifqi Abdul Jabbar; Neng Resti Wardani; Nunik Destria Arianti; Muhamad Muslih
Jurnal Abdi Nusa Vol. 1 No. 2 (2021): Oktober 2021
Publisher : LPPM Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (307.034 KB) | DOI: 10.52005/abdinusa.v1i2.25

Abstract

The village of bencoy has abundant agricultural potential or natural products so it is very good for improving the community's economy. Agricultural products or natural products owned by an area can be processed into various products, for example chips, rengginang, sale, cilok and so on. Empowerment activities and marketing of natural products are carried out by providing counseling and socialization to communities who have MSMEs and millennial youth farmer members with the aim of providing information and knowledge about processing natural products into various types of processed which are not only for their own enjoyment or consumption but also for family use. made into an effort that can improve the household economy and the village economy.
Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest Neng Resti Wardani; Sudin Saepudin; Cecep Warman
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.497

Abstract

This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal
Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest Neng Resti Wardani; Sudin Saepudin; Cecep Warman
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.497

Abstract

This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal
Co-Authors Cecep Warman Muhamad Muslih Muhammad Rangga Abdurrasyid Muhammad Rifqi Abdul Jabbar Nunik Destria Arianti Saepudin, Sudin