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All Journal Jurnal Algoritma Jurnal PkM MIFTEK Jurnal Algoritma
Faisal, Ridwan Nur
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Analisis Sentimen Pengguna Twitter dalam Pemilihan Presiden (PILPRES) 2024 dengan Menggunakan Algoritma K-Means Amin, Abdusy Syakur; Kurniadi, Dede; Nurzaman, Muhammad Zein; Nurfadillah, Rifa Sri; Khoerunisa, Sarah; Khaerunisa, Nisrina; Ajiz, Rafi Nurkholiq; Jembar, Tegar Hanafi; Faisal, Ridwan Nur
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1596

Abstract

One form of upholding democracy carried out by the Unitary State of the Republic of Indonesia is through holding presidential elections or often known as presidential elections. which is held every five years to elect the next President. Apart from that, in this digital era, people are increasingly actively using social media to convey their views, opinions and sentiments regarding the presidential election. Ahead of the 2024 presidential election, many groups such as political parties, success teams, buzzers and supporters are using social media as a campaign medium to increase the popularity and electability of their prospective candidates. One of the social media that is widely used in political party promotion media is Twitter. Which is used by people to post various comments that can be positive or negative regarding the election. Sometimes, people also express hoax opinions before or during the election. Considering that comments on Twitter are currently difficult to categorize as positive or negative, sentiment analysis is needed to understand public attitudes towards the presidential election. This research aims to evaluate text documents and determine whether the documents have a positive or negative sentiment orientation. Apart from that, the method used is K-Means to cluster the data. The results of this weighting are in the form of positive and negative sentiment. Data taken from Twitter regarding the 2024 presidential election (pilpres) totaling 1015 tweet data.
Analisis Sentimen Pengguna Twitter dalam Pemilihan Presiden (PILPRES) 2024 dengan Menggunakan Algoritma K-Means Amin, Abdusy Syakur; Kurniadi, Dede; Nurzaman, Muhammad Zein; Nurfadillah, Rifa Sri; Khoerunisa, Sarah; Khaerunisa, Nisrina; Ajiz, Rafi Nurkholiq; Jembar, Tegar Hanafi; Faisal, Ridwan Nur
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1596

Abstract

One form of upholding democracy carried out by the Unitary State of the Republic of Indonesia is through holding presidential elections or often known as presidential elections. which is held every five years to elect the next President. Apart from that, in this digital era, people are increasingly actively using social media to convey their views, opinions and sentiments regarding the presidential election. Ahead of the 2024 presidential election, many groups such as political parties, success teams, buzzers and supporters are using social media as a campaign medium to increase the popularity and electability of their prospective candidates. One of the social media that is widely used in political party promotion media is Twitter. Which is used by people to post various comments that can be positive or negative regarding the election. Sometimes, people also express hoax opinions before or during the election. Considering that comments on Twitter are currently difficult to categorize as positive or negative, sentiment analysis is needed to understand public attitudes towards the presidential election. This research aims to evaluate text documents and determine whether the documents have a positive or negative sentiment orientation. Apart from that, the method used is K-Means to cluster the data. The results of this weighting are in the form of positive and negative sentiment. Data taken from Twitter regarding the 2024 presidential election (pilpres) totaling 1015 tweet data.
KKN Tematik Penerapan Teknologi Dalam Rangka Mendukung Percepatan Pemulihan Ekonomi Pedesaan Mulyani, Asri; Faisal, Ridwan Nur; Sopian, Alpi; Nuraisah, Tintin; Prayoga, Hardi; Alamsyah, Fathi Ridwan; Firdaus, Raden Syaban; Aulia, Wafa Gaida; Ramdhani, Nabila Aprilia; Suwandy, Mochamad Riefky Rafliana; Ardimansyah, Dendi; Gumelar, Agung; Ruspa, Rena; Mustaatinah, Tutin; Aldiansah, Aldiansah; Oktavian, Gilang Anhari; Arifin, Pipin Zaenal; Banowati, Rika; Andarista, Hilda Dian
Jurnal PkM MIFTEK Vol 4 No 1 (2023): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.4-1.1327

Abstract

Real Work Lecture is a mandatory activity that combines the implementation of the Tri Dharma of Higher Education with the method of providing learning and work experience to students. KKN is also a vehicle for the application and development of science and technology which is carried out outside the campus within certain times, work mechanisms, and requirements. The real work lecture that has been held in Sindangprabu Village, Wanaraja District, Garut Regency, West Java during August 2022, with this Thematic Real Work Lecture is expected to help with the difficulties faced by the government, including in handling the development of villages in the Wanaraja sub-district, district Garut in an effort to increase the Human Development Index (IPM) of Garut and West Java. In general, the shift in the economic structure that occurred in Garut and West Java districts looks the same, namely the shift from the primary sector to the secondary and tertiary. Garut's condition only relies on the agricultural sector, of course the economy so that, on its way to the industrialization transition area, Garut district really needs to build a foundation a strong economy, including creating human resources to increase village potential and independent industrial commodities or industries that make better use of domestic resources than imports.
Co-Authors Abdusy Syakur Amin Agung Gumelar Ajiz, Rafi Nurkholiq Alamsyah, Fathi Ridwan Aldiansah, Aldiansah Andarista, Hilda Dian Ardimansyah, Dendi Arifin, Pipin Zaenal Asri Mulyani Aulia, Wafa Gaida Banowati, Rika Dede Kurniadi Firdaus, Raden Syaban Jembar, Tegar Hanafi Khaerunisa, Nisrina Khoerunisa, Sarah Mustaatinah, Tutin Nuraisah, Tintin Nurfadillah, Rifa Sri Nurzaman, Muhammad Zein Oktavian, Gilang Anhari Prayoga, Hardi Ramdhani, Nabila Aprilia Ruspa, Rena Sopian, Alpi Suwandy, Mochamad Riefky Rafliana