Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Algoritma Jurnal PkM MIFTEK Jurnal Algoritma
Jembar, Tegar Hanafi
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Pengembangan UMKM Melalui Peningkatan Pengetahuan Masyarakat Terhadap Proses Produksi dan Pemasaran Supriatna, Asep Deddy; Naufal, Shofwan Dzaki; Bintang, Muhammad; Pertiwi, Asri Indah; Nuraeni, Siska; Amelia, Shalma; Herlina, Lina; Arianto, Mohamad Jefry; Mutaqien, Rizky; Nasrulloh, Anas; Ajijah, Ayu Nur Isti; Jembar, Tegar Hanafi; Abdussalam, Iqbal; Aliansyah, Difa Eka; Fahreza, Reynaldi; Rohimat, M. Galuh Fajar; Rustandy, Sandy; Alamsyah, Hadi; Lestari, Ayu; Diningrat, Galant Ababil Haqti
Jurnal PkM MIFTEK Vol 3 No 2 (2022): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.3-2.1317

Abstract

The majority of MSME actors in Sukamenak Village have mostly elementary and junior high school education so they can be categorized as having low education. This has an impact on increasing production and marketing capacity. The main problem in marketing knowledge starts with MSME players, especially Papandak coffee and palm sugar, who only market their products without advertising support, both traditionally and digitally. The Digital Literacy Seminar training program regarding increasing MSME marketing aims to increase the capacity of MSME actors in marketing products and improve the quality of marketing knowledge. The methodology used is the ICT Volunteer Integration methodology, where the implementation method uses the offline method. The results achieved were by increasing the knowledge of MSME actors in product marketing so as to help increase the marketing capacity of MSMEs for Papandak coffee and palm sugar.
Co-Authors Abdussalam, Iqbal Abdusy Syakur Amin Ajijah, Ayu Nur Isti Ajiz, Rafi Nurkholiq Alamsyah, Hadi Aliansyah, Difa Eka Amelia, Shalma Arianto, Mohamad Jefry Asep Deddy Supriatna AYU LESTARI Bintang, Muhammad Dede Kurniadi Diningrat, Galant Ababil Haqti Fahreza, Reynaldi Faisal, Ridwan Nur Khaerunisa, Nisrina Khoerunisa, Sarah Lina Herlina Mutaqien, Rizky Nasrulloh, Anas Naufal, Shofwan Dzaki Nuraeni, Siska Nurfadillah, Rifa Sri Nurzaman, Muhammad Zein Pertiwi, Asri Indah Rohimat, M. Galuh Fajar Rustandy, Sandy