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All Journal AbdiTechno (Jurnal Pengabdian Masyarakat) Jurnal Minfo Polgan (JMP)
Situmorang, Ayu Triana
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences

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PELATIHAN PENGGUNAAN ALAT PEMERAS SANTAN DAN MESIN PENGUPAS SABUK KELAPA PADA BUMDES SUMBER BERKAT DESA MARINSOW Sanjaya, Ricardo; Sidik, Dilan Richardo; Somba, Junri; Alfredo, Jannifer; Sarubang, Gabriel Vieri Sarubang; Moningka, Gregorius Viktor; Simbolon, Efriwandy; Sthiefandy, Alvin; sinaga, Amos Axcel; Pomalingo, Moh. Fikri; Bandu, Rahmat; Pabubung, Kurnia; Silalahi, Rike Firda; Situmorang, Ayu Triana; Sinaga, Ramesintia; Saerang, Syefiorita Angela; Zega, Cici Chrisdayanti
Abdi Techno Jurnal AbdiTechno, Vol. 4, Nomor 1, Januari 2024
Publisher : Departemen Teknologi Pertanian Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70124/abditechno.v4i1.1146

Abstract

Decree of the President of the Republic of Indonesia, Likupang became one of the priority tourist destinations. This is certainly a blessing for the people of North Sulawesi, especially Likupang. One of the industries in Marinsow Village is producing klentik coconut oil (village oil). Klentik coconut oil (village oil) is a product derived from coconuts. This oil has a distinctive taste and aroma for the fish meat that will be served. This product is also one of North Sulawesi's export commodities. In the process of making klentik coconut oil, there are several stages that must be passed. These stages include, peeling the coconut, splitting the coconut, grating the coconut meat, making coconut milk, stirring, fermenting, separating and cooking. Based on the results of the team's interviews with partners, several problems were raised. These problems include, 1) the equipment used is still traditional, 2) peeling coconut is still manual and takes a lot of time and energy, 3) the process of separating oil and water in coconut milk still takes 12-15 hours, 4) there is no business management and 5) the product is not yet standardized. The methods used in this activity are surveys, tool manufacturing, machine use training and reporting. From the results obtained, the participants were very enthusiastic about taking part in the training as evidenced by the presence of the participants. As a result of the interviews, the training participants were able to explain orally the components that make up the tools 80% of the time. The presentation value was not optimal, because there were 2 people aged 56 and 60 years. Both of them were unable to remember and were shy in explaining. For practical material, all participants with a percentage of 100% were able to process coconut milk using a homogeneizer machine or coconut peeling
Sentiment Analyisis On Twitter Social Media Towards Minahasa Using Naive Bayes Algorithm Situmorang, Ayu Triana; Rantung, Vivi Peggie
Jurnal Minfo Polgan Vol. 12 No. 2 (2023): Artikel Penelitian 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i2.13416

Abstract

Sentiment analysis on social media is an effort to understand the opinions, attitudes, and emotions contained in content shared online. The study focused on sentiment analysis o f Minahasa, an area located in North Sulawesi using data obtained from the social media platform Twitter. Through the use of the Naïve Bayes algorithm, this approach aims to understand and analyze sentiment patterns associated with Minahasa. The Naïve Bayes method used in this study is a classification technique that bases its predictions on probabilities and assumptions about the independence of observed features. By utilizing text data from tweets related to Minahasa, the algorithm can classify the sentiment of each tweet into positive, negative, or neutral. This analysis allows for a deeper understanding of people's views reflected in online conversations. The data obtained from Twitter amounted to 915 tweet data which was then analyzed so as to get a variety of negative, positive and neutral sentiments.
Co-Authors Alfredo, Jannifer Bandu, Rahmat Moningka, Gregorius Viktor Pabubung, Kurnia Pomalingo, Moh Fikri Saerang, Syefiorita Angela Sanjaya, Ricardo Sarubang, Gabriel Vieri Sarubang Sidik, Dilan Richardo Silalahi, Rike Firda Simbolon, Efriwandy sinaga, Amos Axcel Sinaga, Ramesintia Somba, Junri Sthiefandy, Alvin Vivi Peggie Rantung Zega, Cici Chrisdayanti