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All Journal Jurnal Teknologi Informatika dan Komputer Rambideun : Jurnal Pengabdian Kepada Masyarakat
Andika, Muhamad
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Public Health Social Sciences

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Besemah Language Translation Machine Model Based on Machine Learning with Recurrent Neural Network (RNN) Model Algorithm Andika, Muhamad; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Purnamasari, Susan Dian
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2614

Abstract

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.
Besemah Language Translation Machine Model Based on Machine Learning with Recurrent Neural Network (RNN) Model Algorithm Andika, Muhamad; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Purnamasari, Susan Dian
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2614

Abstract

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.
INOVASI PELAYANAN PUBLIK BERTEMA POSYANDU TERNAK BAGI PETERNAK DESA DANGIANG KABUPATEN LOMBOK UTARA Sagita, Suci Syafti; Fentika, Fena; Juwita, Kartika; Adekantari, Dyah Rizki; Andika, Muhamad; Hidayat, Rahmad; Dermawan, Muhammad Ari
RAMBIDEUN : Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 3 (2024): Rambideun: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Al Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/pkm.v7i3.3035

Abstract

Dangiang Village is one of the villages in Kayanagan District, North Lombok Regency, West Nusa Tenggara which has superior potential in the field of animal husbandry. However, farmers often face challenges in cultivation due to lack of knowledge, awareness of the importance of livestock health management, and innovation in increasing livestock productivity. The purpose of implementing this community service activity was to improve livestock health and productivity in Dangiang Village. The method of implementing the activity used was in the form of discussion and direct practice to provide active health services to livestock. The implementation stage included preparation, socialization and implementation, involving collaboration between UMMAT KKN students, local authorities, and livestock experts. The results of this activities carried out in Dangiang Village, North Lombok Regency with the theme of Livestock Posyandu showed a positive impact on livestock health and community welfare. Therefore, it is important to have regular health interventions for livestock to prevent disease, increase productivity, and educate farmers about proper care practices. Collaboration between UMMAT KKN students, local authorities, and livestock experts reflects the effectiveness of community involvement in overcoming agricultural challenges and promoting sustainable livestock processing practices. 
Co-Authors Adekantari, Dyah Rizki Dermawan, Muhammad Ari Fentika, Fena Ilman Zuhri Yadi Juwita, Kartika Sagita, Suci Syafti Susan Dian Purnamasari, Susan Dian Yesi Novaria Kunang