Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Jurnal Agrista Journal of Integrated Agribusiness Literatus The Indonesian Journal of Computer Science
Veny Betsy Saragih
Universitas Muhammadiyah Sampit
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
FAKTOR PENENTUAN BIAYA PRODUKSI PADI DI SUMBERTANI, TALAWI, BATU BARA Saragih, Veny Betsy; Supriana, Tavi; Chalil, Diana
Jurnal Agrista Vol 23, No 2 (2019): Volume 23 Nomor 2 Agustus 2019
Publisher : Fakultas Pertanian, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (731.792 KB)

Abstract

Abstrak, Nasi yang diolah dari padi adalah makanan pokok orang Indonesia. Banyak orang Indonesia bekerja sebagai petani, terutama di daerah pedesaan. Tujuan petani di pertanian padi adalah untuk mendapatkan keuntungan dengan biaya rendah. Ada dua cara untuk meningkatkan laba apakah meningkatkan produksi atau mengurangi biaya produksi. Tetapi banyak petani tidak tahu bagaimana kombinasi input mempengaruhi biaya produksi. Tujuan dari penelitian ini adalah untuk menganalisis bagaimana pengaruh harga faktor produksi terhadap biaya produksi pada usahatani padi sawah di Sumbertani, Talawi, Batubara. Metode pengambilan sampel yang digunakan adalah accidental sampling. Jumlah sampel yang dikumpulkan sebanyak 79 petani. Metode analisis data yang digunakan adalah regresi linier berganda dengan perangkat lunak SPSS 15 dan Eviews 6. Analisis menunjukkan dengan perhitungan oportunitas bahwa biaya tenaga kerja, harga pupuk, harga pestisida, dan produksi secara signifikan positif pada biaya produksi beras tetapi harga benih dan sewa traktor tidak berpengaruh. Determinant Factors of Production Cost of Paddy in Sumbertani, Talawi, Batu Bara Abstract, Rice which is processed from paddy is the staple food of Indonesians. Many of Indonesians are working as farmers, especially in rural areas. The purpose of farmers in paddy farm is to gain profits with a low costs. There are two ways to increase profits whether increase production or reduce production costs. But many farmers do not know how the input combination influencing cost production. The purpose of this study was to analyze how the influence of the price of production factors to the cost of production on paddy rice farming in Sumbertani, Talawi, Batubara. The sampling method used was accidental sampling. The number of samples collected as many as 79 farmers. Data analysis method used is multiple linear regression with SPSS software tools 15 and Eviews 6. The analysis showed with the oportunity calculation that the labor cost, the price of fertilizer, pesticide prices, and production significantly positive on the production cost of rice but the price of seed and the rent of tractor has no effect.
Kecerdasan Buatan dalam Aspek Deforestasi dan Keberlanjutan Perkebunan: Pendekatan Bibliometrik Sutriani, Linda; Impron, Ali; Saragih, Veny Betsy; Anggraini, Syadza; Suraji, Suraji
LITERATUS Vol 6 No 2 (2024): Jurnal Ilmiah Internasional Sosial Budaya
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/lit.v6i2.1583

Abstract

This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.
Sertifikasi Indikasi Geografis Kopi: Pendekatan Studi Bibliometrik Saragih, Veny Betsy; Barus, Riantri; Yanti, Chicka Willy; Anggraini, Syadza
Journal of Integrated Agribusiness Vol 6 No 2 (2024): Journal of Integrated Agribusiness
Publisher : Jurusan Agribisnis, Fakultas Pertanian, Perikanan dan Kelautan Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jia.v6i2.5589

Abstract

Geographical indication certification of coffee is growing due to the demand for "specialty" coffee based on geographic origin. According to coffee lovers, the taste of coffee varies, depending on where the coffee is produced. As demand for geographic indication certified coffee increased, the research that related to this topic also developed. To find out the extent of research that has been carried out regarding the topic, this bibliometic research was conducted. The purpose of this research is to map research on geographical indication certification of coffee based on keywords and research titles so that gaps and novelties related to the topic are obtained. The type of research used is a quantitative descriptive method. The analysis used is bibliometric analysis with the Vosviewers software tool. Based on the analysis results, it was obtained from the network visualization mapping that there were 6 clusters originating from 3032 terms with 50 keywords that appeared at least 10 times. In research on geographical indication certification, the 3 most keywords that appeared were coffee, Indonesia, geographical indications. From the mapping visualization results, it is known that the latest research topics studied are signs, Arabica coffee, coffee farmers, factors, indicators, Indonesian coffee, Robusta coffee, quality, and West Java. The average publication related to these items was published in 2020-2021. From the results of the visualization mapping, the density of research topics related to coffee items in cluster 1, Indonesian items in cluster 4, and geographical indication items in cluster 2 have been widely researched, while topics in clusters 3, 5 and 6 have not yet been widely researched. These items are signs, coffee farmers, indicators, value, quality, coffee farmers, Robusta coffee, quality, and West Java.
Penerapan Machine Learning Pada Kelapa Sawit: Analisis Bibliometrik Anggraini, Syadza; Saragih, Veny Betsy; Sutriani, Linda
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4224

Abstract

The advancement of machine learning-based technology spread widely, especially in oil palm. Oil palm has become a main source of domestic products because of its high production and a leading commodity where it cannot be separated from the use of machine learning. However, the potential of machine learning has not yet been identified specifically through bibliography aspects where those aspects are needed for future research. The main objective of this research is to analyze trends of machine learning utilization and potential topics in oil palm by using bibliometric analysis to obtain year distribution, author productivity, citation, and keyword co-occurrence. As a result, the highest peak number of publications is 2023 where the most cited authors are Haohuan Fu and Weijia Li. Then, the most used algorithms are deep learning, ANN, SVM, RF and CNN based on the occurrences while the tree detection and counting topic has the highest citation articles. The result indicates that scientific interest in the study of this research benefits as a starting point for future works.
Co-Authors Ali Djamhuri Chicka Willy Yanti Impron, Ali Linda Sutriani Riantri Barus, Riantri suraji suraji Syadza Anggraini Tavi Supriana