Publication Search

This study aims to analyze production levels and identify leading vegetable commodities in Rejang Lebong Regency during the period of 2023–2024. The data used is secondary data sourced from the Rejang Lebong Regency Agriculture Office and empirical calculations based on Location Quotient (LQ) analysis. The results show that the total vegetable production in Rejang Lebong Regency in 2023 reached 3,451,596 quintals and decreased to 3,198,925 quintals in 2024. The commodities with the highest average production were cabbage (647,021 quintals), eggplant (646,994.5 quintals), and carrots (339,041.5 quintals), while the lowest production was found in kale and long beans. LQ analysis shows that there are eight leading commodities (LQ > 1), namely spring onions, green beans, cauliflower, potatoes, cabbage, mustard greens, carrots, and large chilies, most of which are highly competitive highland crops. The results of this study confirm that Rejang Lebong Regency has strong potential as a horticultural center in Bengkulu Province. Therefore, a commodity-based agricultural development strategy is needed that is oriented towards increasing productivity, added value, and the region's competitiveness in a sustainable manner.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

Fruits and vegetables are perishable food items that require special handling during both the receiving and storage processes. The purpose of this study is to investigate the implementation of fruit and vegetable receiving and storage processes in the kitchen of Hotel Mercure Surabaya Grand Mirama. This research employs two approaches: quantitative descriptive and qualitative. Data collection techniques include questionnaires, interviews, and documentation studies. The data analysis technique used is descriptive quantitative analysis, where questionnaire results are calculated using a percentage formula (%) and described using percentage interpretation criteria. Based on the findings, the implementation of fruit and vegetable receiving processes at Hotel Mercure Surabaya Grand Mirama shows that indicators for product freshness, material inspection, rejection of non-conforming materials, sorting, and washing are categorized as good. However, the indicator for material cleanliness falls under the fair category. Meanwhile, the implementation of storage processes, including storage temperature, storage facilities, storage inspection, and the application of the FIFO (First In First Out) storage method, demonstrates a very good criterion. Nevertheless, material separation received a good criterion, indicating room for further improvement to optimize storage management and ensure compliance with established standards.

The purpose of this study was to determine the effect of the application of goat manure and liquid organic fertilizer derived from vegetable waste on the growth and productivity of celery plants (Apium graveolens L.). This study used a factorial Randomized Block Design (RBD) consisting of two factors with 48 plots.The first factor was the application of goat manure fertilizer, symbolized as “G”, which consisted of four treatment levels: G0 = 0 g/polybag, G1 = 200 g/polybag, G2 = 400 g/polybag, and G3 = 600 g/polybag.The second factor was the application of liquid organic fertilizer from vegetable waste, symbolized as “S”, which consisted of four treatment levels: S0 = 0 ml, S1 = 100 ml/L of water, S2= 200 ml/L of water, and S3 = 300 ml/L of water.The parameters observed were plant height, number of leaves, leaf width, number of tillers, plant weight per polybag, and plant weight per plot. The results of the study showed that the application of goat manure and liquid organic fertilizer from vegetable waste had a significant effect on the parameters of plant height, number of leaves, leaf width, number of tillers, plant weight per polybag, and plant weight/plot.