Naïve Bayes and Laplacian Correction Method in Agrarian Application of Vegetable Crops

Abstract

Indonesia is one of the agricultural countries with the livelihood of the population coming from agro-industrial agriculture. Onfarm agricultural activities, commodities that are widely cultivated are vegetable and fruit crops. The problem that occurs at this time is that physically the vegetables grown experience failure and a decrease in the quality of growth. Socially, many people complain about the quality of vegetables in the market, and this has an impact on the economic decline of farmers. For this reason, this research aims to assist farmers in classifying their land information data. The data classification is a web-based application that will process data in the form of soil type, soil texture, soil pH, temperature, rainfall, soil fertility, soil moisture, altitude, wind speed and sunlight. The data processing uses the Naïve Bayes and Laplacian Correction methods. The trial was conducted in Padang Panjang City, West Sumatra, Indonesia. The results of this research are in the form of a web-based Vegetable Crop Agrarian application. test data used as much as 27 data on the types of vegetables that have the potential to be planted in Padang Panjang City and produce the most recommended crops, namely Radish vegetables. This interprets that the application can be built well and presented as an alternative in helping farmers before planting crops in Indonesia.