Prediksi Harga Komoditi Jagung Menggunakan K-Nn  Dan Particle Swarm Optimazation  Sebagai Fitur Seleksi

Lasulika, Mohamad Efendi

doi:10.33096/ilkom.v9i3.148.233-238

Cited by 16 publications

(21 citation statements)

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“…Selanjutnya, prediksi harga komoditi jagung menggunakan algoritma K-Nearest Neighbor (KNN) berbasis Particle PSO. Hasil eksperimen penelitian ini menunjukkan bahwa algoritma ini menghasilkan nilai RMSE 0.06 yang berarti akurasi prediksi cukup baik [8].…”

Section: Pendahuluanunclassified

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Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

et al. 2020

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Section: Pendahuluanunclassified

“…. , ) menjumlahkan delta masukannya (dari unit-unit yang berada pada lapisan atasannya) dengan persamaan (8).…”

Section: A Backpropagation Neural Networkunclassified

Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

et al. 2020

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“…Particle Swarm Optimization [3] adalah teknik optimasi dengan cara menghitung secara terus menerus calon solusi dengan menggunakan suatu acuan kualitas. Adapun algoritma K-NN [4] merupakan metode untuk melakukan klasifikasi terhadap objek [5] berdasarkan data yang jaraknya paling dekat dengan objek tersebut [6], cara untuk mengukur jarak kedekatan antara data baru dengan data lama(data training) [7], diantaranya Euclidian distance [8]dengan menggunakan sampel-sampel dari data testing [9].…”

Section: Pendahuluanunclassified

Klasifikasi Topik Tugas Akhir Mahasiswa menggunakan Algoritma Particle Swarm Optimization dan K-Nearest Neighbor

Sumarni

Rustam

2020

Ilk. J. Ilm.

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Problems the Topic of the final project is a form of scientific writing that contains the results of observations from a study of the problems that occur with the use of methods related to the particular field of science. Every student in every program of study must draw up a final project. However, before embarking on writing the final project, each student must have the topic area as a destination, the step of selection the topic of final project is an initial step before working on the final task. One way to get the final task is to see the value of general courses as well as courses, concentration majors, the value of which dominate the is is decent to scope the research topic. this research is conducted on the application of the method of K-Nearest Neighbor (KNN) for categorization of the value of the courses of concentration for the coverage of the research topic, topic the entire value in the dataset will be classified by KNN and in the optimization with the Particle swarm Optimization algorithm (PSO). The experimental categorization of the final project is built with the training data Mahasiswa Universitas Ichsan Gorontalo that has been classified previously and test data derived from the entire value of the courses is not yet known categories. The results of the experiments, the value of the resulting accuracy of algorithms KNN, namely the value of the best accuracy with K=3, K Folds = 10 has an accuracy that is 72.46% and the Algorithm of KNN-PSO best accuracy with K=3, K Folds = 10 has an accuracy that is 89.86%, shows the accuracy is better by using the optimization algorithm

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“…The purpose of prediction is to estimate the food commodity cost that occurs in the future. In addition, predictions also provide an overview of the cost situation for a certain period [2]. The need for forecasting to facilitate the stakeholders involved in decision maker related to cost stability and availability stock.…”

Section: Introductionmentioning

confidence: 99%

Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

Lutfi¹,

Muttaqien²,

Apriliani³

et al. 2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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Inconstancy of the market prices can affect society's purchasing power. One effort to anticipate the price uncertainty is by conducting commodity price forecasting. In the concept of forecasting, the commodity prices can be predicted by studying sales data in the previous period. This study aims to implement a decision support system in predicting food commodity prices trend. In data collection, the authors used list of food commodities provided by Industry and Trade Service of Gowa Regency. For data analysis, we use Naive Bayes algorithm to predict the food commodity prices in the future and Simple Exponential Smoothing to find out the price trend in a certain period. As a result, both methods can predict commodity prices and market tendency in a given time completely.

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Prediksi Harga Komoditi Jagung Menggunakan K-Nn Dan Particle Swarm Optimazation Sebagai Fitur Seleksi

Cited by 16 publications

References 0 publications

Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Klasifikasi Topik Tugas Akhir Mahasiswa menggunakan Algoritma Particle Swarm Optimization dan K-Nearest Neighbor

Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

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