Klasifikasi Kelayakan Telur Ayam Ras (Broiler) Menggunakan Metode Naïve Bayes Classifier

Maulana, Andre; Hariyanto, Rudi; Widodo, Anang Aris

doi:10.21067/jtst.v2i3.4915

Cited by 3 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Indikasi ini bermakna kelas bisa diperoleh sesudah karakteristik diindentifikasi. Persamaan ini disebut sebagai probabilitas akhir (posterior probability) untuk , sedangkan disebut probabilitas awal (prior probability) (Meilani et al, 2019). Metode Naïve Bayes ini telah banyak digunakan dalam mengambil sebuah keputusan diantaranya diterapkan dalam penelitian 1) Klasifikasi Austism Spectrum Discorder (Dewi, 2021).…”

Section: Pendahuluanunclassified

Penerapan Metode Naïve Bayes Dalam Klasifikasi Kabupaten/Kota Di Sumatera Utara Berdasarkan Produktivitas Pangan Padi

Harahap,

Aprilia,

Lubis

2023

SCI TECH ED MATH

View full text Add to dashboard Cite

Rice production in North Sumatra Province from January to December 2021 reached approximately 2 million tons of GKG, or a decrease of around 36.36 thousand tons of GKG (1.78 percent) compared to 2020. The decline in rice production also had an impact on the level of rice productivity and also had an impact on rice production. In the economic sector, it is necessary to classify rice productivity in order to support the increase in future rice production in North Sumatra Province. The purpose of this study was to determine the classification of district/city rice productivity using the Naïve Bayes method. This study uses 20 districts/cities as training data and 12 districts/cities as testing data. From the test results, 6 regencies/cities are classified with high level and 26 regencies/cities are classified with low level. The accuracy results obtained are 75% where this accuracy is a fairly good result in classification

show abstract

Section: Pendahuluanunclassified

Penerapan Metode Naïve Bayes Dalam Klasifikasi Kabupaten/Kota Di Sumatera Utara Berdasarkan Produktivitas Pangan Padi

Harahap,

Aprilia,

Lubis

2023

SCI TECH ED MATH

View full text Add to dashboard Cite

show abstract

“…Performa diukur berdasarkan ketepatan dalam mengenali input yang diberikan dan menghasilkan output yang benar. Persamaan 7 digunakan untuk menghitung akurasi [29].…”

Section: Evaluasiunclassified

Klasifikasi Citra Songket Aceh Menggunakan Metode Probabilistic Neural Network

Amalia,

Mawardi,

Indrawati

et al. 2023

JSE

View full text Add to dashboard Cite

Tujuan penelitian ini untuk mengklasifikasikan citra Songket Aceh. Data penelitian menggunakan sepuluh motif Songket Aceh dan data diperoleh dari tempat usaha tenun Songket Nyak Mu. Tahapan penelitian ini adalah akuisisi citra, pra-proses, ekstraksi fitur, klasifikasi dan evaluasi. Ekstraksi fitur tekstur citra Songket Aceh menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Fitur-fitur yang digunakan pada penelitian ini adalah entropy, energy, sum of squares: variance, difference entropy dan autocorrelation. Metode Probabilistic Neural Network (PNN) diaplikasikan untuk klasifikasi citra Songket Aceh. Metode Leave-One-Out Cross Validation (LOOCV) digunakan untuk pembagian data latih dan data uji. Hasil klasifikasi citra Songket Aceh dengan metode PNN adalah sebesar 93%.

show abstract

“…To achieve profitability, farmers are advised to carefully classify and choose materials such as seeds, feed, drugs or vitamins, cage placement, and other factors. This approach is expected to result in healthy broiler chickens with high-quality meat, positively influencing sales and benefiting farmers (Maulana, et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Broiler Chicken Production Using the Naive Bayes Classification Algorithm

Hasdyna

2024

techno

View full text Add to dashboard Cite

Serious challenges are faced by broiler chicken farmers in Seumirah Village, Nisam Antara Subdistrict, North Aceh Regency, in their efforts to create high-quality and productive chickens. These difficulties not only impact the farmers' income but also result in recurring losses every year. This research aims to design a system using the Naive Bayes Classifier algorithm to assess the capacity and classify production types based on specific criteria such as population, age, depletion, FCR (Feed Conversion Ratio), IP (Index Performance), and BW (Body Weight). The system aims to classify broiler chicken production as either increasing (profitable) or decreasing (unprofitable). In the development of this predictive system, the PHP programming language is employed, with a MySQL database as the data storage medium. The results of this broiler chicken production prediction system have proven effective in providing information in the form of profit or loss reports based on the harvest results for each monthly period. The implementation of this system is expected to assist in optimizing farmers' production management, increasing business profitability, and providing better guidance for future business decisions. The classification results using the Naive Bayes method indicate an accuracy rate of 86,67 and error rate of 13,3%.

show abstract

Klasifikasi Kelayakan Telur Ayam Ras (Broiler) Menggunakan Metode Naïve Bayes Classifier

Cited by 3 publications

References 2 publications

Penerapan Metode Naïve Bayes Dalam Klasifikasi Kabupaten/Kota Di Sumatera Utara Berdasarkan Produktivitas Pangan Padi

Penerapan Metode Naïve Bayes Dalam Klasifikasi Kabupaten/Kota Di Sumatera Utara Berdasarkan Produktivitas Pangan Padi

Klasifikasi Citra Songket Aceh Menggunakan Metode Probabilistic Neural Network

Predictive Modeling of Broiler Chicken Production Using the Naive Bayes Classification Algorithm

Contact Info

Product

Resources

About