Application of the Naive Bayes Method to a Decision Support System to Provide Discounts (Case Study: PT. Bina Usaha Teknik)

Burdi, Fauzan; Setianingrum, Anif Hanifa; Hakiem, Nashrul

doi:10.1109/ict4m.2016.064

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…Bina Usaha Teknik)" Classification was conducted by Naïve Bayes method as decision support system in granting discount to company product with some criterion specified by company, purchasing item, product status, big day and price range. The result of this study found that the application of decision support system to provide discounts using the Naïve Bayes method can provide an alternative to manage data quickly in providing provisional price recommendatio (Burdi, Setianingrum, & Hakiem, 2017).…”

Section: Literature Reviewmentioning

confidence: 96%

Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Fath

Arini

Hakiem

2020

SinkrOn

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Sentiment analysis is an important and emerging research topic today. Sentiment analysis is done to see opinion or tendency of opinion to a problem or object by someone, whether it tends to have a negative or positive view. The main purpose of this study is to find out public sentiment on Full Day school's policy comment from Facebook Page of Kemendikbud RI and to find out the performance of the Naïve Bayes Classifier Algorithm. In this study, the authors used the Naïve Bayes Classifier algorithm with trigram and quad ram character feature selection with two different training data models and labeling of training data using Lexicon Based method in the classification of public sentiment toward the Full day school policy. The result of this research shows that public negative sentiment toward Full Day School policy is more than positive or neutral sentiment. The highest accuracy value is the Naïve Bayes Classifier algorithm with trigram feature selection of 300 data training models with a value of 80%. The greater of training data and feature selection used on the Naïve Bayes Classifier Algorithm affected the accurate result.

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Section: Literature Reviewmentioning

confidence: 96%

Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Fath

Arini

Hakiem

2020

SinkrOn

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“…Algoritma naïve bayes juga digunakan dalam melakukan klasifikasi dokumen berita [12]. Algoritma Naive Bayes banyak diimplementasikan untuk sistem pengambilan keputusan, diantaranya: sistem pengambilan keputusan untuk menganalisis gangguan perkembangan anak dan memberikan rekomendasi dalam mengambil keputusan untuk mendeteksi gangguan perkembangan pada anak [13], pemilihan mata kuliah pilihan [14], pemberian bantuan sosial [15], sistem seleksi penerimaan dan penempatan sumber daya manusia pada suatu perusahaan [16], dan sistem pemberian diskon dalam sistem multi-kriteria [17].…”

Section: Pendahuluanunclassified

Implementasi Sistem Klasifikasi Tim Kerja Menggunakan Algoritma Naïve Bayes (Studi Kasus: PT.MNL)

Angelya,

Rahman,

Pradesan

2023

TEKNOSI

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PT.MNL adalah perusahaan swasta yang bergerak dibidang jasa kehutanan khususnya Hutan Tanaman Industri. Perusahaan memiliki banyak areal kerja yang memiliki banyak tim kerja di semua arealnya. Mengingat besarnya tim kerja, pemilik perusahaan harus bisa menjaga kinerja tim kerjanya agar selalu berada dalam posisi baik. Dengan semakin banyaknya tim kerja maka data yang masuk ke dalam perusahaan semakin banyak pula, data-data tersebut tentunya perlu dimanfaatkan dengan cara mengolah dan menganalisis untuk menghasilkan suatu informasi yang berguna bagi perusahaan. Sehingga dapat digunakan dalam mengklasifikasikan seluruh anggota-anggota tim pada lokasi kerja tertentu. Maka dari itu, diperlukan sebuah teknologi Data Mining yang dapat membantu perusahaan dalam memilah nama tim yang sebaiknya dipilih. Metode yang dipakai untuk klasifikasi tim kerja adalah algoritma Naïve Bayes. Penelitian ini menghasilkan tingkat akurasi klasifikasi sebesar 93,66%, hasil recall 90,74% serta hasil precision sebesar 88,58% sehingga hal ini menunjukkan bahwa sistem klasifikasi tim menggunakan algoritma Naive Bayes dapat digunakan oleh pemilik perusahaan sebagai bahan pertimbangan dalam mengatur persebaran tim di setiap lokasi kerja.

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“…Secondly, the amount of training data NB needs for classification is not necessarily large. In addition, although the NB classifier is naively designed and its assumption seems to be too simple, it performs well in a number of complicated real-world situations [28].…”

Section: Naı ¨Ve Bayesmentioning

confidence: 99%

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Chang

Bailey

et al. 2022

Neural Comput & Applic

117

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This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naı ¨ve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naı ¨ve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.

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Application of the Naive Bayes Method to a Decision Support System to Provide Discounts (Case Study: PT. Bina Usaha Teknik)

Cited by 10 publications

References 2 publications

Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Implementasi Sistem Klasifikasi Tim Kerja Menggunakan Algoritma Naïve Bayes (Studi Kasus: PT.MNL)

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

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