Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing

Fitriani, Eka; Aryanti, Riska; Saepudin, Atang; Ardiansyah, Dian

doi:10.31294/p.v22i1.6898

Cited by 6 publications

(8 citation statements)

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“…Vectors are formed from all the terms or words that make up the document. The term is a collection of words consisting of several different meanings [21], [22]The vector in each element represents the weight of words that have a relationship between the document and the query. The cosine angle is used to find the value of the two vectors for each document and query weight.…”

Section: B Vector Space Models (Vsm)mentioning

confidence: 99%

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

Heryawan,

Novitaningrum,

Nastiti

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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The growth of medical record documents is increasing over time, and the various types of diseases and therapies needed are increasing. However, this has not been followed by an effective and efficient search process. This study aims to deal with search problems that often take a long time with search results that are not necessarily as expected by building a search model for medical record documents using the vector space model (VSM) and TF-IDF methods. The VSM method allows retrieval of results that are not the same as the search queries entered by the user but are expected to provide still results relevant to the user's desired needs. The model development process was taken based on the data in the FS_ANAMNESA and FS_DIAGNOSA columns, followed by preprocessing, which consists of deleting blank lines, lowercase, removing punctuation marks, HTML tags, stop words, excess spaces between words, and normalizing typo words, then forming a TF-IDF matrix based on the frequency of occurrence of each word feature, and followed by the calculation of the similarity value of the search query compared to medical record documents based on the cosine similarity formula. The retrieval results were all columns of each existing medical record document and were sorted based on 10 rows with the highest similarity value. The model evaluation results were based on 1000 medical record documents and tested with 20 search queries in this study, which gave an average precision value of 0.548 and an average recall value of 0.796.

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Section: B Vector Space Models (Vsm)mentioning

confidence: 99%

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

Heryawan,

Novitaningrum,

Nastiti

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Penelitian yang dilakukan oleh Eka Fitriani dengan judul "Implementasi Metode Naive Bayes Dalam Penyeleksian Karyawan Untuk Penempatan Bagian Pemasaran" diperoleh hasil bahwa algoritma ini tergolong baik untuk seleksi karyawan dan diperoleh akurasi sebesar 87,22% [12].…”

Section: Pendahuluanunclassified

Klasifikasi Pemenuhan Pilar Sanitasi Puskesmas Menggunakan Metode Naive Bayes

Syam,

Hayati,

Syafie

2023

Komputika

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Sanitasi merupakan usaha dalam menjaga kebersihan serta kondisi lingkungan sekitar. Dalam memenuhi pilar sanitasi di setiap wilayah pastinya kita juga membutuhkan peran instansi kesehatan untuk melakukan pemicuan dan pemberian edukasi. Pada desa area lingkup puskesmas Bontomangape diketahui bahwa pemenuhan pilar sanitasi masih belum merata. Berdasarkan hal tersebut, penulis bermaksud untuk melakukan klasifikasi pemenuhan pilar sanitasi puskesmas menggunakan metode Naive Bayes sehingga hasil klasifikasi tersebut dapat digunakan sebagai tolak ukur desa yang perlu diprioritaskan oleh petugas sanitasi. Adapun hasil klasifikasi diperoleh sebanyak 55 terlaksana dan 20 tidak terlaksana untuk desa Bontomangape, 70 terlaksana dan 5 tidak terlaksana untuk desa Campagaya, 60 terlaksana dan 15 tidak terlaksana untuk desa Kalenna, 45 terlaksana dan 30 tidak terlaksana untuk desa Parambambe, 52 terlaksana dan 23 tidak terlaksana untuk desa Parangmata, 64 terlaksana dan 11 tidak terlaksana untuk desa Parasangangberu, serta 57 terlaksana dan 18 terlaksana untuk desa Pattinoang. Adapun hasil klasifikasi memperoleh rata-rata nilai accuracy sebesar 95,81%, nilai precision sebesar 94,78% dan nilai recall sebesar 100%.

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“…Data mining atau machine learning itu sendiri dapat diartikan sebagai serangkaian proses untuk mengekstraksi pengetahuan dan pola dari sebuah dataset. Tujuan dari data mining adalah untuk mengklasifikasi, mengelompokkan, dan mencari pola asosiasi untuk membuat prediksi [4].…”

Section: Pendahuluanunclassified

Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram

et al. 2022

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Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method. Keywords: Color Histogram; Rice Leaf Disease; Random Forest.

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Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing

Cited by 6 publications

References 0 publications

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

Medical Record Document Search with TF-IDF and Vector Space Model (VSM)

Klasifikasi Pemenuhan Pilar Sanitasi Puskesmas Menggunakan Metode Naive Bayes

Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram

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