Penerapan Data Mining dalam Menganalisis Kepribadian Pengguna Media Sosial dengan Naive Bayes Classifier: Studi Kasus Media Sosial Instagram

Harits, Muhammad; Sudrajat, R.; Rosadi, Rudi

doi:10.24198/jin.v1i1.8552

Cited by 3 publications

(2 citation statements)

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“…Penelitian mengenai analisis media sosial pernah dilakukan Muhammad menggunakan teori five factor model, hasil penelitian menyimpulkan bahwa kepribadian Openness to New Experiences sebanyak 11.68%, kepribadian Conscientiousness 5.34%, Extraversion 5.5%, Agreeableness 8.54%, Neuroticsm 4.82%, serta 64.12% tidak dapat diklasifikasikan [5].…”

Section: Pendahuluanunclassified

Analisis Data Status Di Facebook Untuk Merekomendasikan Produk Makanan Ringan Menggunakan Tf-Idf Dan Document Weighting

Abidin¹,

Sediyono

Prasetyo

2018

Simet

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Penelitian ini bertujuan untuk mengembangkan menganalisa tingkat analisis status pada akun media sosial untuk mendukung kegiatan promosi Usaha Mikro Kecil khususnya produk makanan ringan. Penelitian menggunakan teknik yang terdapat pada text mining, analisis Term Frequency- Invers Document Frequency dan Document Weighting untuk merekomendasikan produk. Objek penelitian akun media sosial facebook dengan sample random yang tergabung dalam grup Warga Demak serta produk hasil UMK yang terdaftar pada Dinas Perdagangan dan Koperasi Kabupaten Demak. Hasil akhir penelitian ini adalah prototype sistem analisis status media sosial untuk mengklasifikasikan pelanggan produk UMK yang sesuai dengan hasil analisa status facebook. Dari hasil analisa didapatkan rata-rata tingkat akurasi prediksi sebesar 73.27% untuk keberhasilan merekomendasikan produk. Hasil penelitian ini menunjukkan bahwa analisis TF-IDF dan Document Weighting dapat dimanfaatkan untuk pengembangan sistem baru guna mendukung kegiatan promosi pengelola kelompok UMK agar lebih tepat sasaran.

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Section: Pendahuluanunclassified

Analisis Data Status Di Facebook Untuk Merekomendasikan Produk Makanan Ringan Menggunakan Tf-Idf Dan Document Weighting

Abidin¹,

Sediyono

Prasetyo

2018

Simet

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“…In this analysis each group is homogeneous between members in the group or object variations in the smallest possible group. Algorithms commonly used in data mining namely algorithm ID3 [1]- [3], C4.5 [1], [4]- [8], K-Means [9], [10][11]- [13], Naive Bayes [14], [15], Support Vector Machine [2], Priori [16] And a number of algorithm data mining other. Algorithm K-Means can used for study various type matter, among them study related [17] who do study about grouping in classifying the number of villages/kelurahan that have health facilities by province with k-method.…”

Section: Introductionmentioning

confidence: 99%

Data Mining K-Means: Clustering Health and Complaints Resident in Indonesia

Sri Wulandari,

Husna Sarirah Husin,

Wahyu Ratri Sukmaningsih

2021

IJHS

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This study aims to utilize the Clustering Algorithm in grouping the population Which have complaint health with algorithm K-means in Indonesia. Source data study This collected based on the information documents. The total population of the province have complaints health produced by the Central Bureau of National Statistics. The data used in this study are data for 2013-2017 which consists of 34 provinces. The method used in this research is K-means algorithm. The data will be processed by clustering in 3 clusters, namely level clusters high health complaints, clusters of moderate and low health complaints. Data center for clusters high population level 37.48, Centroid data for clusters of moderate population level 27.08, and Centroid data for low population level cluster 14.89. So that the acquisition of the assessment is based on the population index owned health complaints with 7 provinces with high levels of health complaints, namely Central Java, in Yogyakarta, Bali, Nusa Southeast West, Nusa Southeast East, Borneo South, Gorontalo, 18 province level complaint moderate health, and 9 other provinces including low levels of health complaints. It can be input to the government to pay more attention to residents in each area that has high health complaints through improving public health services so that the Indonesian population becomes healthier without exists complaint health.

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Analysis of Student Academic Performance and Social Media Activities by Using Data Mining Approach

Pratama

Ripanti

2020

Proceedings of the 2020 the 6th International Conference on E-Business and Applications

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Penerapan Data Mining dalam Menganalisis Kepribadian Pengguna Media Sosial dengan Naive Bayes Classifier: Studi Kasus Media Sosial Instagram

Abstract: Big Data, Data Mining, Five Factor Model, Instagram, Naïve Bayes Classifier

Cited by 3 publications

References 0 publications

Analisis Data Status Di Facebook Untuk Merekomendasikan Produk Makanan Ringan Menggunakan Tf-Idf Dan Document Weighting

Analisis Data Status Di Facebook Untuk Merekomendasikan Produk Makanan Ringan Menggunakan Tf-Idf Dan Document Weighting

Data Mining K-Means: Clustering Health and Complaints Resident in Indonesia

Analysis of Student Academic Performance and Social Media Activities by Using Data Mining Approach

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