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Journal : Faktor Exacta

Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote nanang ruhyana; didi rosiyadi
Faktor Exacta Vol 12, No 4 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i4.4981

Abstract

Customer satisfaction is one of the things expected by a company when the product produced has been marketed, both in the form of goods and services. How to complain through customer service is very diverse, lately not only by telephone, customers submit their suggestions or complaints. Customers can submit their suggestions or complaints via e-mail or e-mail or forums in the virtual world that are made by product-producing companies to accommodate a variety of complaints, suggestions, and direct criticism from consumers, especially social media, who are free to express their opinions on shipping services. they use. Instagram is a social media that is more inclined to images and on the other hand has text captions and comments, from the above problems trying to make a research for customer complaints of users of goods delivery services on an Instagram account shipping service company. From the background of the problem, the researchers tried to solve the problem for text mining classifiers by using the Support Vector Machine (SVM) and Naïve Bayes methods and using the SMOTE technique with the usual processes for text mining so that they could produce 69.68% accuracy for Support Vector Machine (SVM) and Naïve Bayes with an accuracy of 88.54%, using the Instagram comment text dataset of 776 records that have been done with preprocessing text.
Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp Irwansyah Saputra; Didi Rosiyadi
Faktor Exacta Vol 12, No 2 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i2.4181

Abstract

WhatsApp is the most popular messaging application in Indonesia. This causes the emergence of cyberbullying behavior by its users. This study aims to classify WhatsApp chat to two classes, namely bully and not bully. The classification algorithms used are k-NN, NBC and SVM. The results show that the SVM algorithm is better at solving this case with an accuracy of 81.58%.
Co-Authors -, Cucu Abdul Mahatir Najar Abdullah, Ifnu Abdul Aziz Ade Cahyana Adiwijaya Adnyana, I Ketut Widhi Agus Indra Jaya Agus Salim Akbari Basuki Akbari Indra Basuki Alvi Alvi Aridarma, Dedi Bambang Joko Triwibowo Basuki, Akbari Indra Bobby Suryo Prakoso Bobby Suryo Prakoso Cahya, El Rangga Un Chandra Nugraha Chandra Nugraha, Chandra Cucu Cucu Daniati Uki Eka Saputri Dedi Ariadarma Dedi Aridarma Deni Rusdiaman Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Eko Saputro Endang Sri P Fajar Pramono Fajar Priyono Fariszal Nova Arviantino Fauzi, Fariz Furqon Hensan Muttaqien Hadi Susanto Harianto Harianto Heru Sukma Utama Heru Sukma Utama Heru Susanto Horng, Shi Jinn Husain, Syepry Maulana Ifnu Abdul Aziz abdullah Imam Amirulloh Ipin Sugiyarto Iqbal Dzulfiqar I Irawan, Rama Irwansyah Saputra Iwan Setiawan Iwan Setiawan Jamil, Muh. Juninisvianty, Tri Kurniawan W. Handito Kurniawan, Rohim Kusnadi - Kusnadi Muh. Jamil Muhammad Syahrani Mulyanto, Yudi Nana Suryana Nana Suryana Nana Suryana nanang ruhyana Nila Hardi Nuryani Nuryani Prakoso, Bobby Suryo Prasetyo, Heri Putra, Yeffry Handoko Qhomar, Mohammad Arifin Nurul Qurrota Ayun Hariyanto Putra RACHMAT HIDAYAT Rama Irawan Riski Annisa Salim, Taufik Ibnu Salsabila, Unik Hanifah Saputra, Surya Fajar Seimahuira, Syarah Sidiq, Muhammad Fajar Sriyadi Sriyadi Sugiyarto, Ipin Surti Kanti Surya Fajar Saputra Sutiyono Sutiyono Suwanda Aditya Aaputra Taufik Iqbal Ramdhani Tiska Pattiasina Utama, Heru Sukma Windu Gata Yeffry Handoko Putra Yusnan Hasani Siregar Yusuf Arif Setiawan Zulfikar Fauzi