CLASSIFICATION OF CUSTOMER COMPLAINTS ON INSTAGRAM COMMENTS USING NAÏVE BAYES ALGORITHM WITH N-GRAM FEATURE EXTENSION

Customer complaints about the company can be used as a form of self-evaluation and performance that has been carried out by the company, based on customer complaints the company can find out the weaknesses that exist in the company and fix them. The forms of submitting customer complaints are very diverse, currently not only by telephone, but customers also submit suggestions or complaints, customers can submit suggestions or complaints via electronic mail or e-mail or forums in cyberspace that are indeed created by product-producing companies to accommodate various complaints, suggestions, and direct criticism from consumers, especially social media that are free to express opinions on the delivery services used. Instagram is a social media that is more inclined towards images and on the other hand, has captions and comments text, a study is needed for the problem of customer complaints from shipping service users on an Instagram account of a delivery service company. Based on this background, a solution is needed in solving problems for text mining classification using Naïve Bayes with SMOTE techniques and N-Gram feature extraction with the usual process for text mining so that it can produce Naïve Bayes and SMOTE accuracy with an accuracy of 88.54%, before implementation. N-Gram and the accuracy rate increased by 1.44% after the N-Gram Term was applied to 89.98% by using a dataset of 776 Instagram comment text records that had to preprocess text

Klasifikasi Komentar Instagram Untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang Dengan Teknik Smote

Kepuasan pelanggan adalah salah satu hal yang diharapkan suatu perusahaan ketika produk yang dihasilkan telah dipasarkan, baik berupa barang maupun jasa. Cara pengaduan melalui customer service sangat beragam, belakangan ini tak hanya melalui telepon saja pelanggan menyampaikan saran atau keluhannya. Pelanggan bisa menyampaikan saran atau keluhannya melalui surat elektronik atau e-mail maupun forum- forum di dunia maya yang memang dibuat perusahaan penghasil produk untuk menampung beragam keluhan, saran, dan kritik langsung dari konsumen terlebih sosial media yang bebas mengutarakan pendapat mereka terhadap jasa pengiriman yang mereka pakai. Instagram merupakan sebuah sosial media yang lebih cenderung kepada gambar dan disisi lain mempunyai teks caption dan komentar, dari permasalah tersebut diatas mencoba membuat sebuah penelitian untuk keluhan pelanggan pengguna jasa pengriman barang pada sebuah akun Instagram perusahaan jasa pengiriman. Dari latar belakang permasalah itulah peneliti mencoba mencoba memecahkan masalah untuk pengklasifikasi text mining dengan menggunakan metode Support Vector Machine (SVM) dan Naïve Bayes dan menggunakan teknis SMOTE dengan proses yang biasa dilakukan untuk text mining sehingga dapat menghasilkan akurasi 69.68% untuk Support Vector Machine (SVM) dan Naïve Bayes dengan akurasi 88.54%, dengan menggunakan dataset teks komentar Instagram sebanyak 776 record yang sudah dilakukan teks preprocessin

Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote

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

TANGGUNG JAWAB HUKUM RUMAH SAKIT TERHADAP PASIEN HEALTH CARE-ASSOCIATED INFECTIONS (HAIS)

Rumah Sakit memiliki peran penting dalam mewujudkan derajat kesehatan masyarakat secara optimal, untuk itu dituntut agar mampu mengelola secara professional dan bertanggung jawab, mengusung arus keutamaan tanggung jawab profesi pada aspek kesehatan, khususnya tenaga medis dan tenaga keperawatan dalam menjalankan tugas dan wewenangnya.  Health-Care Associated Infection (HAIs) penyebabnya terkait dengan proses dan sistem kesehatan. Aspek pertanggungjawaban hukum Rumah Sakit terhadap pasien yang terkena Health care-associated Infections (HAIs) menjadi fokus kajian, dengan pendekatan penelitian yuridis normatif, pada perspektif hukum sebagai kaidah tertulis yang tertuang dalam produk perundang-undangan yang berlaku. Rumah Sakit bertanggung jawab atas tindakan kelalaian tenaga kesehatan di Rumah Sakit, yang menyebabkan kerugian pada pasien, dibutuhkan adanya perlindungan hukum yang memadai sebagaimana tertuang dalam peraturan hukum secara normatif. Dalam hal perlindungan pasien, sebelum pelaksanaan pelayanan medis yang berkaitan dengan tindakan medis, tenaga kesehatan memberikan edukasi terhadap pasien terlebih dahulu, berupa penjelasan mengenai informasi, risiko yang terjadi, serta bentuk penanganannya. Apabila pasien merasa dirugikan dalam hal materiil maupun imateriil, pasien dapat mengajukan gugatan kepada Rumah Sakit yang melakukan kelalaian dan kesalahan, sebagai salah satu bentuk tanggung jawab hukum yang timbul

Implementation of the Saw Method to Discover the Optimum Internet Service Recommendations for Online Gaming

Currently, the development and use of the Internet have a more complex function so that it can change the paradigm of people's lives, including in aspects of entertainment, especially games. With the rise of numerous ISPs in Indonesia, different internet service packages are now available, particularly for gamers, such as Indihome, Biznet, First Media, and My Republic. The variety of services makes it difficult for users to choose an internet package that suits their needs. Therefore, this research aims to build a decision support system that can facilitate users in choosing the ideal internet service for gamers based on five criteria: quota, network speed, connection, cost, and the number of users using the SAW method. The data collection methods used are observation, questionnaires, and interviews. The research results obtained from data processing using the SAW method through Microsoft Excel are then implemented into a website-based program. With this program, it is hoped that it can be a tool for users in determining the service package to be purchased