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Notifikasi Tagihan Pembayaran Berbasis Whatsapp Gateway Untuk Pelanggan Aplikasi Katib ID Menggunakan Metode Mesin Turing Andita Permata Rahmawati; Subagja Putra Pratama; Hafifah Bella Novitasari; Eni Heni Hermaliani; Windu Gata
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 8, No 2 (2021): TEKNOVASI JUNI 2021
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v8i2.573

Abstract

KATIB.id is a business recording tools and management system application that is intended to make it easier for business people to manage and develop their business. Every month, users are required to pay a bill for the use of the application system. However, users often forget to pay if they are not reminded by the developer. There needs to be a notification containing the billing bill that is sent to regular users at the end of every month. The bill notification is currently still being delivered manually via private WhatsApp messages one by one so that this can make it possible for messages not to be spread optimally when the number of application users has been counted. The billing message should be able to spread automatically to each user without any worries caused by the limitations of the developer team. In overcoming these problems, a study was conducted on the WhatsApp Gateway which will be integrated with the automation system in the process. Where in this study the distribution of billing information can be sent through the WA Business application with a broadcast system to each user based on the telephone number recorded in the application. Through WhatsApp Business in delivery because the chat application is closer to customers and can view messages directly and at any time compared to the intensity of users in opening messages via Email. Therefore we need a method, one of which is the Turing Machine Model combined with REST API Technology.
Analisis Sentimen Terhadap Telkomsel dan XL Berbasis Machine Learning Pada Data Twitter Trisiwi Indra Cahyani; Windu Gata; Dedi Dwi Saputra; Hafifah Bella Novitasari; Hernawati Hernawati
INTECOMS: Journal of Information Technology and Computer Science Vol 6 No 1 (2023): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v6i1.5765

Abstract

Di Indonesia pengguna internet mencapai lebih dari 200 juta pengguna. Telkomsel dan XL bersaing untuk menjadi penyedia layanan internet nomor satu. Media sosial Twitter membuat pengguna lebih jujur dalam memberikan review. Umpan balik pengguna akan menjadi rekomendasi dari mulut ke mulut (WoW). Pada penelitian ini bertujuan untuk mengetahui pandangan masyarakat terhadap provider Telkomsel dan XL berdasarkan data tweet di Twitter pada bulan Juli dan Agustus 2022. Dataset dikumpulkan dari Twitter menggunakan Twitter API dengan kata kunci “XL Internet”, “Telkomsel Internet”, “MyXL”, dan “MyTelkomsel” dan diperoleh sebanyak 17.543 data. Kemudian dataset akan dilakukan case folding, tokenized, normalized, stopword removal, stemming, dan proses pembobotan TF-IDF. Model klasifikasi menggunakan Entropy Maksimum, Multinomial Naïve Bayes, dan Complement Naïve Bayes. Untuk menguji kemampuan menggeneralisasi, dilakukan 10-Fold Cross Validation untuk masing-masing model. Hasil menunjukkan bahwa metode ME lebih baik dari MNB dan CNB dengan nilai akurasi 84,11%, 81,53%, dan 79,95%.