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All Journal Jurnal Ekonomi Pembangunan JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Pendidikan Ekonomi (JUPE) Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Sistemasi: Jurnal Sistem Informasi Paradigma JIKO (Jurnal Informatika dan Komputer) Jurnal Komputasi Faktor Exacta Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Statistics and Its Applications Jurnal Teknoinfo International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Sains, Aplikasi, Komputasi dan Teknologi Informasi J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Jurnal Statistika dan Aplikasinya Business Economic, Communication, and Social Sciences Journal (BECOSS) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Kependudukan Indonesia
Nooraeni, Rani
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Journal : JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI

 1 
Analysis of User Sentiment of Twitter to Draft KUHP Nawang Indah Cahyaningrum; Danty Welmin Yoshida Fatima; Wisnu Adi Kusuma; Sekar Ayu Ramadhani; Muhammad Rizqi Destanto; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 3 (2020): JMSK, MAY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (567.565 KB) | DOI: 10.20956/jmsk.v16i3.8239

Abstract

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.
Penerapan Metode Random Forest dalam Pengklasifikasian Penerima Kartu BPJS Kesehatan Penerima Bantuan Iuran (PBI) di Kabupaten Karangasem, Provinsi Bali 2017 Qonita Raihananda; I Wayan Edy Darma Putra; Monica Seftaviani Sijabat; Sifa Rofatunnisa; Ibnu Maruf; Hermarwan Hermarwan; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11710

Abstract

BPJS Kesehatan is a social security facility provided by the government to all people who are registered as members. BPJS Kesehatan membership is divided into two, namely BPJS for Contribution Assistance Recipients (BPJS PBI) and BPJS Non-Contribution Assistance Recipients (BPJS Non-PBI). In 2019, Bali Province is targeted to achieve Universal Health Coverage of 95 percent so that the Bali Provincial Government has budgeted funds worth IDR 945 billion to finance JKN - KBS services which are integrated with JKN - KIS. Karangasem is one of the four districts in Bali Province that received the most percentage of financing, which is 51 percent of the total budget needed when compared to other areas. This study aims to classify the BPJS-PBI recipient community based on education variables, employment indicators, age, and per capita expenditure in Karangasem Regency in 2017. The classification method used in this study is the random forest method. The results showed that the per capita expenditure variable had the largest contribution in classifying the status of PBI participants. The model that is formed produces an accuracy of 0.8017. This means that the model can predict 80.17 percent testing data correctly.
Co-Authors Adinda Hermambang Aditya Firman Baktiar Adrian Firmansyah Afifatul Ilma Widyatami Aisyah Nur Fahira Aldi Rochman Nulkarim Amalia Susanti Amelia Syahadati Amirah Balqis Safiruddin Amran Pratama Putra Ana Pangestika Andriansyah Muqiit WS Anindia Wahyu Inayah Annisa Putri Ramadhanty Aprilia Lutviana Dewi Apriliansyah Mahmud Aprillia Anis Saputri Arul Fathurrahman Arya Damar Prakasa Arya Wahyu Nugroho Astrid C. A. Pangaribuan Astry Julyana Eliawati Aulia Adita Rahma Aulia Fatin Afifah Aulia Fikri Fadhilah Iskandar Ayufi Reyza Zakaria Cesaria Dewi Choirul Ummah Danty Welmin Yoshida Fatima Delvira Cindy Rosmilda Dewi Retno Oscarini Diana Agustin Dinda Desinta Diva Arum Mustika Dwi Cahyo Firmansyah Elina Mayasari Elvera Wahyu Triana Emalia Septiani Hirma Emban Permata Siam Ersa Budi Sutanto Eunike Sola Gratia Evita Dyah Wardhani Fadhilatul Khairi Fajar Hari Dwiono Fathin Nadillah Fathul Sanusi Febi Taufiqurrahman Fiqih Tri Mahendra Firza Refo Adi Pratama Frengky Sele Galang Madya Putra Galuh Sri Natungga Dewi Susilo Putri Garinca Firgiana Santoso Geraldi Putra Prasetya Balebu Ghita Nurfalah Ghytsa Alif Jabir Gona Asri Wijayanti Helen Fricylya Br Tobing Heny Dwi Sariyanti Hermarwan Hermarwan Herpanindra Fadhilah I Wayan Edy Darma Putra Ian Tryaldi Halim Ibnu Maruf Iffati Uzma Indonesian Journal of Statistics and Its Applications IJSA Ineke Kristin Dwi Astuti Intan Maulida Khoirun Nisa' Isdhani Nurrahmah Ivana Yoselin Purba Siboro Krisna Dwi Agung Kuncoro Dwi Dhanutama Lady Deborah Latifah Hasanah Lisa Widyarsi M. Alfa Rizki Machsus Machsus Margareth Dwiyanti Simatupang Marita Mutiara Sinsyi Megananda Ghowo Rizky Meilani Thereza Saragih Mikha Aprilio Miko Oktavio Wijaya Monica Seftaviani Sijabat Muhamad Bagus Adji Briliyanto Muhamad Zidan Nuralifian Muhammad Rizqi Destanto Mula Warman Mustika Putri Muti Nurjannah Fitri Nada Nabila Rosyad Nadhifan Humam Fitrial Najia Helmiah Nawang Indah Cahyaningrum Ni Luh Putu Yayang Septia Ningsih Ni Putu Gita Naraswati Noval Irgi Zekha F Novert Cyril Lengkong Nufaisa Munia Nurfitri Aulia Nurhidayatul Fitri Nurul Hanifah Septiani Ouditiana Safitri Peterson Hamonangan Immanuel Sihotang Pramudya Kusuma Putri Tareka Navasha Qonita Raihananda Raihan Fitrika Azzahra Rhevita Lula Eksanti Ria Dotul Ilmia Ridson Alfarizal Pulungan Riska Damaiyanti Rizka Wulandari Roy Pratama Wijaya Salsa Vira Satria Dirgantara Satria Kurnia Areka Sekar Ayu Ramadhani Sifa Rofatunnisa Siti Andhasah Siti Andhasah Siti Fatimatul Munawwaroh Sri Rahayu Yogyana Sinurat Suciarti Pertiwi Syifa Rahmawati Hakim Takdir Viana Mei Reistiani Vina Astriani Wilda Maria Ulfa Windri Wucika Bemi Wisnu Adi Kusuma Yakobus Natanael Tarigan Yolanda Rizkie A Yolanda Rizkie Aprilia Yongki Ramanda Putra Yulianus Ronaldias Yuniar Putri Awaliyah Risky Yusuf Yahya Zahrotul Firdaus