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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi INOVTEK Polbeng - Seri Informatika Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Data Science Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Journal of Engineering and Information Technology for Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Computer Science Jurnal Informatika: Jurnal Pengembangan IT Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science International Journal of Informatics and Data Science
Isnain, Auliya Rahman
Fakultas Teknik Dan Ilmu Komputer, Universitas Teknokrat Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PENERAPAN NAÏVE BAYES CLASSIFIER UNTUK PENDUKUNG KEPUTUSAN PENERIMA BEASISWA Alita, Debby; Sari, Indah; Isnain, Auliya Rahman; Styawati, Styawati
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1028

Abstract

Scholarships are the provision of assistance in the form of financial assistance provided to individuals with the aim of being used for the sustainability of the education achieved. The problem that occurs in this research is that the process of determining which is still carried out conventionally the student section must check one by one the scholarship application files submitted by students because each data will be compared one by one according to predetermined criteria, which results in the student section becoming difficult in the decision so that It takes a long time, therefore we need a decision support system that can help schools make decisions about scholarship recipients.The Naive Bayes Classifier method is a method that can be used in decision making to get better results on a classification problem. The purpose of this study is to build a scholarship recipient decision support system using the Naïve Bayes Classifier method. In this study, a problem analysis was carried out using PIECES analysis and for the system development method using.The result of this research is that applying the naïve Bayes method to the scholarship recipient's decision support system can assist the school in determining the scholarship recipient more quickly and accurately. The scholarship recipient's decision support system was built using the Java programming language and MySQL database. Keyword: Decision Support Systems, Naïve Bayes Classifier, Waterfall, Blackbox Testing, PIECES
SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM Isnain, Auliya Rahman; Sakti, Adam Indra; Alita, Debby; Marga, Nurman Satya
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1021

Abstract

Media sosial menjadikan masyarakat mengalami pergeseran perilaku baik budaya, etika dan norma yang ada, sehingga mereka dapat mengeluarkan opini-opini yang mereka miliki. Opini merupakan suatu pendapat dari pemikiran masayarakat mengenai suatu permasalahan yang sedang terjadi, saat ini Indonesia sedang dihadapkan oleh masalah mengenai virus Covid-19 yang memakan begitu banyak korban jiwa sehingga masyarakat mengeluarkan opini mereka mengenai virus tersebut dan kebijakan yang dilakukan pemerintah menghadapi virus tersebut.Penelitian ini bertujuan untuk mengetahui bagaimana sentiment publik terhadap kebijakan yang akan dilakukan pemerintah mengenai kebijakan lockdown ataupun pembatasan sosial berskala besar menggunakan metode Support Vector Machine denga ekstraksi fitur tf-idf  dengan pengujian yang nantinya akan dilihat bagaimana nilai accuracy, precision, Recall dan F1-Score.Penggunaan metode Support Vector Machine dan ekstraksi fitur dengan tf-idf yang membagi kelas menjadi sentiment positif 68,75% dan negative 31,25% menghasilkan nilai accuracy sebesar 74%, precision sebesar 75%, recall sebesar 92% dan F1-Score sebesar 83%.
Co-Authors Ade Dwi Putra Ade Surahman Ady Chandra Agung Saputra Agus Sihabuddin Ahmad Ari Aldino Aidil Akbar Alita, Debby Andi Nurkholis Andrian, Eka Aprian Nuriansah Aprilia, Ni Wayan Ayu Sekar Ari Wibowo Bagus Miftaq Hurohman Bakti Eka Putra Deby Sintia Amelia Dedi Darwis Dinda Ayu Prasticha Donaya Pasha Elisa, Putri Elvano Delisa Mega Fatriade Saputra Fenty ariany Fikri Hamidy Heni Sulistiani Ikbal Yasin Ikbal Yasin Indah Sari Jepi Supriyanto Jepi Supriyanto Lestari, Rohmah Dewi Lili Andraini Linda Yunita M Agung Saputra Marga, Nurman Satya Mesran, Mesran Muhammad Indigo Muhammad Pajar Kharisma Muhammad Pajar Kharisma Putra Nadia Apriliza Neneng Neneng Nirwana Hendrastuty Nurman Satya Marga Nuroji Nuroji Oktavia, Ingrid Permata Permata Priandika, Adhie Thyo Priandika, Arjuna Priskilia Lovika Prita Dellia Qadhli Jafar Adrian Rido Febriansyah Rika Mersita Romadhona, Waldy Ryan Randy Suryono Sakti, Adam Indra Sanriomi Sintaro Saputra, Dany Eka Sebastian, Dicky Fernanda Setiawan, Samuel Budi Setiawansyah Setiawansyah Styawati Styawati Styawati, Styawati Taufiq Martiwansyah Very Hendra Saputra Wahyudi, Agung Deni Wang, Junhai Yasinta Ismi Yohanes Suyanto Yunita Yunita Yuri Rahmanto Zaenal Abidin Zai, Charles Zeafli Pebriawan Zulfa Nabila