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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam 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 J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science
Alita, Debby
Universitas Teknokrat Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health 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%.
ANALISIS SENTIMEN MASYARAKAT TERHADAP KASUS JUDI ONLINE MENGGUNAKAN DATA DARI MEDIA SOSIAL X PENDEKATAN NAIVE BAYES DAN SVM M Febrian As Shidiq; Debby Alita
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3624

Abstract

Research conducted by analyzing public sentiment related to online gambling cases using datasets from x social media using the naïve bayes method approach and support vector machine (SVM). The analysis phase starts with data gathering or crawling, followed by data labeling, data preprocessing, and ultimately method categorization. The dataset comprises 2,866 tweets, with 1,436 classified as positive (50.12%) and 1,429 as negative (49.88%). The data before to the classification process is partitioned into training data and testing data, including 70% training data and 30% testing data. The analysis with the SVM approach yielded a classification accuracy of 83%, whereas the naïve Bayes method achieved just 79%. Upon completion of the method classification process, the subsequent phase involves visualization and assessment. During the visualization step, bar plots, word clouds, and word frequencies derived from sentiment analysis calculations are shown, alongside a visualization of words from the dataset. The investigation indicates that the SVM approach outperforms Naive Bayes in sentiment classification. The benefit of SVM resides in its capability to manage data with elevated limits and accuracy, enhancing its efficiency in discerning positive and negative thoughts. The findings of this study demonstrate that SVM is better appropriate for data exhibiting complicated distributions, whereas the Naive Bayes approach yields suboptimal results. Thus, SVM can be proposed as a more appropriate and reliable approach for similar sentiment analysis in the future.
Co-Authors ., Rusliyawati Ade Dwi Putra Ade Setiawan alifa, Chairunnisa fadia Andi Kurniawan Andi Nurkholis Anggraini, Jenny Aris Munandar Asmawati Asmawati Asmawati Asmawati Bambang Satrio Gandhi Cholid Fadilah Hasri Danuarta, Ervin Dedi Darwis Dinda Titian Lestari Dyah Aminatun Dyah Ayu Megawaty Dyah Ayu Megawaty Emi Suwarni Erliyan Redy Susanto Fadila Shely Amalia Feby Amanda Fenty ariany Handika, H Hasrina, Cut Delsi Hayatunnufus Hayatunnufus Heni Sulistiani Ika Septiana Imam Ahmad Imam Ahmad Indah Sari Irwan Tubagus Isnain, Auliya Rahman Jepi Supriyanto Krisna, Anan Lathifah L Lathifah Lathifah Lisa Ariyanti M Febrian As Shidiq Mandasari, Berlinda Marga, Nurman Satya Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Munti Sarida Nur Rokhman Nurhasan Nugroho Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Pustika, Reza Putra, Ade Dwi Putri Sukma Dewi Rahmadila, Selvi Rahmawati, Ira Tri Rani, Ni Made Suka RB Ali Shodiqin Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Sakti, Adam Indra Salsabilla, Tasya SANTIKA, YULI Saputri, Gita Aprinda Sari, Raras Kartika Sena, Fikih Yuhada Setiawansyah Setiawansyah Sigit Priyanta Styawati Styawati Styawati, Styawati suaidah suaidah Suhadi, Miki Surahman, Ade Suwarni, Emi Syahwaluddin, Risal Taufiq Martiwansyah Tenda, Edwin Wang, Junhai YOHANA TRI UTAMI, YOHANA TRI Yuri Rahmanto Yusra Fernando