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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Intelektualita: Keislaman, Sosial, dan Sains POSITIF Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Ilmiah Matrik INOVTEK Polbeng - Seri Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Global JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Mantik Jurnal Muara Ilmu Ekonomi dan Bisnis Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) International Journal of Advanced Science Computing and Engineering Bulletin of Social Informatics Theory and Application
Edi Surya Negara
Universitas Bina Darma
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOIN (Jurnal Online Informatika)

 1 
Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms Amanda, Riyan; Negara, Edi Surya
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.505

Abstract

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is Naïve Bayes, with an accuracy rate of 88% and an average precision of 90%.
Classification of the Fluency Multipurpose of Bank Mandiri Credit Payments Based on Debtor Preferences Using Naive Bayes and Neural Network Method Putri Armilia Prayesy; Edi Surya Negara
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One that has an important role in generating bank profits is providing credit to customers, but credit also carries a very high risk. For this reason, in providing credit to debtors, of course the bank will utilize the personal data of prospective debtors in detail to avoid the risk of problems that will arise in the future. One of the appropriate risks for banks in providing credit is the behavior of customers who do not pay installments at the time which causes bad loans. To overcome and overcome the many bad events, there is an algorithmic calculation method with an intelligent computing system that helps banks in selecting prospective debtors who will be given credit. There are many algorithmic methods that can be used in this kind of research. This study analyzes the classification of staffing credit based on the criteria that become the Bank's standard.The data used by the author in this study uses existing debtor credit data from 2017 to 2020, the modeling process is carried out using split validation with the Naive Bayes algorithm and Neural Network, with this algorithm the 1,314 datasets is divided into 2 parts, namely 80% used as training data and 20% used as testing data. The results showed that the Neural Network algorithm has better results with a correct value of 84.13%, while the Naive Bayes algorithm only produces a value of 72.62%
Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms Riyan Amanda; Edi Surya Negara
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.505

Abstract

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is Naïve Bayes, with an accuracy rate of 88% and an average precision of 90%.
Co-Authors - Bhagaskara Abi daud Yusuf Adam Prasetya Ade Putra Amanda, Riyan Amin, Zulius Akbar Andreean Dharma Arisandi Andry Meylani Andryani, Ria Andryani, Ria Bhianta Wijaya Chairul Mukmin Deddy Rezano Akhiruddin Dedek Julian Dedi Irawan Dedy Syamsuar Dedy Syamsuar Dendi Triadi Dendi Triadi Diana Diana Fauzan Azhiman Ferdiansyah Ferdiansyah Fernandy Jupiter Herdiansyah, M. Izman Jepri Yandi Juminovario Juminovario KENI KENI Kiki Rizky Nova Wardani Kurniawan, Tri Basuki Linda Atika Linda Atika Liza Fahreni M. Izman Herdiansyah M. Izman Herdiansyah Mery Sintia Muhamad Akbar Muhammad Akbar Muhammad Cahyono Muhammad Izman Herdiansyah Muhammad Izman Herdiansyah Muhammad Marzuki Muhammad Raihan Nanda Tri Haryati Nely Apriyanti Nico Michael Bryan Novita Anggraini Nurhachita Nurhachita Nurhachita Nurhachita Prihambodo Hendro Saksono Purnama Dharmawan Puspita Dewi Setyadi Putri Armilia Prayesy Qisthiano, M Riski Rahmad Kartolo Rahmat Novrianda Dasmen Reza Pahlevi Reza Vidi Aditama Ria Andriani Ria Andryani Rifan Fadilah Riyan Amanda Saksono, Prihambodo Hendro Selvia Damayanti Sunda Ariana, Sunda Tata Sutabri Tri Basuki Kurniawan Tri Basuki Kurniawan Wawan Setiawan Widya Cholil Widya Cholil, Widya Winata Nugraha Winoto Chandra Yeni Widyanti Yepi Kusmeta Yesi Novaria Kunang Yessi Novaria Kunang Yuni Amrina Yusuf Andi Putra Zulius Akbar Amin