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All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Sains dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika METIK JURNAL
Windu Gata
STMIK Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Social Sciences

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Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah Riki Supriyadi; Windu Gata; Nurlaelatul Maulidah; Ahmad Fauzi
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.247

Abstract

Abstract In this study that was used as the object of research in classifying red wine based on the quality influenced by each red wine or red wine based on the content of each type of wine, from each attribute containing the composition in the wine seen which attributes most affect the quality of red wine, so that it will be known ingridents that can improve the quality of the wine, in this study was carried out by the application of Machine learning by comparing three algorithms of mining data that is , Decission Tree, Random Forest and Support Vector Machine (SVM), from the results of research that has been done by comparing the three algorithms, Random Forest produced the best accuracy among other algorithms that have been tested. Random Forest with accuracy results of 0.7468 makes this algorithm best used to classify the quality of red wine. And in the second order Decission Tree with accuracy results of 0.7031, while Support Vector Machine (SVM) get an accuracy result of 0.65. So in the research that has been done to classify the quality of red wine based on its composition Random Forest becomes the best algorithm to use..
ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Mawadatul Maulidah; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

Abstract

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.
Co-Authors Abdul Hamid Abdul Latif Abdul Latif Abdussomad, Abdussomad Achmad Maezar Bayu Aji Achmad Rifai Ade Irma Rizmayanti Agustiani, Sarifah Akrom, Akrom Ali Mustopa, Ali Andi Saryoko Angelina Puput Giovani Ardiansyah Ardiansyah Ardiansyah Arifin Nugroho Chintamia Bunga Sari Dewi Cucu Ika Agustyaningrum Dedi Priansyah Deni Anugrah Sahputra Deni Gunawan Diantika, Sri Didi Rosiyadi Didi Rosiyadi Dwi Andriyanto Erni Erni Fachrurozi, Ahmad Fadillah Said Fajar Pramono Fakihotun Titiani Fariszal Nova Arviantino Grace Gata, Grace Hafez Aditya Hiya Nalatissifa Husain, Syepry Maulana Ikin Rojikin Imam Santoso Ipin Sugiyarto Irwan Herliawan Istiqal Hadi Jajang Jaya Purnama Jordy Lasmana Putra Kartika Handayani Khoirun Nisa Laela Kurniawati Lilyani Asri Utami, Lilyani Asri M. Rangga Ramadhan Saelan Mawadatul Maulidah Mufid Junaedi Muhammad Fahmi Julianto Muhammad Fahmi Julianto Muhammad Iqbal Muhammad Iqbal Muhammad Rifqi Firdaus Nadiyah Hidayati Nia Kusuma Wardhani Nuraeni Herlinawati Nurlaelatul Maulidah Paryanti, Atik Budi Prasetyo, Basuki Hari Rangga Pebrianto Ranu Agastya Nugraha Rendi Septian Retno Sari Rhini Fatmasari Ridan Nurfalah Ridwansyah Ridwansyah Riki Supriyadi Rizki Aulianita Safitri Linawati Saifurrachman Chohan Samudi Samudi Setiaji Setiaji Sidik Sidik Siswanto, Siswanto Siti Faizah Siti Khotimatul Wildah Siti Nurhasanah Nugraha Sofian Wira Hadi Sri Rahayu Sukmawati Anggraeni Putri Sukri Syafrudin Suwanda Aditya Aaputra Syaifur Rahmatullah Tri Rivanie Tuti Haryanti Wawan Kurniawan Widiastuti Widiastuti Yudhistira Yudhistira Yuliani, Yuri Yusuf Arif Setiawan