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All Journal International Journal of Electrical and Computer Engineering Jurnal Pilar Nusa Mandiri Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Buletin Poltanesa Journal of Students‘ Research in Computer Science (JSRCS) Reputasi: Jurnal Rekayasa Perangkat Lunak

Rosiyadi, Didi

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Author-ID : 1102920

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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OPTIMALISASI KLASIFIKASI BERITA MENGGUNAKAN FEATURE INFORMATION GAIN UNTUK ALGORITMA NAIVE BAYES TERHUBUNG RANDOM FOREST Prakoso, Bobby Suryo; Rosiyadi, Didi; Aridarma, Dedi; Utama, Heru Sukma; Fauzi, Fariz; Qhomar, Mohammad Arifin Nurul
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Penelitian ini adalah tentang pengklasifikasian berita yang mengoptimalisasi dengan kombinasi antar algoritma. Tentang dataset yang digunakan diambil pada situs pemberitaan online. Algoritma yang digunakan adalah algoritma Naive Bayes Classifier, dan Random Forest dengan pembobotan seleksi fitur Information Gain. Dataset yang digunakan terdapat 615 dataset dengan 3 katagori atau tema berita. Dalam permodelan terdapat 6 model skenario sebagai pembanding untuk menentukan skenario mana yang mendapatkan nilai terbaik, berdasarkan hasil penelitian ini nilai terbaik didapatkan oleh model Remove Useless Attributes, Naive bayes Classifier-Multinomial, dan Random Forest-Feature Selection Information gain. Hasil evaluasi yang didapatkan adalah nilai accuracy 85.67%, nilai recall 85.67%, dan nilai precision 86.23

SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN Utama, Heru Sukma; Rosiyadi, Didi; Aridarma, Dedi; Prakoso, Bobby Suryo
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

Co-Authors Abdul Mahatir Najar Abdullah, Ifnu Abdul Aziz Agus Indra Jaya Aridarma, Dedi Basuki, Akbari Indra Cahya, El Rangga Un Chandra Nugraha, Chandra Fauzi, Fariz Hadi Susanto Irawan, Rama Iwan Setiawan Jamil, Muh. Kurniawan, Rohim Mulyanto, Yudi Nila Hardi Prakoso, Bobby Suryo Qhomar, Mohammad Arifin Nurul RACHMAT HIDAYAT Salim, Taufik Ibnu Saputra, Surya Fajar Sugiyarto, Ipin Utama, Heru Sukma

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Journal : Jurnal Pilar Nusa Mandiri