Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Jurnal Pilar Nusa Mandiri Dinasti International Journal of Digital Business Management Jurnal Abdimas Perbanas Jurnal Pariwisata, Bisnis Digital dan Manajemen
Prakoso, Bobby Suryo
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Pilar Nusa Mandiri

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1384.907 KB) | DOI: 10.33480/pilar.v15i2.684

Abstract

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
USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION Aridarma, Dedi; Sadikin, Rifki; Prakoso, Bobby Suryo; Utama, Heru Sukma
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1038.378 KB) | DOI: 10.33480/pilar.v16i1.702

Abstract

Religious lectures are activities that are identical to the religious presentation, delivered verbally by a person who has religious knowledge and then delivered to the community with the aim of the knowledge delivered can be understood. Ustadz Abdul Somad was one of the preachers who had been known to various levels of society, but his lectures were not all acceptable to the people who liked or disliked those who came from various positive and negative comments on social media. To solve these problems, Sentiment Analysis was used by applying the Support Vector Machine Algorithm method. The purpose of this study is to compile using the selection of feature Particle Swarm Optimization and Information Gain. The results for Particle Swarm Optimization Selection Feature resulted in Accuracy of 80.57%, Precision of 85.45%, and Recall of 79.52%, Selection Feature Information Gain resulted in Accuracy of 79.78%, Precision of 78.47%, and Recall of 78, 43%, Based on the results of this study, it can be concluded that using the Particle Swarm Optimization selection feature is better at the level of accuracy when compared to using the Information Gain selection feature.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1207.156 KB) | DOI: 10.33480/pilar.v15i2.705

Abstract

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 Abdullah, Hamzah Ali Afriano, Daniel Chandra Aridarma, Dedi Fabrianto, Luky Fauzi, Fariz Febrianti, Nurlia Gani Wiharso Nabilah, Saripah Prasetyo, Johan Hendri Qhomar, Mohammad Arifin Nurul Rosiyadi, Didi Sadikin, Rifki Safitri, Rizky Ade Utama, Heru Sukma Yuwanda, Henny