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All Journal Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Informatika Kaputama (JIK) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmiah Islamic Resources Jurnal Litbang Kota Pekalongan Simpatik: Jurnal sistem Informasi dan Informatika Academic Journal of Computer Science Research (AJCSR)

Eka Fitriani

Universitas Bina Sarana Informatika

Author-ID : 267115

Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Komparasi Algoritma Naive Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Pengguna Busway Riska Aryanti; Atang Saepudin; Eka Fitriani; Rifky Permana; Dede Firmansyah Saefudin
JURNAL TEKNIK KOMPUTER Vol 5, No 2 (2019): JTK - Periode Agustus 2019
Publisher : Universitas Bina Sarana Informatika

Congestion major cities in Indonesi caused by the proliferation of the use of private vehicles. Some expressing he thinks about busway user through the social media and other web site, This opinion can be used as a sentiment analysis to see if the user busway proposes a review of positive or negative. The results of the analysis sentiment can help in the sight of and evaluate the use of busway, also expected to improve and transjakarta facility from so they tend to have an opinion positive. Based on the results of the analysis, sentiment it is hoped people will switch to using the will of course will reduce congestion. In the study also added the stages preprocesing by using the framework gataframework to complete the process that cannot be done on tools rapidminer. The methodology that was used in this research was it is anticipated that analysis the sentiment of the by the application of an genetic algorithm for an election features with an algorithm naive bayes. From the results of the testing to the case in research it is found that classification algorithm naive bayes based genetic algorithm having the kind of accuracy that good enough 88,55 % and value of auc reached 0,813 % with the level of the diagnosis classifications good. So that in this research classification algorithm naive bayes based genetic algorithm can be recommended as algorithms classifications good enough to analyze the busway user sentimen. Based on analysis is expected to private transport users will switch to using the busway will reduce congestion

Optimasi Algoritma SVM Dan k-NN Berbasis Particle Swarm Optimization Pada Analisis Sentimen Fenomena Tagar #2019GantiPresiden Atang Saepudin; Riska Aryanti; Eka Fitriani; Dahlia Dahlia
JURNAL TEKNIK KOMPUTER Vol 6, No 1 (2020): JTK-Periode Januari 2020
Publisher : Universitas Bina Sarana Informatika

Analisis sentimen adalah proses untuk menentukan konten dataset berbasis teks yang positif atau negatif. Saat ini, opini publik menjadi sumber penting dalam keputusan seseorang dalam menemukan solusi. Algoritma klasifikasi seperti Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) diusulkan oleh banyak peneliti untuk digunakan dalam analisis sentimen untuk pendapat ulasan. Namun, klasifikasi sentimen teks memiliki masalah pada banyak atribut yang digunakan dalam dataset. Fitur pemilihan dapat digunakan sebagai proses optimasi untuk mengurangi set fitur asli ke subset yang relatif kecil dari fitur yang secara signifikan meningkatkan akurasi klasifikasi untuk cepat dan efektif. Masalah dalam penelitian ini adalah pemilihan pemilihan fitur untuk meningkatkan nilai akurasi Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) dan membandingkan akurasi tertinggi untuk analisis sentimen tweet / komentar yang menggunakan tagar # 2019GantiPresiden. Algoritma perbandingan, SVM menghasilkan akurasi 88,00% dan AUC 0,964, kemudian dibandingkan dengan SVM berdasarkan PSO dengan akurasi 92,75% dan AUC 0,973. Data hasil pengujian untuk akurasi algoritma k-NN adalah 88,50% dan AUC 0,948, kemudian dibandingkan untuk akurasi dengan PSO berbasis k-NN sebesar 75,25% dan AUC 0,768. Hasil pengujian algoritma PSO dapat meningkatkan akurasi SVM, tetapi tidak mampu meningkatkan akurasi algoritma k-NN. Algoritma SVM berbasis PSO terbukti memberikan solusi untuk masalah klasifikasi tweets/ komentar yang menggunakan tagar # 2019GantiPresiden di Twitter agar lebih akurat dan optimal.

Implementasi Metode Naive Bayes Dalam Penyeleksian Karyawan untuk Penempatan Bagian Pemasaran Eka Fitriani; Royadi Royadi; Atang Saepudin; Dian Ardiansyah; Riska Aryanti
Jurnal Teknik Komputer AMIK BSI Vol 8, No 2 (2022): JTK Periode Juli 2022
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v8i2.12532

Marketing is a job that has a scope of work on the promotion of a product, goods or service. The problem that always occurs in the company is that there is no department responsible for selecting reliable marketing employees. The existence of these problems resulted in the process of recruiting employees for the new marketing division which was still not carried out professionally. This can happen because there is no standard method to be able to support in assessing the selection of prospective employees in the marketing department, it is necessary to do an analysis related to the selection of employees in the placement of the marketing department. By holding the analysis process for employees in the placement of a new marketing division, it can be seen whether the prospective marketing division employee passes or does not pass. From the existing problems, a data mining classification method is used to predict the selection of employees for the Marketing section by using the nave Bayes method. After testing using the nave Bayes method, it produces an accuracy value of 87.22% and an AUC value of 0.920 with an Excellent Classification diagnostic level. So it can be concluded that using the nave Bayes method can be a good method for implementation in selecting employees for placement in the Marketing department.

Co-Authors Abdussomad Abdussomad Ardiana Priharwanti Atang Saepudin Baiti, Nurul Dahlia Dahlia Dede Firmansyah Saefudin Dede Saefudin Dian Ardiansyah Dila Delia Fadilah Duwi Cahya Putri Buani Hakim, Ahmad Ilham Kurniawan Muhammad Faittullah Akbar Negsi, Ratika Richardus Eko Indrajit Rifky Permana Riska Aryanti Rizal Amegia Saputra Royadi - SALMAN ALFARIZI Widya Apriliah

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