Article Per Year (5 Year)
p-Index From 2021 - 2026
0.835
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Journal of Practical Computer Science (JPCS) SAINTEK
Aswan S Sunge
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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

Found 2 Documents
Search
Journal : journal of practical computer science jpcs

 1 
Optimasi Algoritma Genetika Dalam Memprediksi Minat Baca Siswa Pada Perpustakaan SMK Negeri 1 Gantar Dengan Metode Decision Tree Lina Yulita; Aswan S Sunge; Nisa Nurhidayanti
Journal of Practical Computer Science Vol. 2 No. 1 (2022): Mei 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v2i1.949

Abstract

Students are one part of the world of education that cannot be separated from reading activities. Each school certainly seeks to provide reading facilities such as school libraries, as well as libraries owned by SMK Negeri 1 Gantar aim to be able to foster and increase students' interest in reading books in the school library. But at the moment the library of SMK Negeri 1 Gantar tends to be minimal in number of visitors, this could be due to the lack of student awareness of the importance of reading books or there are other factors that can influence such as service, type of book, comfort, collection of books and so on. Then conducted a study that aims to find out how much interest in reading students of SMK Negeri 1 Gantar library using genetic algorithm optimization with the decision tree method. The data used in this study are visitor data owned by the library of SMK Negeri 1 Gantar as many as 290 data, the process of testing the method using RapidMiner 9.2. Based on the results of testing on research in predicting students' reading interest in the library of SMK Negeri 1 Gantar, the results obtained from the C4.5 algorithm or decision tree are accuracy by 84.48% and after being optimized using genetic algorithms the accuracy increases by 12.07% so that the accuracy value obtained from optimization of 96.55%. Then it can be concluded that the genetic algorithm optimization technique in value succeeded in increasing the accuracy of the C4.5 algorithm or decision tree in predicting students' interest in reading at the SMK Negeri 1 Gantar library. Keyword: Reading interest, library, C4.5 algorithm, genetic algorithm.
Analisis Sentimen Tentang Mobil Listrik Dengan Metode Support Vector Machine Dan Feature Selection Particle Swarm Optimization Ahmad Santoso; Agung Nugroho; Aswan S Sunge
Journal of Practical Computer Science Vol. 2 No. 1 (2022): Mei 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v2i1.1084

Abstract

Analisis sentimen twitter merupakan teknik untuk mengidentifikasi sentimen atau pendapat dalam tweet dan kemudian mengategorikannya ke dalam tweet positif atau tweet negatif salah satu topik yang dibahas pada social media twitter adalah mobil listrik, mobil listrik memiliki beberapa kelebihan dibandingkan dengan mobil bahan bakar fosil. Mobil listrik ini menuai banyak komentar dari masyarakat sehingga menimbulkan pro dan kontra di sosial media twitter. Penelitian ini dilakukan tujuannya untuk mengetahui pendapat masyarakat terhadap mobil listrik. Apakah pendapat tersebut lebih mengarah ke positif atau negatif dan untuk mengetahui nilai accuracy, AUC dari penggunaan metode Support Vector Machine dan feature selection Particle Swarm Optimization pada Software RapidMiner Studio. di dalam penelitian ini dapat diketahui bahwa 94,25% pengguna twitter setuju dan 5,75% pengguna twitter tidak setuju terhadap kehadiran mobil listrik. Penggunaan feature selection Particle Swarm Optimization pada metode support vector machine untuk menganalisis sentimen masyarakat mengenai mobil listrik dapat meningkatkan nilai accuracy dan AUC. Dimana nilai accuracy yang awalnya sebesar 82,51% menjadi 86,07%, terjadi kenaikan sebesar 3,56%. Sedangkan nilai AUC yang awalnya sebesar 0,844 menjadi 0,862 terjadi kenaikan sebesar 2,13%. Kata kunci: Analisis Sentimen, Text Mining, Support Vector Machine, Particle Swarm Optimization, Mobil Listrik.
Co-Authors Agung Nugroho Ahmad Santoso Ahmad Turmudi Zy Edy Widodo Lina Yulita Miftah Wangsadanureja Nur Azizah Nurhidayanti, Nisa Riani Riani Tahyani Tahyani Wahyu Hadikristanto