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All Journal Jurnal Teknik Komputer AMIK BSI Paradigma JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) Computer Science (CO-SCIENCE) Jurnal Abdimas Komunikasi dan Bahasa Simpatik: Jurnal sistem Informasi dan Informatika Profitabilitas DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Sistem Informasi dan Manajemen Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Atang Saepudin
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (204.905 KB) | DOI: 10.31294/jtk.v5i2.5406

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.407 KB) | DOI: 10.31294/jtk.v6i1.6828

Abstract

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.
Perancangan Sistem E-Commerce Menggunakan Model Rapid Application Development Pada Pengurus Cabang Judo Karawang Atang Saepudin; Riska Aryanti; Eka Fitriani; Dian Ardiansyah
Paradigma Vol 23, No 1 (2021): Periode Maret 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (984.62 KB) | DOI: 10.31294/p.v23i1.9822

Abstract

The development of information technology which is increasingly advanced and dynamic and the increasing competition in the global market has made all competition, both labor and business sectors, tighter. Of course this affects all types of businesses and business actors must start thinking about new breakthroughs so that their business can survive and be able to compete with other business competitors. The lack of breakthrough in marketing is experienced by the Judo Karawang Branch Management, where marketing is the spearhead of a business. So, if the marketing strategy is blunt, it will have an impact on the process of business activities. In order for these businesses to survive, the use of information technology is of course the main choice in business development, especially in terms of promotion and sales of services so that they can compete with other businesses. To increase competitiveness and to obtain other business opportunities, this can be done by taking advantage of developments in Information and Communication Technology (ICT). The system development method used in this study is the Rapid Application Development model. This model is used because it is considered a development method that has a short and fast development time. Referring to previous research, the researcher chose this RAD model because it was considered appropriate for the e-commerce system that the researcher built. E-commerce will make it easier for producers in marketing activities and also cut operating costs for trading and marketing activities so that it will increase sales volume and increase revenue. This increase in income will eventually expand the business.
Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing Eka Fitriani; Riska Aryanti; Atang Saepudin; Dian Ardiansyah
Paradigma Vol 22, No 1 (2020): Periode Maret 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.029 KB) | DOI: 10.31294/p.v22i1.6898

Abstract

Marketing is a job that has a scope of work to market a product. Marketing is at the forefront of the company. The problem that often occurs in companies is not having a reliable marketing force. The problem arises because the process of accepting new marketing is still not done professionally. This happens because there is no systematic standard method to assess the feasibility of marketing candidates. Therefore it is necessary to analyze the marketing placement so that it can determine the feasibility of a new marketing placement problem. Through the results of the analysis of new marketing placements, it can be seen whether the marketing candidates passed or did not qualify. Of the problems that exist testing the data mining classification method to find out the algorithm to predict marketing feasibility is to use an algorithm that is C4.5. After testing with the C4.5 algorithm the results obtained are that the C4.5 algorithm produces an accuracy value of 91.10% and an AUC value of 0.921 with a diagnosis level of Excellent Classification. So that the conclusion C4.5 algorithm is a good algorithm to be applied to the feasibility of marketing placement. Keywords : Marketing Feasibility, Data Mining, C4.5
PERANCANGAN SISTEM INFORMASI AKADEMIK MADRASAH ALIYAH NEGERI (MAN) 4 KARAWANG BERBASIS WEB Dian Ardiansyah; Atang Saepudin; Riska Aryanti; Eka Fitriani
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol 3 No 2 (2020): Jurnal Teknologi dan Open Source, December 2020
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v3i2.783

Abstract

The development of web-based information technology is currently increasing. One of them is the website. The website has the advantage that all the information you want can be easily and cheaply obtained. Madrasah Aliyah Negeri (MAN) Rengasdengklok or now known as MAN 4 Karawang currently processing academic data, especially in processing grades is still very simple, namely only with Microsoft Excel. The presence of this academic information system is expected to help teachers and students get academic information more quickly, easily and cheaply. In building this system the writer uses the waterfall methodology. The design and implementation were carried out with the Dreamweaver programming language and PHP and the database used by PhpMyAdmin. With this academic information system, hopefully it can help to support school performance, especially for processing academic data.
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

Abstract

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.
Penerapan Metode Rapid Application Development Dalam Pengembangan Sistem Informasi Akademik Berbasis Web Riska Aryanti; Eka Fitriani; Dian Ardiansyah; Atang Saepudin
Paradigma Vol 23, No 2 (2021): Periode September 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.206 KB) | DOI: 10.31294/p.v23i2.11170

Abstract

SMA Panca Moral Cikampek merupakan suatu perwujudan dalam membentuk, mendidik genarasi muda bangsa Indonesia. Semakin bertambahnya jumlah siswa setiap tahunnya, dituntut ketepatan dan ketelitian dalam memberikan suatu informasi yang akurat dan tepat kepada siswa tanpa adanya perulangan data atau informasi yang sama. SMA Panca Moral Cikampek sangat membutuhkan adanya sebuah sistem yang dapat menunjang dalam proses penyajian informasi akademik. Untuk menangani masalah tersebut maka dibutuhkan suatu sistem Informasi Akademik Berbasis Web Pada SMA Panca Moral Cikampek sebagai salah satu solusi untuk memberikan sebuah informasi secara akurat, cepat dan tepat. Sistem informasi akademik yang ditawarkan dapat mengelola mengenai informasi akademik baik data absensi siswa, absen mengajar masuk dan absen mengajar guru, mengelola data nilai, jadwal pelajaran, jadwal mengajar guru, serta dapat menerbitkan informasi-informasi yang berhubungan dengan akademik. Pengembangan sistem informasi akademik berbasis web ini menggunakan metode rapid application development(RAD), model pengembangan ini digunakan karena model ini dianggap model dengan mengutamakan waktu, sehingga pengerjaannya relatif singkat sehingga dengan adanya pengembangan sistem informasi ini dapat berguna dalam memberikan kemudahan baik pelajar maupun kepada pengajar.
ANALISIS SENTIMEN REVIEW PADA APLIKASI MEDIA SOSIAL TIKTOK MENGGUNAKAN ALGORITMA K-NN DAN SVM BERBASIS PSO Dian Ardiansyah; Atang Saepudin; Riska Aryanti; Eka Fitriani; Royadi
Jurnal Informatika Kaputama (JIK) Vol 7 No 2 (2023): Volume 7, Nomor 2, Juli 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v7i2.148

Abstract

Review sentiment analysis on social media applications is one of the methods used to analyze opinions and feelings (sentiment) of social media users towards a particular product, service or topic. Tiktok social media users are the second most in the world. The Tiktok app is the leading social media platform and the ultimate destination for short-form videos. Music, dance, education, beauty, passion, or talent show. This research uses data from Tiktok application reviews based on positive and negative sentiments to compare the K-Nearest Neighbor (K-NN) and Particle Swarm Optimization (PSO)-based Support Vector Machine (SVM) algorithms. To test the results of the PSO-based K-NN and SVM algorithms using the Cross Validation method from the test results that the PSO optimization SVM algorithm has the best accuracy compared to the KNN algorithm. Where the accuracy value of SVM is 86.40% and AUC is 0.908. The PSO optimization SVM has an accuracy of 88.20% and an AUC of 0.91. While the K-NN algorithm has an accuracy of 83.40% and an AUC of 0.903 then the accuracy value of the K-NN optimization PSO gets an accuracy of 69.20% and an AUC of 0.77. This means that the use of the PSO optimization SVM algorithm has the highest level of accuracy.
Pemanfaatan Internet Dalam Menunjang Kegiatan Belajar Mengajar Di Masa Pandemi Covid-19 Aryanti, Riska; Saepudin, Atang; Wahyuni, Tri; Hasan, Fuad Nur; Harefa, Kristine
Jurnal Abdimas Komunikasi dan Bahasa Vol. 1 No. 1 (2021): Juni 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (772.393 KB) | DOI: 10.31294/abdikom.v1i1.331

Abstract

Pandemi Covid-19 berdampak besar pada berbagai sektor, salah satunya pendidikan. Dunia pendidikan juga ikut merasakan dampaknya. Pendidik harus memastikan kegiatan belajar mengajar tetap berjalan, meskipun peserta didik berada di rumah. Solusinya, pendidik dituntut mendesain media pembelajaran sebagai inovasi dengan memanfaatkan media daring (online). Ini sesuai dengan Menteri Pendidikan dan Kebudayaan Republik Indonesia terkait Surat Edaran Nomor 4 Tahun 2020 tentang Pelaksanaan Kebijakan Pendidikan dalam Masa Darurat Penyebaran Corona Virus Disease (Covid-19). Sistem pembelajaran dilaksanakan melalui perangkat Mobile Phone, Personal Computer (PC) atau laptop yang terhubung dengan koneksi jaringan internet. Proses belajar mengajar akhirnya berubah dari bertatap muka secara langsung dikelas menjadi pembelajaran berbasis jaringan/internet atau yang biasa disebut dengan istilah daring. Tidak sedikit peserta didik bahkan pendidik yang masih belum terbiasa dengan sistem pembelajaran daring ini. Oleh karena itu, dosen Program Studi Ilmu Komputer (S1) Universitas Bina Sarana Informatika akan menyelenggarakan sosialisasi/pelatihan terhadap peserta didik khususnya pada warga RT. 002/RW.002 Tegal Parang–Jakarta Selatan. Pelatihan tersebut memiliki tema Pemanfaatan Internet Dalam Menunjang Kegiatan Belajar Mengajar Di Masa Pandemi Covid-19. Pelatihan ini diharapkan mampu menambah pemahaman dan keterampilan para peserta agar lebih mampu memanfaatkan layanan internet dengan lebih bijak untuk menunjang proses belajar mengajar di masa pandemi Covid-19 saat ini. Adapun target luaran dari pelaksanaan Pengabdian Masyrakat ini yaitu berupa publikasi artikel di media online, video dokumentasi kegiatan dan meningkatkan pengetahuan dan keterampilan peserta dalam memanfaatkan layanan internet.
Analisis Sentimen Pemanfaatan Artificial Intelligence di Dunia Pendidikan Menggunakan SVM Berbasis Particle Swarm Optimization Saepudin, Atang; Aryanti, Riska; Fitriani, Eka; Royadi, Royadi; Ardiansyah, Dian
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2921

Abstract

The utilization of Artificial Intelligence (AI) in the field of education in Indonesia has witnessed significant developments in recent years. The advancements in AI technology have opened up new opportunities to enhance the quality of education, and address various challenges faced by the Indonesian education system. This has naturally sparked diverse opinions and comments from the public, particularly on the social media platform X/Twitter. This research focuses on sentiment analysis of reviews expressed on the X/Twitter social media platform. The primary goal of this study is to develop an effective sentiment analysis method by leveraging the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. In this research, user reviews from X/Twitter were collected and analyzed to identify positive or negative sentiments within the context of each comment. The SVM algorithm was used to classify sentiments based on similarity to comments with known sentiments. Feature Selection PSO was employed to optimize the parameters within SVM to enhance sentiment analysis accuracy. The results of sentiment analysis on comments or tweets on the X/Twitter social media platform using both SVM and PSO-based SVM algorithms indicated that the PSO-based SVM algorithm achieved a higher accuracy. The SVM algorithm with feature selection PSO produced accuracy 89.50%, precision 86.98%, recall 93.00%, and AUC 0.964. Meanwhile, the SVM algorithm had accuracy 87.50%, precision 85.46%, recall 90.50%, and AUC 0.956. This demonstrates that the use of feature selection PSO in the SVM algorithm is capable of improving the accuracy of the results.
Co-Authors Agustiani, Sarifah aryo tunjung kusumo Aryo Tunjung Kusumo Dahlia Dahlia Dede Firmansyah Saefudin Dian Ardiansyah Eka Fitriani Haliza Ramadhanti, Pristya Harefa, Kristine Hasan, Fuad Nur Kusumo, Aryo Tunjung Rifky Permana Riska Aryanti Roy Prasetya, Andreas Royadi - Royadi, Royadi Rudianto, Biktra tri wahyuni Tunjung Kusumo, Aryo Zulyanto, Yudi