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Universitas Bina Sarana Informatika

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WATERFALL METHODS FOR APPLICATION OF ACCOUNTING INFORMATION SYSTEMS IN HOTEL INCOME MANAGEMENT CASE STUDY: CITRA GRAND HOTEL KARAWANG dede firmansyah saefudin; Widya Apriliah; lham Kurniawan; Yuli Komalasari; Muhammad Faittullah Akbar; Royadi -
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol 4 No 1 (2021): Jurnal Teknologi dan Open Source, June 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i1.1369

Abstract

The need for information systems in the business more increase as the same as technological developments in the digital era which give the impacts rapidly changes in all sectors. The accounting cycle in the company is inseparable from the existence of financial recording activities in a certain period. Thus it requires an application to provides the processing financial data or it’s called accounting information system. The Grand Hotel Karawang is the focus of research on building income management accounting information system. Based on the data collection method used: observation, interviews and literature studies (library research), it can be concluded that in managing hotel rental income is still manual or not computerized so that why the research to provide solutions based on the need by designing income accounting information system using waterfall software development method, with phases including needs analysis, design, implementation of program code based on open source in the implementation of the Java programming language to make easier to implement into desktop-based applications. Testing is carried out using blackbox testing as a tool for testing each process step by step in the application that all processes are running well (valid) and can be implemented as needed.
Perancangan Program Manajemen Gudang Pada Programmer Room Diskominfosantik Kabupaten Bekasi Dila Delia Fadilah; Abdussomad; Eka Fitriani; Royadi
Simpatik: Jurnal Sistem Informasi dan Informatika Vol. 1 No. 2 (2021): Desember 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (759.725 KB) | DOI: 10.31294/simpatik.v1i2.904

Abstract

Diskominfosantik Kabupaten Bekasi merupakan instansi pemerintah yang menangani pengolahan teknologi informasi di lingkungan Kabupaten Bekasi. Terutama bagian Programmer Room dan Network Operation Center (NOC Room) didalam instansi ini terdapat gudang untuk menyimpan berbagai peralatan teknisi. Dalam rangka pembuatan prasarana jaringan Fiber Optik (FO) sejauh 1.153 km dan tersambung dengan 14 desa, guna mencapai cita-cita yakni menjadikan Kabupaten Bekasi sebagai smart city. Maka Diskominfosantik haruslah dibekali dengan peralatan mengenai teknologi informasi. Untuk mengupayakan pelayanan internet terbaik tersebut Diskominfosantik menyediakan layanan command room untuk menanggapai setiap keluhan pengguna, lalu diteruskan ke Network Operation Center (NOC Room). Setelah itu pihak Diskominfosantik mengirimkan teknisi ke titik gangguan. Untuk itu Diskominfosantik membutuhkan program manajemen gudang yang berfungsi untuk mencatat setiap penggunaan alat teknisi pada saat perbaikan jaringan tersebut. Oleh karena itu Penulis membuat Perancangan Program Manajemen Gudang dengan menggunakan metode pengembangan perangkat lunak Air Terjun (Waterfall). Dimana program ini mampu mencatat setiap transaksi peminjaman dan pengembalian barangserta dapat membuat laporan data peminjaman untuk diserahkan kepada pimpinan instansi. Dengan menggunakan program manajemen pergudangan ini diharapkan mampu mempermudah pencatatan barang-barang di gudang Diskominfosantik Kabupaten Bekasi.
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.
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.