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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Jurnal Ilmiah FIFO Faktor Exacta Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) MULTINETICS Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Jurnal Ilmu Teknik dan Komputer Jurnal Pengabdian Masyarakat Asia Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia International Journal of Advanced Multidisciplinary Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pengabdian Pada Masyarakat Media Abdimas Kapas: Kumpulan Artikel Pengabdian Masyarakat Sinergi: Jurnal Pengabdian Kepada Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Nasional Jurnal Pengabdian Masyarakat Nauli CSRID KOMPUTASI
Saruni Dwiasnati
Universitas Mercu Buana
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Journal : Faktor Exacta

 1 
Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes Devi Fitrianah; Saruni Dwiasnati; Hanny Hikmayanti H; Kiki Ahmad Baihaqi
Faktor Exacta Vol 14, No 2 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i2.9297

Abstract

Customers are people who trust the management of their money in a bank or other financial service party to be used in banking business operations, thereby expecting a return in the form of money for their savings. To reach information to increase company profits, a method is needed to be able to provide knowledge in supporting the data that the company has. The model can be obtained by using predictive data processing of customer data that is categorized as potential or not potential. Data processing can be done using Machine Learning, namely classification techniques. This technique will produce a churn prediction model for determining the category of customers who fall into the Potential or Not Potential category and find out what accuracy value will be generated by applying the classification technique using the Naïve Bayes Algorithm. The parameters used in this study are Gender, Age, Marital Status, Dependent, Occupation, Region, Information. The data used are 150 data from customers who have participated in the savings program to find out whether the customer is in the Potential or Non-Potential category. The accuracy results generated using this data are 86.17% of the tools used by Rapidminner.
Perancangan Sistem Penerimaan Siswa Baru Berbasis Web Pada Sekolah Dasar Islam Plus Baitul Maal Herfandi Herfandi; Saruni Dwiasnati; Kiki Ahmad Baihaqi; reza Avrizal
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.12894

Abstract

Islamic-based education in Indonesia is an educational institution that focuses on forming the character and knowledge of Islamic religious values. Islamic Elementary School Education Institute Plus Baitul Maal in carrying out educational administration procedures still uses the conventional system. The procedure that took place has not been efficient because prospective students are required to complete the registration form sheet by manual means and still stored in the folder. The implementation of information system technology in the education sector is able to provide convenience, especially in terms of efficientness, accuracy and novelty of information. Therefore, the design and creation of a website-based new student admission information system is expected to be able to solve the problem. This research resulted in a website-based new student admission information system with the main Page having Home, About Us, How to Apply, and Contact functionality. The student dashboard page has Home, Student Profile, Document functionality. The admin dashboard page has the functionality of Home, Website Content, Management, Document Completeness, and Settings as an admin user management serves to set up admin accounts (Create, Record, Update and Delete (CRUD) for admin accounts), development methods using waterfalls, research methods using qualitative and information system testing using black box testing that gets conclusions according to various functionality tests. This system is expected to ease the work of new student admission administrators as well as adjustments to education in the Industrial 4.0 era.
Deteksi Pelat Nomor Dengan Menggunakan Optical Character Recognition Berbasis Algoritma LSTM Wardhani, Elisa; Dwiasnati, Saruni
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19216

Abstract

Illegal Parking which has become a frequent occurrence in the Jakarta area, encourages system transformation which includes the addition of automatic parking system facilities based on the building infrastructure and facilities regulation. The development of an automatic parking system can utilize license plate number detection to minimize the need to manually input license plate numbers into the parking system. In this study, LSTM algorithm training is done and implemented on Optical Character Recognition to detect license plate numbers accurately. Based on the evaluation results, the LSTM algorithm has a good performance in detecting license plate numbers with an accuracy rate of 86,36%. However, the LSTM algorithm performance improved when implemented on Optical Character Recognition with an accuracy rate of 95,8%. Hence, based on the evaluation, the LSTM algorithm that has been implemented on Optical Character Recognition is considered a preferable choice in license plate number detection as it has a higher level of accuracy compared to the use of the LSTM algorithm alone.
Co-Authors Abdillah, Reza Wahyu Agung Susilo Yuda Irawan Ahmad Fauzi Akmal, Medina Fibriyanti Alfa, Bonitasari Nurul Anissa Lestari Antono, Fajar Arif , Sutan Mohammad ARIF, SUTAN MOHAMMAD Avrizal, Reza Ayu, Kurnia Gusti Aziz, Topaz Malik Baihaqi, Kiki Ahmad Bambang Jokonowo Bambang Sukowo Bernadetha, Suzan Da Costa Martins, Ana Maria Ildiberta Darmawan, Bagus Budi Devi Fitrianah Diah Utami, Diah Elga Ramdani Eliyani Eliyani Eliyani Fadli Nurrizky Fajar Antono Fatimah, Nona Fatonah, Nenden Siti Fauzi Ahmad Muda Halim, Alfian Noer Hanny Hikmayanti Handayani Haris Yusuf Bakhtiar Herfandi Herfandi Herry Derajad Wijaya Herry Derajad Wijaya Hidayat, Rahmat Rian Imawan, Sonny Aditya Jumaryadi, Yuwan Lokesworo M. Fajrun Nadhif Muhammad Rifqi muhammad rizky, muhammad Nia Kusuma Wardhani Nicholas Reinaldo, Ryo Nur, Nur Umar Azaliy Awal Nurfiant, Muhammad Arya Hafsah Nurrizky, Fadli Pertiwi, Anggun Popy Yuliarty, Popy Prihandani, Kamal Prihandi, Ifan Qitala, Shofia Reza Avrizal Rifqi Muhammad Riri Fajriah Rudiarto, Sabar Saputra, Dony Ramadhan Sarima Lumbansiantar Shofia Qitala Sukarna, Royan Habibie Sukma Wardhana Sumarto, Marco Alfan Surya Perdana Triwibowo, Ardy Wardhana, Ariyani Wardhani, Elisa Wawan Gunawan Wawan Gunawan Wijayanti, Atiek Ike Yudo Devianto Yuliarty, Poppy