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All Journal Paradigma JISICOM (Journal of Information System, Infomatics and Computing)
Dwi Andriyanto
Universitas Nusa Mandiri
Computer Science & IT

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Penerapan Finite State Automata Pada Mesin Tiket Otomatis Bus Damri Di Bandara Internasional Yogyakarta Khabib Astoni; Faruq Aziz; Fadillah Said; Dwi Andriyanto; Windu Gata
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 (398.202 KB) | DOI: 10.31294/p.v23i2.11290

Abstract

Intisari—Sebagai sarana transportasi udara yang vital, Bandara Internasional Yogyakarta membutuhkan sarana alih moda transportasi darat untuk memudahkan penggunanya. Salah satunya adalah Perum Damri yang menyediakan airport shuttle yang melayani ke berbagai tujuan dengan sistem penjualan tiket yang masih dilakukan secara manual. Dalam penelitian ini, didesain mesin penjualan tiket otomatis berbagai jurusan dengan prinsip kerja menyerupai vending machine yang mengimplementasikan Finite State Automata. Mesin ini menerima pembayaran tunai dan non tunai dan mengeluarkan cetak tiket dan uang kembalian. Kesimpulan dari penelitian ini, Finite State Automata dapat dijadikan logika dasar untuk merancang desain mesin tiket otomatis serupa vending machine yang dapat menggantikan peran manusia dan meningkatkan efisiensi.
Optimization Of Recurrent Neural Network In Indonesia Stock Exchange Price Prediction Modeling Dwi Andriyanto; Yan Rianto
Journal of Information System, Informatics and Computing Vol 6 No 1 (2022): JISICOM: June 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i1.744

Abstract

One of the online trades is stock trading on the stock exchange. To increase the number of investors, the government invites the public to invest in the capital market by buying shares regularly and periodically in the form of shares. With the increase in potential investors in the stock market, deep knowledge about stock trading is needed so that the returns are as desired. This research can help to make it easier for the public to predict the desired stock price through machine learning technology even though they do not have more in-depth technical knowledge about stocks. Three types of algorithms RNN, LSTM, GRU are used to find the best method by optimizing parameters so as to get an r-square error of 0.96 through the number of epochs of 100 and a learning rate of 0.01.
Co-Authors Fadillah Said Faruq Aziz Khabib Astoni Windu Gata Yan Rianto