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All Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer)
Rosadi Rosadi
Universitas Nusa Mandiri
Computer Science & IT

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DESAIN VENDING MACHINE PENYEWAAN KAMAR HOTEL DENGAN MENGIMPLEMENTASIKAN KONSEP FINITE STATE AUTOMATA Rosadi Rosadi; Yessica Fara Desvia; Lael Kurniawati; Sri Rahayu; Windu Gata
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 4 No 2 (2021)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v4i2.264

Abstract

The hotel industry will compete in developing their business, one of which is by applying information technology. In addition to designing an information system for business development, a vending machine can also be designed using the concept of finite state automata (FSA). FSA is a computational model with limited memory that has the function of a digital computer, which can accept input, output, temporary storage and is able to make decisions in defining inputs into outputs. The VM penyewaan kamar hotel design will make a new solution for tourists who do not book rooms online, can choose rooms independently through VM and using two payment methods, that is cash and non-cash method payment. The design stage begins with analyzing business processes at a hotel, describing the machine configuration with state diagrams and then designing the VM user interface. So that an output from the machine can be generated in the form of a room key which will then be used to enter the selected room.
APPLICATION OF CLASSIFICATION ALGORITHM FOR SALES PREDICTION Sendi Permana; Rosadi Rosadi; Nikki Nikki
TEKNOKOM Vol. 5 No. 2 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (920.249 KB) | DOI: 10.31943/teknokom.v5i2.77

Abstract

Increasing sales results is a desired target for all companies both at home and abroad. The company has a wide variety of products to offer. This paper (to fulfill a Business Intelligence course assignment) is the result of an experiment from data (keaggle) about consumer demand for products during the 2013-2015 period, then based on this data we try to predict to classify product sales, in order to make it easier for companies to classification for sales predictions. To find out the sales of the best-selling products, data mining classification techniques are used, namely XGBoost, Decision Tree, Random Forest, Linear Regression, and Nave Bayes. Based on the test results of the five classification techniques, the XGBoost model is the best with the data training value producing an RMSE value of 0.68% and data testing of 0.79%. This method is also better than the results of previous studies.
Co-Authors Lael Kurniawati Nikki Nikki Sendi Permana Sri Rahayu Windu Gata Yessica Fara Desvia