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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal Perilaku dan Strategi Bisnis
Meditya Wasesa
School of Business and Management, Institut Teknologi Bandung
Computer Science & IT Economics, Econometrics & Finance

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Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers Mochammad Agus Afrianto; Meditya Wasesa
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.123-132

Abstract

Background: Literature in the peer-to-peer accommodation has put a substantial focus on accommodation listings' price determinants. Developing prediction models related to the demand for accommodation listings is vital in revenue management because accurate price and demand forecasts will help determine the best revenue management responses.Objective: This study aims to develop prediction models to determine the booking likelihood of accommodation listings.Methods: Using an Airbnb dataset, we developed four machine learning models, namely Logistics Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest Classifiers. We assessed the models using the AUC-ROC score and the model development time by using the ten-fold three-way split and the ten-fold cross-validation procedures.Results: In terms of average AUC-ROC score, the Random Forest Classifiers outperformed other evaluated models. In three-ways split procedure, it had a 15.03% higher AUC-ROC score than Decision Tree, 2.93 % higher than KNN, and 2.38% higher than Logistics Regression. In the cross-validation procedure, it has a 26,99% higher AUC-ROC score than Decision Tree, 4.41 % higher than KNN, and 3.31% higher than Logistics Regression.  It should be noted that the Decision Tree model has the lowest AUC-ROC score, but it has the smallest model development time.Conclusion: The performance of random forest models in predicting booking likelihood of accommodation listings is the most superior. The model can be used by peer-to-peer accommodation owners to improve their revenue management responses. 
Smart approach to determine solution of coal quality discrepancy in coal-fired power plant project under binding project finance scheme (case study: PT SBT) Matiinu Iman Ramadhan; Meditya Wasesa
Jurnal Perilaku dan Strategi Bisnis Vol 10, No 1: Februari 2022
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jpsb.v10i1.2170

Abstract

PT Semangat Berjuang Terus (“PT SBT”) as the developer of the coal-fired power plant with capacity 2 x 950 MW (“the Project”) under project finance scheme was experiencing an issue with the coal suppliers that has been engaged under the Coal Supply Agreement (“CSA”) where coal suppliers could not provide conforming coal in accordance with the specification in CSA. This condition caused the coal suppliers is entering an event of default and it cause PT SBT went into event of default under agreement with financial lenders that potentially affect the eligibility to obtain loan to complete the Project construction. This paper is intended to find the root cause, provide alternative solutions, and finally a recommendation towards the problem that caused by coal quality non-conformity. Using Why-Tree and KT Problem Analysis, the Author identify the root cause of coal quality non-conformity issue is the CSA terms is not applicable to accommodate the practice of coal supply arrangement for power plant project. Based on the Simple Multi Attribute Rating Technique (SMART) analysis method involving key persons from the PT SBT which comes from technical, finance, legal, procurement, and operation side, requesting Lenders waiver to utilize the available coal that acceptable by Contractor, despite the event of default under CSA, is the best option to maintain the eligibility to obtain loan and complete the construction. Furthermore, PT SBT shall amend the CSA in order to mitigate the event of default which occurred because the coal quality will never meet the contract requirement if it remains\
Co-Authors Matiinu Iman Ramadhan Mochammad Agus Afrianto