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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Research Report - Engineering Science Majalah Ilmiah Ekonomi Komputer Jurnal Rekayasa Sistem Industri Jurnal Kesehatan Masyarakat Jurnal Ilmiah Teknik Industri Jurnal Teknik Sipil Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG)

F. Rian Pratikto

Jurusan Teknik Industri, Fakultas Teknologi Industri Universitas Katolik Parahyangan, Bandung

Author-ID : 346113

Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Public Health Other

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Preference-Based Revenue Optimization for App-Based Lifestyle Membership Plans Fransiscus Rian Pratikto; Gerardus Daniel Julianto; Sani Susanto
Jurnal Ilmiah Teknik Industri Vol. 20, No. 1, June 2021
Publisher : Department of Industrial Engineering Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v20i1.13312

The demand for a product is rooted in the consumers’ needs and preferences. Therefore, a pricing optimization model will be more valid if the demand function is represented under this basic notion. A preference-based revenue optimization model for an app-based lifestyle membership program is developed and solved in this research. The model considers competitor products and cannibalization effect from products in other fare-class, where both are incorporated using a preference-based demand function. The demand function was derived through a randomized first choice simulation that converts individual utility values into personal choices based on the random parameter logit model. Cannibalizing products are considered as competing products in the simulation scenario. In the pricing optimization, two and three fare classes based on the membership period are considered. The corresponding pricing optimization problem is a mixed-integer nonlinear programming problem with a solution-dependent objective function. Using enumeration, the three-fare-class optimal prices of Rp420,000, Rp300,000, and Rp60,000 for 12-month, 6-month, and 1-month membership, respectively, are better than those of the two-fare-class. Under this policy, the estimated total revenue is Rp30.56 billion, 41.74% greater than that of the current condition.

Optimasi Tarif Kereta Bandara Soekarno-Hatta dengan Model Permintaan Berbasis Discrete Choice Experiment Fransiscus Rian Pratikto; Mathew Zephaniah Samtani
Jurnal Teknik Sipil Vol 28 No 1 (2021): Jurnal Teknik Sipil
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/jts.2021.28.1.9

Abstrak Penelitian ini bertujuan menentukan tarif optimal Kereta Bandara Soekarno Hatta dengan fungsi permintaan yang diturunkan dengan pendekatan discrete-choice experiment. Fungsi permintaan diperoleh dengan memprediksi pilihan setiap individu pada beberapa tingkat harga yang berbeda, mengagregasikannya, dan kemudian menginterpolasikannya sehingga diperoleh fungsi yang kontinyu dan differentiable. Pilihan setiap individu diprediksi dari data utilitas individual menggunakan simulasi randomized first choice, sementara interpolasi fungsi permintaan dilakukan menggunakan cubic spline. Utilitas individual diestimasi dari data stated-preference berbentuk choice menggunakan pendekatan Bayesian. Dengan membatasi dua kelas tarif, harga ditentukan dengan mempertimbangkan kanibalisasi antar kelas tarif dan memperhatikan profitabilitas jangka panjang. Formulasi masalah optimasi yang diperoleh berbentuk nonlinear integer programming dengan fungsi tujuan polinomial orde empat yang parameternya dipengaruhi oleh nilai variabel keputusan. Ruang solusi yang tidak terlalu luas memungkinkan untuk memperoleh solusi dengan enumerasi, di mana diperoleh tarif optimal sebesar Rp70.000 untuk kelas tarif 1 di mana layanan Kereta Bandara dibundle dengan diskon angkutan taksi berbasis aplikasi, dan Rp67.000 untuk kelas tarif 2 yang berupa layanan Kereta Bandara saja. Dengan tarif tersebut diperkirakan akan diperoleh kontribusi total sebesar Rp375,27 milyar per tahun. Kata-kata Kunci: Bayesian, kanicalisasi, discrete-choice experiment, randomized first choice Abstract This research aims to determine optimal prices for the Soekarno-Hatta Airport Shuttle Train services in which the demand function is derived using the discrete-choice experiment approach. The demand function is obtained by predicting and aggregating individual choices at several price levels, followed by interpolating the results to obtain a continuous and differentiable function. Individual choices are predicted from individual utility data using the randomized first choice simulation, while the interpolation is conducted using cubic splines. Inidividual utilities are estimated choice stated-preference data using Bayesian approach. By assuming two fare-classes, optimal prices are determined by considering cannibalization between fare-classes and operator’s long-term profitability. The resulted optimization formulation is a nonlinear integer programming problem with quartic polynomial objective function whose coefficients depend on the value of the decision variables. Since the solution space is relatively small, optimal prices can be obtained using enumeration. The optimal prices are Rp70,000 for fare-class 1 where the sevice is bundled with a discount on the app-based taxi service, and Rp67,000 for fare-class 2 which provides shuttle train only. The annual total contribution from such pricing policy is estimated to be Rp375.27 billion. Keywords: Bayesian, cannibalization, discrete-choice experiment, randomized first choice.

Uncapacitated Pricing Optimization for Mobile Broadband Services Fransiscus Rian Pratikto
Jurnal Teknik Industri Vol. 20 No. 1 (2018): June 2018
Publisher : Institute of Research and Community Outreach - Petra Christian University

The success of revenue management starting in the mid-1980s has been driving pricing decision to be more tactical and operational. Since then, statistics and operations research have been important tools in pricing and revenue optimization. This research seeks to determine optimal price for mobile broadband services of a particular service provider. The case study is mobile broadband services in Indonesian market. We made a plausible assumption that there is no capacity constraint. We used choice-based conjoint with hierarchical Bayes estimation method to derive individual part-worth utilities, based on which market simulation was run to obtain the price-response function. By combining this with information about market size, we came up with a number of data points representing the demand function. Instead of fitting the data points with some theoretical demand functions, we used monotonic cubic splines to interpolate the demand function. Accordingly, we did not use explicit demand functions in the optimization, but a numerical interpolation function to estimate demand for any particular price level. Using enumeration, we then came up with a recommended contribution-maximizing prices under one, two, and three fare-classes segmentation. We assumed a perfect segmentation where cannibalization and arbitrage were not present. Further, we discussed a generalized optimal segmentation problem under that assumption. We also investigated the impact of the changes in competitors’ service attributes on the optimal prices.

Incorporating Cannibalization into Pricing Optimization Using Choice Data: An Application to the Pricing of Mobile Broadband Services Fransiscus Rian Pratikto
Jurnal Teknik Industri Vol. 21 No. 2 (2019): December 2019
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.21.2.57-68

Price differentiation may not be as effective in increasing profitability due to imperfect segmentation, arbitrage, and, cannibalization. Cannibalization takes place when customer with higher willingness-to-pay buys lower-priced product. This research proposes an approach to incorporating cannibalization into pricing optimization using choice data. From choice data, individual level utilities are estimated using hierarchical Bayes and individual choice is predicted using randomized first choice simulation. Individual choices are then aggregated to obtain the demand function. The novelty of this research is in the way cannibalization is incorporated into the pricing optimization. Instead of integrating cannibalization into the demand function or representing it as a separate component in the optimization formulation, in this research, cannibalizing products are incorporated into the simulation scenario as competing products, based on which the demand functions used in the optimization are derived. This approach is more direct and realistic than those in the previous research. The approach was implemented in a case study of mobile broadband services in Indonesian price-sensitive market. The result shows that two-fare-class price differentiation incoporated with product differentiation increases total contribution of about 60% compared to single-fare-class policy. Furthermore, it is also shown from our case study that starting from a three-fare-class policy, through iterations, our approach suggests that policy with two-fare-class results in a not significantly different total contribution.

Kesediaan Membayar Terhadap Layanan Battery Tram Koridor Kuta, Bali Fransiscus Rian Pratikto
Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Vol 10, No 1 (2023): Maret
Publisher : Institut Transportasi dan Logistik Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54324/j.mtl.v10i1.1061

The Government of Bali is planning to develop a battery tram mass rapid transport in the Kuta area as an effort to resolve severe congestion problem in that area. This research aims to measure consumers’ willingness to pay for the battery tram services in the Kuta corridor. The willingness to pay is measured using a survey based stated preference technique, the Contingent Valuation Method, with the Dichotomous Choice with Follow Up elicitation technique. An electronic survey targeting local residences and tourists that are considered potential users managed to collect 635 data. The survival analysis statistical technique is used to measure the willingness to pay assuming a lognormal distribution. The Survival Package from R software is used to produce the price-response function. The result shows that 50% of local residents are willing to pay up to Rp978/km, while 50% of tourists are willing to pay up to Rp4,402/km for the battery tram services. In general, the price-response curve shows that tourists are willing to pay Rp3,000-Rp5,000/km higher than local residents, most of the respondents that are reluctant to use the battery tram consider the offered prices in the CVM questionnaire too high or tend to avoid using public transport for health reason following the Covid-19 pandemic.

Oversampling Sintetis Berbasis Kopula untuk Model Klasifikasi dengan Data yang Tidak Seimbang Fransiscus Rian Pratikto
Jurnal Rekayasa Sistem Industri Vol. 12 No. 1 (2023): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

A machine learning classification model for detecting abnormality is usually developed using imbalanced data where the number of abnormal instances is significantly smaller than the normal ones. Since the data is imbalanced, the learning process is dominated by normal instances, and the resulting model may be biased. The most common method for coping with this problem is synthetic oversampling. Most synthetic oversampling techniques are distance-based, usually based on the k-Nearest Neighbor method. Patterns in data can be based on distance or correlation. This research proposes a synthetic oversampling technique that is based on correlations in the form of the joint probability distribution of the data. The joint probability distribution is represented using a Gaussian copula, while the marginal distribution uses three alternatives distribution: the Pearson distribution system, empirical distribution, and the Metalog distribution system. This proposed technique is compared with several commonly used synthetic oversampling techniques in a case study of credit card default prediction. The classification model uses the k-Nearest Neighbor and is validated using the k-fold cross-validation. We found that the classification model using the proposed oversampling method with the Metalog marginal distribution has the greatest total accuracy.

Co-Authors Gede Karya Gerardus Daniel Julianto Gerry T. Loren Pratiwi Mathew Zephaniah Samtani Paulus Sukapto Sani Susanto Santi Erawaty Yani Herawati Yogi Yusuf W. Yogi Yusuf Wibisono

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