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Author: search.filters.author.Ritthipakdee, Amarita

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    ENHANCING PLATFORM CREDIBILITY IN THAI E-COMMERCE: RECALIBRATING INFLATED STAR RATINGS VIA SENTIMENT ANALYSIS
    (2026-01-01)
    Leenawong, Chartchai
    ;
    Thangthawornkit, Warunada
    ;
    Ritthipakdee, Amarita
    This study addresses the erosion of platform credibility in international e-commerce caused by the misalignment between numerical star ratings and actual textual sentiment. While star ratings are a dominant mechanism guiding purchasing decisions in rapidly expanding markets like Southeast Asia, they are frequently inflated due to platform-induced biases and region-specific cultural factors, such as the politeness norms and indirect communication strategies prevalent in Thai culture. To overcome this structural limitation, this research aims to empirically quantify this discrepancy and introduce a transparent, sentiment-informed metric—the "Heart Score"—to recalibrate ratings. A rule-based sentiment analysis framework specifically adapted for Thai-language reviews was applied to a dataset of 14,877 women’s clothing entries from Lazada. By leveraging expert-validated lexicons and linguistic rules for negation and amplification, store-level sentiment ratios were extracted and transformed into recalibrated scores via a normalized linear function with penalty adjustments. Findings confirm substantial sentiment misalignment: although the analyzed stores clustered narrowly with star ratings between 4.92 and 5.00, the recalibrated Heart Scores spanned a significantly wider, more meaningful range of 3.81 to 4.71. The most notable gap of 1.11 points exposed hidden dissatisfaction masked by near-perfect ratings. The proposed framework offers strong managerial insights for platform governance by enhancing review credibility, enabling more accurate product benchmarking, and providing a scalable, interpretable model for restoring trust in low-resource language environments.
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    Fuel Costs Optimization for Long-Haul Flight with Refueling Layovers
    (2024-01-01)
    Leenawong, Chartchai
    ;
    Ritthipakdee, Amarita
    This study introduced a mathematical model aimed at optimizing fuel costs for long-haul flights, particularly those requiring refueling. The primary objective was to minimize fuel expenses by considering key factors such as flight routes, aircraft types, refueling points, and refueling quantities. The proposed solution used a 0-1 mixed-integer linear programming (MILP) model, supported by auxiliary variables, to effectively manage the constraints of this optimization problem. The MILP model also considered differences in fuel costs at refueling points, including the departure airport. For validation, a case study was conducted involving a long-haul flight from airport AAA to DDD, with refueling options at airports BBB and CCC. The model effectively determined the most economical flight route, assessed the necessity of refueling, and calculated the required fuel amounts at each refueling location. In summary, this study demonstrated that the proposed model could successfully address the challenges of optimizing fuel costs in long-haul flight.
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    Primate swarm algorithm for continuous optimization problems
    (2017-08-29)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the primate. The communication among them is studied and is also a key part in their food finding strategy. Our proposed algorithm developed upon the behavior is tested with twelve standard benchmark functions and most of which converged to the optimal value.
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    Firefly Mating Algorithm for Continuous Optimization Problems
    (2017-01-01)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    ;
    Premasathian, Nol
    ;
    Jitkongchuen, Duangjai
    This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
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    A new selection operator to improve the performance of genetic algorithm for optimization problems
    (2013-11-25)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    ;
    Premasathian, Nol
    ;
    Uyyanonvara, Bunyarit
    Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.