Vittayakorn, Sirion

Preferred name

Vittayakorn, Sirion

Main Affiliation

School of Information Technology

sirion.vi@kmitl.ac.th

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Pig Carcass Assessment on Image Segmentation
(2021-01-01)
Tanthong, Jitpanu
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Moodleah, Samart
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Vittayakorn, Sirion
Pork is the most commonly consumed meat across the world: about one-third of all meat consumed is pork, ahead of beef and chicken. Every day a massive number of pig carcasses enter the production pipeline. When entering the pipeline, the carcasses are graded by the slaughterhouses to determine the market price of the meat. The grading criteria could depend on a variety of factors such as the tenderness, color, pH value, water holding capacity as well as the proportion of red meat inside the carcasses. Since the grade can be used to determine the market price and the commercial usage of the meat, this process is crucial. Unfortunately, the grading process is not only time consuming but also requires expertise. To mitigate this problem, in this work we propose: 1) a pig carcass image dataset segmented by experts, 2) an LSQ index image dataset and 3) an algorithm for carcass quality analysis based on the ratio of red meat inside the carcasses, or the Lenden-Speck-Quotient (LSQ). Our experimental results demonstrate that the performance of the proposed LSQ index algorithm is reliable and agrees with experts' annotation with MAPE 5.55%.
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Context-Aware Prompting for Japanese-Thai Literary Translation in a Low-Resource Setting
(2026-01-01)
Sikkhamarn, Korawit
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Vittayakorn, Sirion
Japanese-Thai literary translation remains underexplored, particularly in low-resource settings where parallel data and evaluation resources are limited. This paper investigates whether context-aware prompting can improve Japanese-Thai literary translation using large language models. To support this study, we construct JTLitCorpus-80, a Japanese-Thai literary parallel corpus derived from 80 publicly accessible online translated web novels. Using this corpus, we compare eight prompt-based translation settings, including machine translation, post-editing, and context-aware prompting variants with and without dictionary guidance. Results on a 20-title test split show that direct context-aware prompting consistently outperforms machine translation and non-context-aware baselines, while post-editing provides only limited gains once the initial draft is already generated by the same model. We further show that prompt-derived supervision can improve a smaller open model through parameter-efficient adaptation. MQM-based human evaluation supports the automatic-metric trends, indicating that the strongest context-aware setting provides the best overall translation quality. These findings suggest that context-aware prompting is a practical strategy for Japanese-Thai literary translation in low-resource conditions.
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SEPBO: Trash separator bot VR game
(2021-06-30)
Theethum, Thirada
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Iamcharoen, Sopoat
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Arpornrat, Attawut
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Vittayakorn, Sirion
Everyday hundreds of thousands of tons of waste goes to landfills, although more than half of it could be either recycled or composted. The waste management situation tends to get worse when there are many poorly-designed landfill sites that could either leak hazardous chemicals which can contaminate groundwater or emit harmful gases into the atmosphere. These health hazards or environmental chain problems could be diminished if everyone sorted their waste precisely. To mitigate this problems, we aim to tackle it from the beginning. Thus, in this work, we propose a computer-based VR game called SEPBO. SEPBO is an educational game which aims not only for enjoyment, but also to improve the players' waste sorting skills. The experimental results confirm that SEPBO is a better tool to improve the players' waste sorting skill compared to the interactive web-based baseline, ReCollect: The Waste Sorting Game, in ALL aspects: with 6.67% higher performance in improving players' waste sorting skill and a 48% greater degree of satisfaction than the baseline in terms of enjoyment.
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Cyberbullying detection on Tweets
(2021-05-19)
Phanomtip, Aekachai
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Sueb-In, Thaiyathorn
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Vittayakorn, Sirion
Nowadays the young generation spends a median of 3 hours a day on social media sites such as Twitter, Facebook, Instagram, Snapchat or TikTok. As a consequence, these platforms have gradually become part of their daily life as well as an influence on their attitude and behaviour. Although the social media platforms were originally designed to share information/news or connect families and friends, they also spread fake news, conspiracy theories, hate speech and cyberbullying. Cyberbullying is bullying with the use of digital technologies which is repeated, aimed at scaring, angering or shaming those who are targeted. Feeling vulnerable, powerless, humiliated, isolated, depressed or suicidal are examples of the negative effects from cyberbullying. Unfortunately, study has shown that one-third of the students have experienced cyberbullying in their lifetime. To mitigate this problem, we propose 1) a novel dataset of 67K tweets collected from Twitter, 2) automatic annotation methods for a large-scale dataset and 3) a detection system that to identify these toxic behaviours. The experimental results demonstrate that our method outperforms the baseline by 4%.
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Incomplete Adventure: An Educational Game for the TOEIC Exam
(2023-01-01)
Tedsakorn, Surawee
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Aksaralikitsanti, Nattapong
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Vittayakorn, Sirion
These days, the Test of English for International Communication (TOEIC) plays an essential role in both the work environment and everyday life. In daily life, fluency in English offers tremendous benefits including exposure to new experiences, opportunities, cultures, and friends. In the work environment, it is an crucial factor in client relationships, prof-itability, team effectiveness, and employee engagement. Although TOEIC preparation sources can be seen everywhere, most of them aren't very attractive or motivating. As a result, it is very hard for test takers to fully focus on exam practice for a long period of time. Unfortunately, the lack of attractive and interesting of the traditional methods e.g, book or web-based exam, negatively effect the test takers' learning performance. To mitigate this problem, we propose a game-based application for TOEIC preparation called Incomplete Adventure. In game-play, player needs to answer a series of TOEIC questions to defeat the enemy without dying. Even though the experimental results demonstrate that Incomplete Adventure is comparable with traditional learning methods in improving English skill, the game receives a better degree of satisfaction than the web-based exam by 7.6%. Moreover, the overall performance score of our game also outperforms other TOEIC games by 2.68%. We posit that the enjoyment from Incomplete Adventure will enhance the motivation of learning among students and finally effect the students' perception of learning in the end.
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Thinkercise: An educational VR game for Python programming
(2021-05-19)
Theethum, Thirada
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Arpornrat, Attawut
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Vittayakorn, Sirion
Thinkercise was developed to be an educational VR game for reviewing knowledge and skills in Python programming. The game not only encourages players to move their physical body during the gameplay but also prompts players to use their basic Python programming knowledge and skill to complete a task in the game. In the gameplay, players are required to answer a series of programming related questions by moving their body in various ways in a limited time, such as punching a target, or avoiding an obstacle. To achieve the maximum score, players need to hit the correct answer targets at the right time as well as to avoid all obstacles in the game successfully. Experimental results demonstrate that our game helps increase the students' performance in computer programming by 40.8%. It outperformed self-learning by 30%.
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Location-Based Score Prediction for Condominiums in Bangkok
(2023-01-01)
Bunjongsat, Sarun
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Vittayakorn, Sirion
Condominiums are one of the most popular residential properties due to the resurgence of urban living. They are typically found in more metropolitan areas, such as the national capital region, offering easy access to restaurants, shopping, and various activities. Condominiums can be both profitable investment properties and enjoyable homes. However, finding the right one can be challenging. Based on previous work, one crucial factor is the location, which requires experience, expertise, and time to consider. Thus, in this study, we aim to: 1) investigate the factors that affect the potential location of a condominium, and 2) apply machine learning algorithms to create a prediction model for condominium scores. In this research, we collected a novel dataset comprising more than 2,000 condominiums in Bangkok, Thailand, and examined the location-based factors that influence the price and purchasing decisions of individuals. We extracted several features for model training and studied the variables that could be used to measure the potential of a condominium in numerical form. We proposed a multi-layer perceptron neural network with 5-Fold cross-validation and grid-search techniques. The experimental results demonstrate that our network achieves a mean squared error (MSE) of 0.0067 when using the density-based score labeling.
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Protobot: An Educational Game for Algorithmic Thinking
(2022-01-01)
Lertlapnon, Thanachote
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Lueangrungudom, Naruebes
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Vittayakorn, Sirion
Algorithm has been heavily used to provide a comfort life for us: from finding the best route in Google map to biometrics authentication on your phone. The significant of algorithm defines Computational thinking (CT) as one of the 21st Century skills. Although CT has been integrated into the education in the past years, many students still struggle with CT concept due to the complication of the topic and limited learning methods provided in school environment. To provide alternative learning approach for algorithmic thinking, we propose a game-base learning system called Protobot. Protobot requires students to apply their CT knowledge, especially the algorithmic thinking to solve problems in the gameplay. The experimental results demonstrate that Protobot fosters the algorithmic thinking skill of the players as well as provides the amusement during the gameplay.
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Bugtopia: Game-Based Learning for Venomous Insect Symptom Recognition
(2026-01-01)
Wisescharoen, Supitcha
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Thongkwanjai, Sirimongkol
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Vittayakorn, Sirion
This paper presents Bugtopia, a game-based learning prototype developed to support recognition of symptoms associated with venomous insects and related arthropods. The study aimed to compare the game-based approach with a text-based learning method in terms of immediate learning, retention, and user experience. A between-group experiment with 30 participants employed pre-test, immediate post-test, and delayed post-test measures. Learning outcomes were analyzed using both all-or-nothing and partial-credit scoring. Results showed that both methods improved performance from pre-test to immediate post-test, although the text-based method yielded larger immediate gains. In contrast, delayed post-test results showed stronger retention for the game-based method, which maintained more stable performance over two weeks and achieved higher delayed scores. Subjective results further showed positive user experience for Bugtopia and more favorable ratings for the game-based method on understanding, memory support, enjoyment, and future reuse. Overall, the findings suggest that Bugtopia is a promising complementary learning tool, particularly for promoting retention and engagement.
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Electronic map systems for tourist attractions
(2020-03-01)
Manitchalermchai, Thitiphat
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Chotsawasraksa, Rujapa
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Vittayakorn, Sirion
Traditional navigation systems such as paper maps or directional signs are impractical in many ways. For example, they are unable to identify the user's location or continuously navigate the user to their destination. As a result, many users are lost even though they have a map in their hand. To mitigate this problem, this work proposes a navigation system which includes 1) a web-based system that is used to maintain (e.g., insert new places, update place information or delete places) the attractions in the map and 2) an electronic map application for mobile devices which provides many functionalities. For example, 1) a navigation function which directs the users to their desired places correctly and rapidly, 2) a nearby place search function which provides information about nearby places to help users plan their next stops and 3) route recommendation which provides a route to a series of attractions. The evaluations demonstrate that the proposed system outperforms baselines in many aspects.

Vittayakorn, Sirion

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