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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Gelanggang Olahraga: Jurnal Pendidikan Jasmani dan Olahraga (JPJO)
Leslie Anggraini
Institut Teknologi Sumatera
Computer Science & IT Education

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Klasifikasi Kelayakan Sepeda Lomba Triathlon Menggunakan Arsitektur You Look Only Once (Yolov11) Berbasis Data Citra Sepeda Azry Ayu Nabillah; Yudha Hamdi Arzi; Radhinka Bagaskara; Leslie Anggraini; Sikah Nubuahtul Ilmi; Ahmad Zain Mahmud; Mohammad Hisyam Alif Setiawan; Dito Aditya Sasongko; Rian Kosasih
Gelanggang Olahraga: Jurnal Pendidikan Jasmani dan Olahraga (JPJO) Vol. 9 No. 3 (2026): Gelanggang Olahraga: Jurnal Pendidikan Jasmani dan Olahraga
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/s0n1h558

Abstract

The study established a triathlon bicycle eligibility classification framework based on YoloV11 and YoloV11-OBB, informed by the World Triathlon Competition Regulations 2025, as a solution for the ineffectiveness and subjectivity inherent in manual assessment. The methodology used is Research and Development (R&D), which includes training of deep learning algorithms using 427 side view images and 100 top view images, in addition to evaluating the accuracy of geometric measurements across five different types of bicycles. Findings showed that the model achieved 100% visual classification accuracy as determined by the Confusion Matrix and was able to produce measurements of macro dimensions and component placement with an average margin of error of 1.5-3.3 cm. However, handlebar angle measurements remain prone to perspective distortion, resulting in an average error of 5.21°. In conclusion, the system deserves to be considered as an effective initial screening instrument in the bicycle inspection process, although with the caveat that manual validation is still necessary in cases approaching the regulatory threshold. Keywords: Computer Vision, Oriented Bounding Box, Bicycle Regulations, Triathlon Bike Check, YOLOv11
Comparison of Holt’s Exponential Smoothing and Weighted Moving Average Methods in Predicting the Proportion of Alma Mater Sizes Eko Dwi Nugroho; Miranti Verdiana; Leslie Anggraini; Radhinka Bagaskara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12645

Abstract

The annual admission of new students requires the early procurement of university jackets to ensure distribution during the inauguration ceremony. However, the lengthy production lead time necessitates ordering months before the actual sizing data is fully collected. This issue is further complicated by low participation rates in size registration and extreme population spikes. This study proposes a time series forecasting approach to predict the proportional distribution of jacket sizes rather than absolute quantities. Specifically, the research compares the performance of Holt’s Exponential Smoothing and the Weighted Moving Average (WMA) method using historical size proportions from 2019 to 2025. Walk-forward validation was employed to evaluate the models based on the Mean Absolute Percentage Error (MAPE). The results demonstrate that WMA outperforms Holt’s Exponential Smoothing by achieving a lower MAPE of 6.56%. By extrapolating the WMA proportions to the 2026 target of 5,250 students and mathematically integrating the 6.56% error rate as a safety stock buffer, the final procurement quantities for sizes S through XXXL were precisely determined. This proportional forecasting framework provides a robust, quantitative foundation for institutional supply chain management, allowing early and accurate ordering despite incomplete preliminary data.
Forecasting the Demand for Freshmen Alma Mater Jackets and Sports T-Shirts by Size Using a Hybrid Bayesian–Machine Learning Approach Miranti Verdiana; Eko Dwi Nugroho; Leslie Anggraini; Radhinka Bagaskara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12679

Abstract

This study addresses an operational procurement problem in university admissions, where alma mater jackets and sports T-shirts for incoming students must be ordered several months before complete size information becomes available. In the case of ITERA admissions, procurement decisions are typically made in March or April, whereas actual student size data are only gradually collected during the re-registration period from April to July. To support earlier and more reliable procurement planning, this study formulates the problem as a size-demand forecasting task covering six categories: S, M, L, XL, XXL, and XXXL. Historical data from 2015 to 2025 were analyzed, with reliable size records concentrated in the 2019–2025 period. The main novelty of this study lies in formulating freshman uniform procurement as a staged forecasting problem that follows the actual admissions workflow. Specifically, the study proposes a hybrid framework that combines: (i) a time-weighted Bayesian Dirichlet–Multinomial model for early-stage aggregate forecasting when current-year size data are not yet available, and (ii) a CatBoostClassifier-based multiclass machine learning model for prediction updates when student attributes become available. Model performance was evaluated using an expanding-window rolling/forward chaining scheme with a one-year forecasting horizon. In addition to conventional historical baselines, the study also included Simple Exponential Smoothing (SES) as a time-series benchmark. Performance was assessed using cross-entropy for size-distribution accuracy, MAE/size and WAPE for quantity prediction, and stockout/overstock for operational impact. The results show that the previous-year proportion remains a strong baseline, while the Bayesian model provides competitive performance and yields posterior uncertainty estimates that are useful for determining safety-oriented order quantities. The statistical analysis further confirms that gender is the most influential predictor of size, while study program, admission track, and province provide complementary but weaker signals. The findings indicate that the proposed framework can support more adaptive and evidence-based procurement planning, reduce the risk of size shortages and excess inventory, and provide a transferable forecasting workflow that may be adapted to other institutions after local recalibration.
Co-Authors Ahmad Zain Mahmud Azry Ayu Nabillah Dito Aditya Sasongko Eko Dwi Nugroho Miranti Verdiana Mohammad Hisyam Alif Setiawan Radhinka Bagaskara Rian Kosasih Sikah Nubuahtul Ilmi Yudha Hamdi Arzi