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All Journal Journal of Educational Sciences Sriwijaya Journal of Sport
Moh. Hasbi Assidiqi, Moh. Hasbi
Department of Multimedia Creative Electronic Engineering Polytechnic Institute of Surabaya
Religion Humanities Education Library & Information Science Public Health Other

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Developing Dataset Management of Speech Recognition Based Automatic English-Speaking Skill Testing System Muhammad, Aliv Faizal; Assidiqi, Moh. Hasbi; Sa’dyah, Halimatus
Journal of Educational Sciences Vol 7. No. 2. April 2023
Publisher : FKIP-Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jes.7.2.p.296-305

Abstract

Speech recognition technology has been widely used to aid English language testing, such as for pronunciation and speaking practice for various use cases including job interview simulation. We developed an app, powered by speech recognition technology and regex text processing, that automatically score participants’ speaking performance. However, this application lacks dataset management for the speaking test maker to manage the indicators of accepted answers. A feature to effectively manage the dataset and efficiently ease the speaking test creation is needed. This paper presents the development of dataset management feature to support the speech recognition based automatic English-speaking skill testing system. All elements that support the feature in development were tested through Blackbox testing with various scenarios. The elements were proven to work as intended to support the existing system. The new feature has brought efficiency for the English-speaking test maker.
Developing Dataset Management of Speech Recognition Based Automatic English-Speaking Skill Testing System Muhammad, Aliv Faizal; Assidiqi, Moh. Hasbi; Sa’dyah, Halimatus
Journal of Educational Sciences Vol. 7 No. 2 (2023): Journal of Educational Sciences
Publisher : FKIP - Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jes.7.2.p.296-305

Abstract

Speech recognition technology has been widely used to aid English language testing, such as for pronunciation and speaking practice for various use cases including job interview simulation. We developed an app, powered by speech recognition technology and regex text processing, that automatically score participants’ speaking performance. However, this application lacks dataset management for the speaking test maker to manage the indicators of accepted answers. A feature to effectively manage the dataset and efficiently ease the speaking test creation is needed. This paper presents the development of dataset management feature to support the speech recognition based automatic English-speaking skill testing system. All elements that support the feature in development were tested through Blackbox testing with various scenarios. The elements were proven to work as intended to support the existing system. The new feature has brought efficiency for the English-speaking test maker.
Profile of body mass index in swimming athletes aged 7–14 years Assidiqi, Moh. Hasbi; Ariani, Luh Putu Tuti; Yoda, I Ketut
Sriwijaya Journal of Sport Vol. 5 No. 2 (2026): Sriwijaya Journal of Sport
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55379/sjs.v5i2.234

Abstract

Research Background: Swimming performance is closely associated with athletes’ anthropometric characteristics and nutritional status. Body Mass Index (BMI) is widely used as a practical indicator to assess weight–height proportionality in youth athletes; however, evidence on BMI profiles among junior swimmers at the club level remains limited. Research Objective: This study aimed to describe the BMI profile and the distribution of nutritional status categories among junior swimming athletes aged 7–14 years based on BMI-for-age standards. Method: A quantitative descriptive study with a cross-sectional design was conducted involving 40 junior swimmers (20 males and 20 females) selected through total sampling. Body weight and height were measured using standard anthropometric procedures, and BMI was calculated and classified according to BMI-for-age growth references from the Indonesian Ministry of Health. Data were analyzed descriptively and supplemented with simple comparative analysis across age groups. Results: The results showed that the mean BMI was 18.44 ± 3.68 kg/m² (range: 10.43–31.37 kg/m²). Based on BMI-for-age classification, 30.0% of athletes were severely underweight, 27.5% underweight, 35.0% normal, 5.0% overweight, and 2.5% obese. In total, 57.5% of athletes were categorized as underweight. No significant differences in BMI were observed across age groups. Conclusion: In conclusion, most junior swimmers had BMI below the normal range despite regular training. These findings emphasize the importance of regular BMI-for-age monitoring, along with appropriate nutritional interventions to support optimal growth and athletic performance.
Co-Authors I Ketut Yoda Luh Putu Tuti Ariani Muhammad, Aliv Faizal Sa’dyah, Halimatus