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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Linguistik Komputasional
Mandasari, Miranti Indar
Institut Teknologi Bandung
Computer Science & IT Engineering

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Kalibrasi Rasio kemungkinan pada Sistem Rekognisi Pengucap Otomatis untuk Aplikasi Forensik di Indonesia Miranti Indar Mandasari; Angga Dwi Firmanto; Fadjar Fathurrahman
Jurnal Linguistik Komputasional Vol 2 No 2 (2019): Vol. 2, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.142 KB) | DOI: 10.26418/jlk.v2i2.24

Abstract

Kalibrasi LR merupakan tahapan yang sangat penting saat akan mengaplikasikan sistem rekognisi pengucap otomatis pada bidang forensik. Artikel ini memuat tahapan dan evaluasi terhadap sistem rekognisi pengucap yang dibangun menggunakan basis data suara ucap berbahasa Indonesia. Sistem dikembangkan menggunakan fitur MFCC, pemodelan GMM-UBM, dan normalisasi Z. Sistem dievaluasi kinerjanya berdasarkan gender laki-laki dan perempuan, serta dua skenario, yakni percakapan natural dan wawancara. Evaluasi sistem dilakukan menggunakan indikator performa dalam hal kemampuan diskriminasi dan kalibrasi sistem. Hasil evaluasi dengan berbagai indikator menunjukkan bahwa sistem rekognisi pengucap otomatis yang dibangun telah menunjukkan hasil yang sangat baik. Hal ini ditunjukkan dengan nilai EER terbaik sebesar 4.66%, dan nilai Cmc sebesar 0.04. Dengan begitu, sistem yang dikembangkan telah siap untuk dipakai sebagai alat analisis rekognisi pengucap otomatis untuk aplikasi forensik di Indonesia.
Estimating broiler heat stress using computer vision and machine learning Anggoro Agung, Muhammad Iqbal; Mursito Budi, Eko; Indar Mandasari, Miranti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2922-2934

Abstract

To optimize and enhance the efficiency of broiler chicken farming, it is essential to maintain the chicken’s welfare, as heat stress can decrease growth efficiency. The temperature-humidity index (THI) is a key indicator used to determine if chickens are experiencing heat stress. Precision livestock farming (PLF) based on computer vision is one method that can assist farmers in continuously and automatically monitoring the condition of their chickens. This research developed a computer vision-based PLF system to observe chickens with CP 707 strain in a commercial farm using the Mask region-based convolutional neural network (Mask R-CNN) method and object tracking algorithms to analyze features such as the cluster index, unrest index, and the distance traveled by broilers. The results indicated that all features tend to inversely correlate with the THI value, with the cluster index showing the most noticeable tendency. Additionally, it was found that external factors, such as the presence of farmers around the observation area, can affect the chickens' behavior, although the cluster index feature is relatively resilient to disturbances if the operator is not captured by the camera. It was concluded that there is a relationship between the features and the THI value; however, these features are not yet sufficient to distinguish the condition of chickens under high and low THI conditions in real-time.
Co-Authors Angga Dwi Firmanto Anggoro Agung, Muhammad Iqbal Fadjar Fathurrahman Mursito Budi, Eko