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All Journal JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Jurnal Teknik Informatika (JUTIF) Media Jurnal Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) PENA ABDIMAS : Jurnal Pengabdian Masyarakat Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Irani Hoeronis
Universitas Siliwangi, Tasikmalaya, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Nursing Public Health Social Sciences

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ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA Al-Husaini, Muhammad; Lukmana, Hen Hen; Rizal, Randi; Puspareni, Luh Desi; Hoeronis, Irani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2421

Abstract

This research uses an ensemble model and neural network method that combines several machine learning algorithms used in the prediction of stunting and nutritional status children in Purbaratu Tasikmalaya. This ensemble method is complemented by a combination of the prediction results of several algorithms used to improve accuracy. The data used is anthropometry-based calculations of 195 toddlers with 39% of related stunting from 501 total data in Purbaratu Tasikmalaya City; high rates of stunting this research urgent to make a stable model for prediction. The results of this study are significant as they provide a more accurate and efficient method for predicting stunting and nutritional status in children, which can be crucial for early intervention and prevention strategies in public health and nutrition. The best accuracy value for some of these categories is 98, 21% for the Weight/Age category with the xGBoost algorithm, 97.7% of the best accuracy results with the Random Forest and Decision Tree algorithms for the Height/Age category, the Weight/Height category with the best accuracy of 97.4% for the Random Forest and xGBoost algorithms, and the use of neural network models resulted in an accuracy of 99.19% for Weight/Age and Height/Age while for Weight/Height resulted in an accuracy of 91.94%..
Audio Signal Classification using Mel-Frequency Cepstrum Coefficients and Deep Neural Network for Noon Saakin or Tanween Tajweed Rule Dataset Irawan, Genta Hayindra; Mubarok, Husni; Hoeronis, Irani
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2875

Abstract

The Al-Qur’an serves as a fundamental guide for Muslims, requiring both comprehension and practice. Accurate recitation according to tajweed rules is essential for a deeper understanding of its meaning. Despite the growing focus on classification across various modalities, studies specifically targeting audio objects remain relatively limited, motivating further exploration in this area. This study focused on the classification of the tajweed rule as the decided audio object, leveraging the potential of Natural Language Processing (NLP) to support Qur’an research and studies, as well as developing applications that may help learners understand the Qur’an, so further study is needed on the recognition of tajweed reading rules, one of which is the noon saakin or tanween tajweed rule. Audio features were extracted using Mel-Frequency Cepstrum Coefficients (MFCC) technique, which has been widely adopted in various study within the scope of audio processing tasks. These features were subsequently used to train a classification model based on Deep Neural Network (DNN) algorithm. Experiment results demonstrate that the DNN classification model produces an accuracy of 71% and f1 score respectively for iqlab of 0.8, idgham of 0.46, idzhar of 0.77, and ikhfa of 0.72. The results of testing the model with new foreign data, each class tested with one data has successful rate of 50%. These findings indicate that the classification model needs to be further improved in terms of its design or diversity of the audio data, especially model improvements in the recognizing idgham, idzhar, and ikhfa laws.
Co-Authors Acep Irham Gufroni AL Husaini Al-Husaini, Muhammad Al-Husaini, Muhammad Bambang Riyanto Trilaksono Hen Hen Lukmana Hen Hen Lumana Husni Mubarok Irawan, Genta Hayindra Lavenia, Febby Lesi Oktiwanti Lukmana, Hen Hen Muhammad Al-Husaini Nur Fajar Nurrohman, Agni Puspareni, Luh Desi Rachman, Andi Nur Rizal, Randi Santiana Santiana Sidik Ramdani, Cecep Muhamad Sri Maywati Sulastri, Heni