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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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Journal : JOIV : International Journal on Informatics Visualization

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Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia Gufroni, Acep Irham; Hoeronis, Irani; Fajar, Nur; Rachman, Andi Nur; Sidik Ramdani, Cecep Muhamad; Sulastri, Heni
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1949

Abstract

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.
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