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All Journal Dinamik JURNAL INSTEK (Informatika Sains dan Teknologi) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Information System Exploration and Research
Nuk Ghurroh Setyoningrum
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Innovation in Research of Informatics (INNOVATICS)

 1 
Analysis of Image Improvement and Edge Identification Methods in Watermelon Image Sudiarjo, Aso; Praseptiawan, Mugi; Setyoningrum, Nuk Ghurroh; Drajat, Hilmi Maulana; Natsir, Fauzan
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10699

Abstract

The initial stage in digital image processing, known as pre-processing, plays a vital role in enhancing image quality. This essential step involves employing various techniques to prepare the image for subsequent analysis and feature extraction. Among the array of pre-processing methodologies utilized, thresholding, median averaging, median filtering, rapid Fourier transform, point operations, intensity modification, and histogram equalization stand out as prominent tools. These techniques are employed to mitigate noise, enhance contrast, and optimize the overall visual quality of the image. Once the pre-processing phase is complete, the focus often shifts to specific tasks, such as identifying objects or features within the image. In the context of analyzing watermelon images, one such task is the detection of watermelon seeds. To accomplish this, the pre-processed image undergoes further refinement through the application of edge detection techniques. Gradient edge detection, isotropic, Canny, and Sobel edge detection are among the methods commonly employed for this purpose. These techniques aim to highlight the edges and contours of objects within the image, facilitating the identification of distinct features such as watermelon seeds. However, our investigation reveals that not all edge detection methods are equally effective in this context. By employing a combination of pre-processing techniques and judiciously selecting edge detection methods, researchers can enhance the accuracy and reliability of their image processing workflows, ultimately advancing our understanding of complex biological structures such as watermelon seeds.
The Impact of Linguistic Features on Emotion Detection in Social Media Texts Setyoningrum, Nuk Ghurroh; Febriani SM, Neng Nelis; Alam, Alam; Nurdin, Arif Muhamad; Nursamsi, Dede Rizal; Lodana, Mae B
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13221

Abstract

Emotions are an important aspect of human life, and scientific theories on emotions have been widely developed in various research fields such as philosophy, psychology, and neuroscience. In human-computer interaction, understanding emotions is also very important. Detecting emotions not only enables better decision-making, but is also useful in various contexts such as business, politics, and mental health. The focus on identifying emotions in text arises because emotions are often implied without explicit words. Through the analysis of grammar and sentence structure, text mining techniques enable the extraction of sentiments and emotions. Detecting and identifying emotions in text is important because it can be applied in a variety of fields, including decision-making, prediction of human emotions, product assessment, analysis of political support, and identification of depression. Text as textual data is an important source of information due to its ability to convey human emotions. In this research, emotion detection uses the Naïve Bayes method, with attribute weighting to improve accuracy using count vector. This classification approach allows grouping text into six emotion categories: happy, sad, fear, love, shock, and anger. The Naïve Bayes method was chosen for its reliability in classifying data based on conditional probabilities. Thus, this research provides a deeper understanding of understanding and managing emotions in the context of social media. The data classification results yield precision, recall, F1-Measure, and accuracy values.
Data Augmentation Strategies on Spectrogram Features for Infant Cry Classification Using Convolutional Neural Networks Alam, Alam; Setyoningrum, Nuk Ghurroh; Maududy, Robby; Damayanti, Dea Dewi; Rahmawati, Hilmi
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16823

Abstract

Infant cry classification is an important task to support parents and healthcare professionals in understanding infants’ needs, yet the challenge of limited and imbalanced datasets often reduces model accuracy and generalization. This study proposes the application of diverse audio data augmentation strategies including time stretching, time shifting, pitch scaling, and polarity inversion combined with spectrogram representation to enhance Convolutional Neural Network (CNN) performance in classifying infant cries. The dataset from the Donate-a-Cry Corpus was expanded from 457 to 6,855 samples through augmentation, improving class balance and variability. Experimental results show that CNN accuracy increased from 85% before augmentation to 99.85% after augmentation, with precision, recall, and F1-score reaching near-perfect values across all categories. The confusion matrix further confirms robust classification with minimal misclassifications. These findings demonstrate that data augmentation is crucial to overcoming dataset limitations, enriching acoustic feature diversity, and reducing model bias, while offering practical implications for the development of accurate, reliable, and real-world applicable infant cry detection systems.
Co-Authors Agung Prihandono ALAM Anisa Solehah Nurwendah Damayanti, Dea Dewi Drajat, Hilmi Maulana Ema Utami Fauzan Natsir Ferry Wahyu Wibowo Hari Murti KAPI, CECEP M. Kholiq Mochamad Ramdan Khozizah, Putri Kusrini, Kusrini Lodana, Mae Lodana, Mae B Maududy, Robby Much Aziz Muslim Mulyani, Fithri Sri Mustofa, Pramesti Melyna N. Nelis Febriani SM Nurdin, Arif Muhamad Nursamsi , Dede Rizal Nursamsi, Dede Rizal Pepi Alpiani Pertiwi, Dwika Ananda Agustina Praseptiawan, Mugi Putri Khozizah Rahmawati, Hilmi Sudiarjo, Aso