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All Journal Dinamik JURNAL INSTEK (Informatika Sains dan Teknologi) Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Nuk Ghurroh Setyoningrum
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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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
INNOVATICS: International Journal on Innovation in Research of Informatics 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.
Co-Authors Agung Prihandono Alam, Alam Drajat, Hilmi Maulana Fauzan Natsir Hari Murti KAPI, CECEP M. Lodana, Mae B Maududy, Robby Mulyani, Fithri Sri Mustofa, Pramesti Melyna N. Nelis Febriani SM Nurdin, Arif Muhamad Nursamsi , Dede Rizal Nursamsi, Dede Rizal Praseptiawan, Mugi Sudiarjo, Aso