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All Journal International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat
Areni, Intan Sari
Hasanuddin University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Public Health Other

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Cocoa bean quality identification using a computer vision-based color and texture feature extraction Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1609

Abstract

The current pressing issue in the downstream processing of cocoa beans in cocoa production is a strict quality control system. However, visually inspecting raw cocoa beans reveals the need for advanced technological solutions, especially in Industry 4.0. This paper introduces an innovative image-processing approach to extracting color and texture features to identify cocoa bean quality. Image acquisition involved capturing video with a data acquisition box device connected to a conveyor, resulting in image samples of Good-quality and Poor-quality of non-cutting cocoa beans dataset. Our methodology includes multifaceted advanced pre-processing, sharpening techniques, and comparative analysis of feature extraction methodologies using Hue-Saturation-Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) with correlated features. This study used 15 features with the highest correlation. Machine Learning models using Support Vector Machine (SVM) with some parameter variation value alongside an RBF kernel. Some parameters were measured to compare each approach, and the results show that pre-processing without sharpening achieves better accuracy, notably with the HSV and GLCM combination reaching 0.99 accuracy. Adequate technical lighting during data acquisition is crucial for accuracy. This study sheds light on the efficacy of image processing in cocoa bean quality identification, addressing a critical gap in industrial-scale implementation of technological solutions and advancing quality control measures in the cocoa industry.
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal Chyan, Phie; Achmad, Andani; Nurtanio, Ingrid; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2026

Abstract

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Co-Authors ., Dewiani ., Wardi Adnan Adnan Ahmad, Andani Akil, Yusri Syam Al Ghozy, Osama Iyad Alimuddin, A.Ais Prayogi Amil Ahmad Ilham Amirullah, Indrabayu Andani Achmad Andini Dani Achmad, Andini Dani Anshar, Muh Arifin, Nurhikma Basri Basri Bustamin, Anugrahyani Bustamin, Anugrayani Dewiani Dewiani Eka Qadri Nuranti Elly Warni Fitriyanti Mayasari Ida Ayu Putu Sri Widnyani Ifwan, Ichsanul Al Ikhlas Kitta Indrabayu . Indrabayu Indrabayu Ingrid Nurtanio Jefri, Muhammad Rivaldi Lubis, Nurlatifah Mahdaniar, Mahdaniar Marindah, Tyanita Puti Mokobombang, Novy Nur R A Mokobombang, Novy Nur R.A Mukarramah Yusuf Musyfirah, Kamtina Naili Suri Intizhami Nur Hikmah Nurdin, Arliyanti Nurdin, Winati Mutmainnah Nurjannah . Palantei, Elyas Paundu, Ady Wahyudi Phie Chyan Putri Akhmad, Ummu Qalsum Raden Wirawan Rahayu, Mesra RIFALDI, AHMAD Salam, Ejah Umraeni Salama Manjang Suandi, Ahmad Yusuf Syafaruddin Syafaruddin Tassakka, Muhammad Irpan Sejati Wardi Wardi Yohannes, Christoforus Yohannes, Chystoporus Yudha, Muh. Reza Eka Zaenab Muslimin Zahir Zainuddin Zulkifli Tahir