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All Journal Jurnal Sains dan Teknologi Pangan Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Fish Protech Journal of Robotics and Control (JRC) Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT)
Sadimantara, Muhammad Syukri
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Immunology & microbiology Materials Science & Nanotechnology Mechanical Engineering Physics

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The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning Sadimantara, Muhammad Syukri; Argo, Bambang Dwi; Sucipto, Sucipto; Al Riza, Dimas Firmanda; Hendrawan, Yusuf
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.19081

Abstract

Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”.  300 images were acquired through a mini studio equipped with UV lamps.  The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.
Study on The Addition of Red Ginger (Zingiber officinale Var. Rubrum) and Black Rice (Oryza sativa L. Indica) to The Organoleptic Evaluation and Physical-Chemical Properties of Robusta Coffee (Coffea canephora) from Southeast Sulawesi Asyik, Nur; Herdiansyah, Dhian; Sadimantara, Muhammad Syukri
Jurnal Penelitian Pendidikan IPA Vol 11 No 7 (2025): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i7.10946

Abstract

This study aims to determine the effect of adding red ginger and black rice on the organoleptic and physicochemical properties of robusta coffee. The problem in this study arose because, so far, the caffeine content in coffee has generally been high. On the other hand, the addition of red ginger and black rice can reduce caffeine levels and improve the taste of coffee. This study employed a factorial Completely Randomised Block Design with two factors. The first factor is the addition of red ginger (J) at three levels: 4% (J1), 8% (J2), and 12% (J3). The second factor is the addition of black rice (B) at two levels: 10% (B1) and 20% (B2). Organoleptic data were analyzed using ANOVA and DMRT tests. Physicochemical properties were analyzed descriptively. The results of the study showed that the best treatment was with the addition of 12% red ginger and 20% black rice (J3B2), with a color of 3.75 (like), aroma 3.90 (like), texture 4.00 (like), and taste 4.03 (like), water content 1.113%, ash 3.657%, pH 5.83, caffeine 0.72%, chlorogenic acid 4.92%, and antioxidant activity (IC50) 39.28 ppm
Co-Authors A.A. Ketut Agung Cahyawan W ABDU RAHMAN BACO, ABDU RAHMAN alim, Usman Aminarti, Aminarti Ansharullah ansharullah Asnani Asnani Auliana, Rana Badiun, Tri Muhammad Nandang Bambang Dwi Argo Chatarina Umbul Wahyuni Dimas Firmanda Al Riza Efendi, Sarina Faisal Faisal Firlana, Ayuh Fitri Fitri Frisandi, Angga Angrullah Haerul, Andi Hasjal, Muhamad Haslianti Haslianti, Haslianti Herdiansyah, Dhian Hermanto Hermanto Ibrahim, Mohamad Nuh Indriani Indriani Irianto, Fitri Amelia Irma Yanti Isamu, Kobajashi T Jesrin, Jesrin Kamaluddin, La Ode L, Mariani La Karimuna Lasran, Lasran Lili Fitriani Maulidina, Ardisa Merlin Merlin, Merlin Mirna Sari Mita Gebriella Inthe Muhammad Amrullah Pagala Muzakkar, Muhammad Zakir Nangi, Laode Jumadil Nilam, Nilam Ntia, Wa Nur Asyik, Nur Nurhaliza, Nurhaliza Patadjai, Andi Besse Pratiwi, Nesya Dhifa Prima Endang Susilowati Putriyana, Sendri Rahayu, Marni Rhenislawaty, Rhenislawaty Rukmana, Rukmana Safitri, Febriani Safitri, Neli Elika Sakir Sakir, Sakir Samsia, Samsia Sariati Sariati, Sariati Siska Siska Sri Rejeki Sri Wahyuni Sudarman Sudarman Suwarjoyowirayatno, Suwarjoyowirayatno Tamrin Tamrin Tan Malaka Tanaka, Imelda Mustika Ulfa, Sitti Hawana Wibisono, Supri Yani, Mohammad Ashabul Yusuf Hendrawan