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All Journal Pelita Perkebunan
Ariefandie Febrianto, Noor
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Agriculture, Biological Sciences & Forestry

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Utilization of cocoa pod husk and wood charcoal into briquettes as an environmentally friendly alternative fuel. Arriza Novan Tahta Aunillah, Mohammad; Bayu Cezarridfalah, Bintang; Kirana Putri, Jesika; Nurmawati, Ardika; Ariefandie Febrianto, Noor; Adi Saputro, Erwan
Pelita Perkebunan (a Coffee and Cocoa Research Journal) Vol. 40 No. 2 (2024)
Publisher : Indonesian Coffee and Cocoa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iccri.jur.pelitaperkebunan.v40i2.616

Abstract

Many efforts have been made to convert cocoa pod husk waste into charcoal briquettes, but they have not yet met the established Indonesian National Standard(SNI). Therefore, steps are needed to produce charcoal briquettes that comply with these standards. One approach that can be taken is by blending cocoa podhusk with wood charcoal since wood charcoal has a sufficiently high calorific value that can enhance the quality of the charcoal briquettes. This research aimsto find the optimal conditions for making briquettes from cocoa pod husk and identify the impact of briquette composition and carbonization time on valuessuch as moisture content, ash content, calorific value, and burning rate of the produced briquettes. The research process includes carbonization, grinding, sieving, adding a binder, and drying, followed by testing the briquettes’ characteristics. This research was conducted by comparing the mass composition of cocoa pod husk charcoal and wood charcoal at ratios of 100:0, 75:25, 50:50, and 25:75, with carbonization times of 1.5, 2, 2.5, and 3 hours. The best analysis results, in accordance with the Indonesian National Standard (SNI), were obtained at a carbonization time of 2 hours with a composition of 50:50 (cocoa pod husk:wood charcoal). The values include a moisture content of 5.944%, ash content of 7.83571%, calorific value of 4388.5 kcal kg -1 , and burning rate of 0.0034 g second -1 . The length of the carbonization process has a significant impact on the characteristics of the resulting briquettes, including moisture content, ash content, calorific value and burning rate. The longer the carbonization process, the lower the moisture content and ash content, and the higher the heating value and burning rate.
Color-based Classification of Dried Cocoa Beans from Various Origins of Indonesia by Image Analysis Using AlexNet and ResNet Architecture-Convolutional Neural Networks Kristianingsih, Wahyu; Dwi Argo, Bambang; Jati, Misnawi; Ariefandie Febrianto, Noor; Hendrawan, Yusuf; Bagus Hermanto, Mochamad; Rahmatullah, Bagus
Pelita Perkebunan (a Coffee and Cocoa Research Journal) Vol. 40 No. 3 (2024)
Publisher : Indonesian Coffee and Cocoa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iccri.jur.pelitaperkebunan.v40i3.638

Abstract

Cocoa plant is widely cultivated in Indonesia and spread across various regions. Diversity in geographical conditions has been known to significantly affect the quality of cocoa beans. Practically, cocoa beans are often mixed without considering the variation in the quality and its origin. This resulted in reduced global quality and product inconsistency. Improved recognition and classification methods are needed to solve those problems. Non-destructive classification methods can be used to provide a more efficient classification process. The use of artificial intelligence with computer-based deep learning methods was used in this study. Beans samples of various origins (Aceh, Bali, Banten, Yogyakarta, East Kalimantan, West Sulawesi, and West Sumatera) were evaluated. From thecollected samples, 9100 images were then taken for data processing. Data preprocessing included denoising of the background image, cropping, resizing andchanging the storage extension through the training-validation stage and the testing process. AlexNet and ResNet architectures on a Convolutional NeuralNetwork were used for classification. The results showed that the average accuracy of cocoa image classification based on color identification by computer machines using Alexnet and ResNet was high (99.91% and 99.99%, respectively). This method can be applied to provide more efficient color-based cocoa bean classification for industrial purposes.
Co-Authors Adi Saputro, Erwan Ardika Nurmawati, Ardika Arriza Novan Tahta Aunillah, Mohammad Bambang Dwi Argo Bayu Cezarridfalah, Bintang Kirana Putri, Jesika Kristianingsih, Wahyu Misnawi Jati Mochamad Bagus Hermanto Rahmatullah, Bagus Yusuf Hendrawan