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ABSTRAKLimbah kulit kakao dalam jumlah banyak (75% dari dari bahan baku pengolahan coklat) menjadi permasalahan tersendiri pada industri pengolahan coklat. Pemanfaatan kulit kakao berpotensi sebagai bahan bakar alternatif terbarukan karena ketersediaanya yang cukup melimpah dan pemanfaatannya yang belum maksimal. Pemanfaatan kulit kakao sebagai biopelet menjadi salah satu solusi pengolahan kulit kakao yang tepat untuk menghindari masalah baru bagi lingkungan yaitu pembusukan karena adanya penguraian karbon oleh mikroorganisme. Karakteristik kulit kakao yang mengandung lignin sebesar 12-19% menjadikan kulit kakao sangat potensial digunakan sebagai biopelet. Tujuan dari penelitian ini adalah mengetahui pengaruh ukuran partikel dan penambahan tapioka pada pembuatan biopelet kulit kakao, menentukan perlakuan optimal serta melakukan uji karakteristik fisik dan kimia biopelet kulit kakao. Metode eksperimen dengan pengolahan data secara deskriptif kuantitatif dengan membandingkan hasil uji karakteristik biopelet dari variasi ukuran mesh dan variasi kadar perekat (tepung tapioka) untuk mencari kualitas biopelet yang optimal. Perlakuan optimal terjadi pada ukuran partikel biopelet 20 mesh dengan penambahan perekat tapioka 20% dengan kadar air 3,52%, kadar abu 6,99%, dan kerapatan 0,87 g/cm 3 dan nilai kalor 3090,1 kal/g. Pengembangan penelitian dapat diarahkan kepada penambahan campuran bahan baku yang mempunyai nilai kalor tinggi sehingga dapat meningkatkan nilai kalor biopelet yang dihasilkan.Kata kunci : biopelet, kakao, kadar air, kadar abu, nilai kalor ABSTRACT Waste of cocoa shellsare becoming a problem in cocoa processing industry in large quantities (75% of raw material). Cocoa shells are potential as a renewable alternative fuel because of its availability and not exploited yet. The utilization of cocoa shells as biopelet is one of method to convert a cocoa shell as

Complexity is considered one of the hallmarks of megaproject failure; however, no common definition of complexity in the megaproject context exists in contemporary literature; particularly in developing countries. The present study explores the definitions, characteristics, and strategy to respond to the complexity of megaprojects in developing countries. An exploration of normative theories and a systematic literature review were performed to investigate the concept of complexity. This study proposes the definition of complexity as a “challenge” for entities—including project managers—in megaproject management. This definition extends to encompass both positive and negative challenges, offering a more balanced perspective on the causes of failure in addition to the sources of opportunities for innovation. We determine the aspects of megaproject complexity associated with structural and social factors of interrelatedness, nonlinearities, and emergence. This study proposes a formal definition, clarifying the characteristics of complexity and synthesizing strategy themes that respond to megaproject complexity. This resulting study provides insights for both megaproject researchers and professionals.

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Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.

The number of corn production and consumption in Indonesia has increased every year. One of the activities in achieving the production target is by supplying high-quality corn seeds. The water content of harvested sweet corn was ranging from 60% to70%. In order to produce a high quality of seeds, drying should perform with a standard drying temperature for seeds which is between 38°C and 43°C. A performance test of a tray drying machine with an air dehumidifying process was conducted. The air dehumidifying process was conducted by an air conditioning system. Drying was carried out until the corn seeds reach 10-11% moisture content with observations every two hours. This drying machine provided lower relative humidity air drying than conventional sun-drying with a 75% relative humidity reduction from the environment. Therefore, drying with optimal temperature can be conducted faster and higher efficiency. The average dehumidified air temperature was 41.1±0.6°C and the average relative humidity was 15.9±0.7% was used for drying air. It took 44 hours 25 minutes to reach 11% of sweet corn’s moisture content while the sun drying process took 89 hours of drying. The seed germination could reach 85.7% by using this method which was a 1% difference from a sun-drying method which was 86.7%.

Climate change will become the priority for the air quality management. It focuses more on carbon dioxide emission. Indonesia which has power generation dominated by coal combustion is expected to develop the national CO2 emission factor. Due to the high variation in Indonesia coal rank and its growing magnitude in CO2 emission caused by the future coal-fired power plant development, the characteristic emission value becomes an important concern. The method used in this study is developed from the IPCC Guidelines for Energy. The conversion unit plays an important role in the calculation method. The result shows that the higher in C content, the lower in its CO2 emission factor. It means that coal classified as high C content or high heating value will produce low carbon dioxide emission factor. The average CO2 emission factor obtained in Indonesian coal is 99,718 kg CO2/TJ with an average value of carbon content 27.2 kg C/GJ, and NCV equal to 19.8 TJ/Gg. Coal rank is categorized as lignite to subbituminous or bituminous.