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International Conference on Science and Technology Applications 2019, ICoSTA 2019

作者： Supriadi, A. Saputra, D. Priyanto, G. Baehaki, A. Pambayun, R. Doctoral Stud. at the Faculty of Agriculture Universitas Sriwijaya Indonesia Agricultural Engineering Study Program Faculty of Agriculture Agricultural Product Technology Study Program Faculty of Agriculture Fishery Product Technology Study Program Faculty of Agriculture Universitas Sriwijaya Palembang30139 Indonesia

Pempek is a traditional ethnic food originated from Palembang, South Sumatra, Indonesia. Currently, there is not exist any profiles which could be used as a determining factor for quality, originality, and ethnicity of pempek. The objective of this research was to obtain the chemical profile of pempek as a determining factor of quality, originality and ethnicity. The samples were collected from the ten famous branded vendors in Palembang. The fish used in making pempek of the samples are snake head fish, Spanish mackerel, and the mix of snake head and Spanish mackerel. The type of pempek used in this experiment was steamed cook pempek lenjer (cylindrical) type. The analysis was performed at a KAN accredited laboratory. It was found that the energy content of pempek used in this experiment was in the range of 125 to 160 kkal/100 g sample and all the pempek used had a 0% of fat. All pempek used had 17 types of amino acid, and all of them had an essential amino acid. The significant finding was that the concentration of glutamic acid was 200% of the concentration of common concentration which indicated that all the vendors used artificial mono-sodium glutamate to improve the taste of pempek. It was concluded that the chemical profile found on this research could be used as database for creating a standard of identity of pempek. © Published under licence by IOP Publishing Ltd.

关键词： Fish

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Benchmarking A New Dataset for Coffee Bean Defects Classification Based on SNI 01-2907-2008

Benchmarking A New Dataset for Coffee Bean Defects Classific...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Made Windu Antara Kesiman Ismail Sulaiman I Made Dendi Maysanjaya Kadek Teguh Dermawan Dept. of Informatics Engineering Virtual Vision Image and Pattern Research Group Universitas Pendidikan Ganesha Bali Indonesia Program Study Teknologi Hasil Pertanian Faculty of Agriculture Universitas Syiah Kuala Banda Aceh Indonesia

To develop a device which can determine automatically the quality of coffee beans, the module to identify and classify coffee bean defects based on digital images processing is needed. For this reason, a new dataset of coffee bean defect images is built in this research. 3 classes were defined and obtained from the image sample of coffee beans, namely good coffee beans, defective coffee beans, and non-valid coffee beans. Moreover, specifically for the class of defective coffee beans, the new dataset also provides ground truth image samples for 17 classes of coffee bean with defects which was defined by SNI 01-2907-2008. Two Convolutional Neural Network (CNN) architecture models, InceptionResnetV2 and MobileNet, were tested to do an initial benchmarking process to classify coffee beans and their types of defects. Both architectures show very promising results to classify coffee bean images in 3-classes. With 92.52% of accuracy, MobileNet outperforms slightly InceptionResnetV2. Both architectures are fairly easy to classify the 3-classes of coffee bean images. But the experimental results indicate that the task of classifying more specifically the 17 classes of different types of defects in coffee beans is still quite difficult. Although the accuracy of InceptionResnetV2 of 53.35% seems much higher than the accuracy of MobileNet, the two architectures still seem to be struggling to identify each type of coffee bean defect. This benchmarking results will serve as the initial basis that helps to analyze the level of complexity of problems in the coffee bean new dataset, as well as the basis for further investigations and research processes to improve the performance of the future coffee bean classifier machine.

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study on the General Regional Energy Planning of North Sumatra Province, Indonesia in terms of Biogas for Household Energy Demand

Study on the General Regional Energy Planning of North Sumat...

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2021 International Conference on Science and Contemporary Technologies, ICSCT 2021

作者： Ginting, Nurzainah Tarigan, Ap Mulia Purba, Yudisthira University of Sumatera Utara Department of Animal Husbandry Faculty of Agriculture Medan Indonesia University of Sumatera Utara Civil Engineering Program Study Faculty of Engineering Medan Indonesia University of Sumatera Utara Food Security and Animal Husbandry Services Medan Indonesia

ISBN: (纸本)9781665421324

Indonesia central government policy in managing national level energy plans must be succeeded by the government at the provincial level. Every province has to make its energy planning projection until 2050. This study aims to study the potential of biogas at provincial level. The method taken for this research is based on secondary data that refers to documents issued by the Department of Mines and Energy, data from the Department of Food Security and Livestock of North Sumatra Province as well as data from the Indonesia Statistic Bereau. These data are used as a basis for calculating the biogas energy potential of livestock. This research is limited to biogas particularly biogas from cow/buffalo for household hold energy necessity/cooking. The calculation results show that the biogas potential in 2050 is 1,178 TOE, meanwhile the projection data issued by the Department of Mines and Energy of the Province of North Sumatra in the first projection, namely business as usual (BAU) is 12 TOE and the second projection, namely the Regional Energy General Plan (RUED) is 55 TOE. Based on the result it can be recommended that biogas potential from livestock has to be utilised as much as possible for household energy necessity/cooking. It is reasonable because the result show the value reported by the Department of Mining and Energy BAU 12 TOE and RUED 55 TOE is far from the value resulted from this study where 1,178 TOE. © 2021 IEEE.

关键词： Biogas

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Land suitability analyze for cocoa development based on square root

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AIP Conference Proceedings 2023年第1期2596卷

作者： Nirmala Juita Husnul Mubarak Erwin Wijaya Emmy Fadhila Nurmaranti Alim Department of Soil Science Faculty of Agriculture Hasanuddin University Agriculture Engineering Study Program Faculty of Agriculture Hasanuddin University Program study of Agroecotechnology Faculty of Agriculture Sulawesi Barat University

Indonesia is the world's third-largest cocoa producer and exporter after Ghana and Pantai Gading. Cocoa is in great demand by people in various parts of the world because Cocoa can be processed into chocolate in food and drinks that benefit the community. One approach that is often used to evaluate cocoa land suitability is a parametric approach with a square root. The square root is part of the parametric approach in evaluating land suitability by assigning a value at a different limiting level to the nature of the land. On a standard scale, it is given a maximum weight of 100 to a minimum value of 0 then the land suitability class calculation is based on the square root. The stages of this research are: 1) Collecting secondary data and basic maps such as climate data, administrative maps, geological maps, soil type maps, and land use maps, 2) Making maps of land units 3) Preliminary surveys, 4) Sampling and analysis of soil samples in the laboratory, 5) analysis of the land and climate suitability, 5) Determination of actual and potential land suitability. The method used in this research is a parametric approach (Square root by Khiddir). The results showed that the parametric method's land suitability class (square root) was N1 (not suitable at present) at the observation points of profiles 1, 3, and 5. In contrast, the S3 land suitability class was found in profile 4. Limiting factors such as annual temperature, slope, soil depth, and coarse fragments are limiting factors that are difficult to repair. Base saturation and pH H2O with liming, or addition of organics.

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Developing of calibration model for acetic acid, flavonoid, and capsaicin content from fresh red chilies using combination Vis-NIR spectral descriptors, machine learning and their stacking ensemble learning

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Devianti Hafsah, Siti Yusmanizar Bulan, Ramayanty Saputra, Edo Department of Agricultural Engineering Faculty of Agriculture Syiah Kuala University Banda Aceh 23111 Indonesia Agricultural Mechanization Research Centre Syiah Kuala University Banda Aceh 2311 Indonesia Department of Agrotechnology Faculty of Agriculture Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agricultural Technology Faculty of Agriculture Universitas Riau Pekanbaru 28293 Indonesia Agricultural Engineering Study Program IPB University Bogor 16680 Indonesia

Rapid and non-destructive measurements to determine the amount of certain compounds in products such as fresh red chilies are still challenging today. Therefore, the objective of this study was to develop a fast and non-destructive calibration model of fresh red chilies for the content of acetic acid, flavonoid, and capsaicin using a combination of visible near-infrared spectroscopy (Vis-NIR) descriptors in the range 380–1065 nm and machine learning regressors including random forest (RF), Ridge, decision tree (DT) and their stacking ensemble learning (SEL). The best calibration model to predict the content of acetic acid and flavonoid compounds is to use SEL, and capsaicin is to use DT. Thus, the proposed model can effectively predict the compound content in fresh red chilies and may have important applications in helping researchers and practitioners assess its quality non-subjectively and non-invasively. © 2024 The Authors

关键词： agriculture product Chemometrics Machine learning Non-destructive measurements

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Multi-parameter prediction of oil palm fruit quality through near infrared spectroscopy combined with chemometric analysis

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Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 2025年 343卷 126505页

作者： Nanda, Muhammad Achirul Amaru, Kharistya Rosalinda, S. Novianty, Inna Park, Tusan Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial Technology Universitas Padjadjaran Sumedang45363 Indonesia Computer Engineering Technology Study Program College of Vocational Studies IPB University Bogor16680 Indonesia Department of Smart Bio-Industrial Mechanical Engineering Kyungpook National University Daegu41566 Korea Republic of Department of Applied Biosciences Kyungpook National University Daegu41566 Korea Republic of Smart Agriculture Innovation Center Kyungpook National University Daegu41566 Korea Republic of

Early information concerning water, oil, and free fatty acid (FFA) content in palm fruit on-site is crucial in determining the commercial value of fresh fruit bunches (FFB) and maintaining oil palm quality. Conventional methods are destructive, labor-intensive, single-target, costly, and time-consuming. Therefore, this study aimed to develop a method for the multi-parameter prediction of oil palm fruit quality using near-infrared (NIR) spectroscopy. Empirical Wavelet Transform (EWT) and Gaussian Process Regression (GPR) were implemented as a novel chemometric method. A total of 750 fruit samples of the Tenera variety (Elaeis guineensis Jacq. var. tenera) with various maturity levels were collected from the Cikabayan Oil Palm Plantation. Each sample was scanned using an NIR instrument at 1000–1500 nm wavelengths to obtain absorbance data. The EWT was applied to decompose the NIR spectra into empirical modes, and GPR was used to build a regression model for fitting. Based on numerical analysis, the combination of EWT and GPR produced the root mean square error (RMSE) values of 2.877 ± 0.900 % (R2 = 0.955 ± 0.018), 1.256 ± 0.543 % (R2 = 0.942 ± 0.030), and 0.065 ± 0.04 % (R2 = 0.964 ± 0.044) for water, oil, and FFA content, respectively. The results showed that the model could accurately predict the internal quality of oil palm fruit without the need for solvents or reagents, supporting environmental sustainability. These were expected to enhance oil palm production management by improving quality control, optimizing harvest timing, and promoting sustainability across the value chain. © 2025 Elsevier B.V.

关键词： Near infrared spectroscopy

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The chemical composition of coconut sap at different tapping condition

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AIP Conference Proceedings 2023年第1期2586卷

作者： Pepita Haryanti Karseno Isti Handayani Susanto B. Sulistyo 1Food Technology Study Program Faculty of Agriculture Jenderal Soedirman University Purwokerto Indonesia 2Agricultural Engineering Study Program Faculty of Agriculture Jenderal Soedirman University Purwokerto Indonesia

Coconut sap is a sweet, transparent, oyster-white liquid derived by tapping coconut inflorescent. This tapping procedure is usually carried out twice a day, at night and during the day, for around 15 and 9 hours, respectively. Coconut sap is especially sensitive to spontaneous fermentation because it contains sugar with a pH close to neutral. Coconut farmers generally use preservatives to keep the sap’s quality throughout the tapping process. In this paper, a proximate analysis of coconut sap obtained during night (CTN) and in the daytime (CTD) were measured. The reducing sugar and amino acid profiles of coconut sap were also measured using High Performance Liquid Chromatography. This research aimed to determine the chemical composition of coconut sap at different tapping condition and variations of mangosteen peel powder concentration. The best preservative concentration of mangosteen peel powder was 0.84 g/L of coconut sap tapped during the daytime, according to the findings. This treatment yielded sap with chemical characteristic i.e. water content of 84.21%; ash content of 3.51% (db); protein content of 2.69% (db); total lipid of 0.10% (db) and total carbohydrate content of 94,62% (db). The CTN contained glucose and fructose of 0.85 and 1.04 g/100 g, respectively, higher than the CTD were 0.52 and 0.58 g/100 g. The CTN contained 13 amino acids which was lower than CTD.

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Grapenet_Lv1 for the Prediction of Optimum Hand-Picking Time of Nagano Purple Grapes

SSRN

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SSRN 2025年

作者： Islam, Md. Parvez Shinagawa, K. Hatou, K. Abdurrakhman, Arief Dian, K. Department of Biomechanical Systems Faculty of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan United Graduate School of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan Department of Instrumentation Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Business Mathematics Program Study School of Applied STEM Universitas Prasetiya Mulya BSD Campus BSD City Kav. Edutown I.1 Tangerang15339 Indonesia Indonesia

We investigated the optimum hand-picking time of Nagano Purple, a rare Japanese table grape variety. The color sensitivity between pure red–purple–black and pure purple–black makes it difficult for farmers to harvest these fruits at the right time while maintaining high sweetness level. We manually extracted 13 color parameters, namely, X, Y, Z, x, y, L*, a*, b*, L, a, b, C*, and h, from 355 grape skin samples using a colorimeter, which is a time-consuming and expensive process. In addition, we developed a 15-layer shallow long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM) neural network, namely, GrapeNet_Lv1, with a learnable parameter of 1.39E+06. This network is compact and requires low computational cost for training. Furthermore, it has the ability to prevent the problems of vanishing gradient and overfitting. The combination of all the 13 aforementioned parameters yielded promising results in predicting color and sweetness levels, with test accuracies of 0.83 and 0.76, respectively. In the prediction of color level, our proposed network GrapeNet_Lv1 achieves a high validation accuracy of 0.92, F1 score of 0.88, recall of 0.87, low validation loss of 0.39, and RMSE of 0.31. However, in the prediction of sweetness level, GrapeNet_Lv1 achieves a relatively low validation accuracy of 0.79, F1 score of 0.70, recall of 0.69, high validation loss of 0.72, and RMSE of 0.33. Further research can help improve the accuracy. GrapeNet_Lv1 can be applied to other crops, where, predicting the whose optimal hand-picking time is difficult to predict based on color recognition. Keywords: Neural network;LSTM-BLSTM;Nagano Purple, color, sweetness © 2025, The Authors. All rights reserved.

关键词： Long short-term memory

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Review of the use of mangrove forests in supporting the socio-economic life of fishing communities 2

Review of the use of mangrove forests in supporting the soci...

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2nd International Conference of Interdisciplinary Research on Green Environmental Approach for Sustainable Development, ICROEST 2020

作者： Untari Darma, R. Betaubun, P. Arief, A.A. Graduate School Hasanuddin University South Sulawesi Indonesia Department of Agribusiness Faculty of Agriculture Universitas Musamus Papua Indonesia Agribusiness Study Program Faculty of Agriculture Hasanuddin University South Sulawesi Indonesia Department of Civil Engineering Faculty of Engineering Universitas Musamus Papua Indonesia Socio Economic of Fisheries Study Program Faculty of Marine Science and Fisheries Hasanuddin University South Sulawesi Indonesia

Mangrove forests have an important function in human life that cannot be replaced by other functions. The existence of mangrove forests has six main functions, namely biological, ecological, physical, social, economic and chemical functions. This article discusses the description of mangrove forest management in meeting the needs of the economic and social functions of the fishing communities on the coast of Merauke Regency, Papua Province, which were reviewed descriptively using relevant literature studies. In terms of economic function, mangrove forests provide economic value for fishing communities because mangrove forests become habitat for crabs, fish and other marine biota that are captured and sold as the main source of family income. In addition, the use of mangrove forests by fishermen on the coast is used as firewood and building materials. The high utilization of mangrove forests by coastal communities that ignores ecological sustainability causes damage to mangrove forests that threatens the sustainability of ecological functions. Damage to mangrove forests in Merauke in the period 2000-2005 was recorded as wide as 2,416 ha while in the 2005-2010 period damage reached 2,233 ha of the total area of the Merauke mangrove forest which reached 216,001.95 Ha or 6.98% of the Merauke forest area. Based on this, the management of mangrove forest utilization needs to get more attention from various parties so that the ecological function of mangrove forests as a condition of sustainable development, especially for fishing communities on the coast, can be realized in Merauke Regency. © Published under licence by IOP Publishing Ltd.

关键词： Sustainable development

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Rapid and non-destructive determination of vitamin C and antioxidant activity of intact red chilies using visible near-infrared spectroscopy and machine learning tools

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Case Studies in Chemical and Environmental engineering 2023年 8卷

作者： Devianti Sufardi Hafsah, Siti Sariadi Ahmad, Fachraniah Arianti, Nunik Destria Saputra, Edo Hartuti, Sri Department of Agricultural Engineering Faculty of Agriculture Syiah Kuala University Banda Aceh 23111 Indonesia Department of Soil Science Faculty of Agriculture Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agrotechnology Faculty of Agriculture Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemical Engineering Politeknik Negeri Lhokseumawe Lhokseumawe 24301 Indonesia Department of Information System Nusa Putra University Sukabumi 43155 Indonesia Department of Agricultural Technology Faculty of Agriculture Universitas Riau Pekanbaru 28293 Indonesia Agricultural Engineering Study Program IPB University Bogor 16680 Indonesia

Red chili peppers are extensively used in the culinary industry due to their high vitamin C, antioxidant content, spice, and natural colorant properties. Traditional methods for determining these parameters are time-consuming and unstable. This study investigates the viability of visible near-infrared spectroscopy as a rapid and nondestructive method for determining vitamin C content and antioxidant activity in intact red chilies. Visible near-infrared spectroscopy measures the sample's light absorption, proportional to its chemical composition. Collecting intact red chili samples from various sources and determining their vitamin C content and antioxidant activity using conventional methods were required for the study. The visible near-infrared spectra of the pieces were collected using a spectrometer, and chemometric models were devised to correlate the spectra with the vitamin C content and antioxidant activity. Traditional methods were compared to visible near-infrared spectroscopy regarding their performance of prediction. In conjunction with chemometric models and machine learning algorithms, visible near-infrared spectroscopy could accurately predict red chilies' vitamin C content and antioxidant activity. The developed models exhibited high calibration and prediction coefficients, low root mean square errors, and high prediction-to-deviation ratios. The identified absorption peaks in the visible near-infrared spectra were related to the samples' color pigments and water content. This study demonstrates the feasibility of visible near-infrared spectroscopy as a rapid and nondestructive method for determining the vitamin C content and antioxidant activity of intact red chilies. The findings may have significant ramifications for the food industry, providing a more efficient and secure quality control and nutritional labeling method. It is recommended that additional research and validation be conducted to ensure the applicability of the developed models to var

关键词： Chemometrics tools Machine learning Red chilies Spectroscopy

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