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Development of a chopper with performance optimization for areca nut frond using response surface methodology
(2023-09-01)
Bulan, Ramayanty
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Mustaqimah
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Arianti, Nunik Destria
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Yunus, Azwar
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Ibrahim, Akhyar
The chopper's performance is affected by the moisture content level of the material being chopped and the speed of the machine's rotation. This research aims to develop a chopper for an areca nut frond with performance optimization using RSM. RSM was utilized to assess the effect of areca nut frond moisture content (50.50 – 73.50%, w.b.) and machine rotation speed (1000 – 1600 rpm) on capacity, efficiency, chopping width, chopping length, and fuel consumption reactions. Next, the desirability formula is used to discover the moisture content and the machine rotation speed to maximize capacity and efficiency while minimizing losses. The optimum moisture content of 73.50% and machine rotation speed of 1600 rpm was performed in the highest capacity of 126.46 kg/hr at more increased efficiency (81.1%) and minimum chopping width and length (2.4 mm, 21.51 mm) and lower fuel consumption (0.91 ml). These findings showed that an appropriate machine performance could be completed by utilizing the moisture content of the areca nut frond and the machine's rotation speed.
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Vis-NIR spectra combined with machine learning for predicting soil nutrients in cropland from Aceh Province, Indonesia
(2022-12-01)
Devianti
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Sufardi
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Bulan, Ramayanty
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Sitorus, Agustami
Rapid analytical methods are needed to measure soil nutrient content in cropland, especially in Aceh Province, Indonesia. This is necessary for quick and accurate decision-making on the suitability of the land in terms of soil nutrients and the types of plants to be cultivated on its cropland. Visible near-infrared (Vis-NIR) spectroscopy with suitable chemometric methods through applied machine learning algorithms could be used to predict soil nutrients in the land of agriculture. The current study compared the implementations of machine learning algorithms (support vector machine for regression (SVR), partial least squares artificial neural network (PLS-ANN) and gradient-boosted tree regression (GBRT)) to predict soil nutrients (TN,TP, and TK content) in cropland in Aceh province (Indonesia). The approaches studied used three algorithms of machine learning with four preprocessing employed from spectral data. Samples (n = 102) of soil horizons (0–60 cm) were taken from ten regions in the province of Aceh (Indonesia) and the soil nutrient was measured, including the TN content by the Kjeldahl method and the TP and TK content by the Bray method. Their Vis-NIR spectra (400–2150 nm) were scanned after air drying and ground into powder. 71 examples were used to create the models, while the remaining 31 were used for validation. All of the machine learning algorithms tested as a chemometric approach yielded outstanding models for quantitative estimations of TN, TP, and TK content. Generally, the accuracy of the SVR models of the algorithm utilizing the full spectra was equivalent to that of the PLS-ANN models. Nevertheless, the ANN algorithm using reduced component spectral data (PLS-ANN) served more usefulness than the SVR algorithm depending on the preprocessing method. The most precise models for the content of TN, TP and TK were obtained using the GBRT algorithm (RPD = 2.64, 3.93 and 2.38 for the content of TN, TP and TK, respectively). The results demonstrate that Vis-NIR related to the machine learning algorithm is trustworthy to apply to measure the content of TN, TP, and TK in soil cropland.
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Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method
(2022-12-01)
Sitorus, Agustami
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Bulan, Ramayanty
In the present study, we developed a screening method for detection of adulteration in coconut milk via mid-infrared spectroscopy. Linear and nonlinear regression methods (principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR)) were employed and compared to achieve an optimal screening method. Spectral data were scanned using the FTIR benchtop with a wavelength range of 4000–16702 nm. The calibration models of the linear and nonlinear regression methods were developed using the leave-one-out cross-validation method before testing using predictive data that had been prepared. Furthermore, five spectral data treatment techniques were employed to improve the accuracy of the proposed calibration model. The results obtained show that the SVMR method is better than PCR and PLSR for the detection of adulteration in coconut milk by mid-infrared data spectroscopy. The coefficient of determination for calibration (R<sup>2</sup><inf>c</inf>) and prediction (R<sup>2</sup><inf>p</inf>), the root mean square error of calibration (RMSEC) and prediction (RMSEP) and the ratio of prediction to deviation (RPD) using the SVMR method were 100%, 0.81, 98.40%, 0.87 and 7.86, respectively. Furthermore, based on RPD analysis, it is known that the SVMR model can be used to perform excellent quality control of water-adulterated coconut milk.
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Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”
(2022-08-01)
Agustina, Sri
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Devianti
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Bulan, Ramayanty
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Muslih, Muhamad
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Sitorus, Agustami
pH is one of the important physical parameters to characterize mango damage because it can indicate changes in the structure and chemical content of the fruit. Thus, the present work evaluated the possibility of NIRs as a rapid and non-destructive tool for measuring the pH properties of intact mango from the cultivar "Arumanis" (Mangifera indica L.) using several algorithms for pre-processing, pre-treatment, and prediction. Three different algorithm predictions, namely principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), were used and compared to predict the pH of mangos. A total of 16 pre-processing and pretreatment algorithms are used to support algorithm prediction, and the results are also compared with the raw data spectra. The NIR spectral data used range from 1000 to 2500 nm. Algorithm performance will be evaluated using RMSE, error differences and concluded using RPD. The results show that the prediction of the PLSR algorithm can be performed with an RPD of 8.17, which is more significant than the PCR and SVMR algorithms, which are 1.04, and 1.90, respectively. To support this, pre-processing and pretreatment of the second derivative Savitzky–Golay is the best algorithm that can be used to predict the pH of the whole mango cultivar "Arumanis".
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Experimental investigation into the performance of cutting betel nut machine via response surface methodology and desirability function
(2022-01-01)
Bulan, Ramayanty
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Siregar, Kiman
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Wardhana, Muhammad Yuzan
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Lubis, Hamzah Hambali
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Thamren, Dewi Sartika
Cutting betel nut machines are increasingly being designed by engineers using local material. However, the performance of the cutting betel nut machine is influenced by the moisture content of the betel nut and the rotational speed of the machine. In this study, the performance of cutting a betel nut machine under moisture content of betel nut and rotational speed of the machine was studied using response surface methodology (RSM) and desirability function. Central Composite Design (CCD) coupled with RSM and desirability function was employed to evaluate the impact of moisture content of betel nut (34.68–50.54%, w.b.) and rotational speed (600–1000 rpm) on machine capacity (kg/hr), efficiency (%), and losses (%) responses. The desirability function was then used to optimize moisture content and rotational speed yielding maximum machine capacity and efficiency at lower losses. Three verification experiments were run to ensure the empirical relationships were valid. Optimum requirements of process parameters have been seen at which moisture content of 50.54% (w.b.) and rotational speed of 1000 rpm was achieved in maximum machine capacity of 44.16 kg/hr at higher efficiency (92.72%) and lower losses (6.31%). The model's conclusions were very consistent with the confirmed values. The results proved that an appropriate performance of the machine can be achieved using moisture content of betel nut and rotational speed of machine cutting betel nut.
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LOW COST TELEMONITORING TECHNOLOGY OF SEMISPHERICAL SOLAR DRYER FOR DRYING ARABICA COFFEE BEANS
(2022-01-01)
Pramono, Eko Kuncoro
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Karim, Mirwan Ardiansyah
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Fudholi, Ahmad
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Bulan, Ramayanty
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Lapcharoensuk, Ravipat
This study focusses on the development of a low-cost Internet of Things (IoT) system for semispherical solar dryers to dry arabica coffee beans. The temperatures and relative humidity (Rh) of a solar dryer room are measured using a DHT22 sensor module. The moisture content of hard arabica coffee beans is calculated by measuring the mass of the dried product using the load cell sensor module. All detected data are then sent using wireless networks and saved on a database cloud server. Tests are conducted to evaluate the uniformity of the DHT22 sensor module in a semispherical solar dryer, measure the temperature and Rh and reduce the mass of the dried coffee beans. The performance of the DHT22 sensor module at the uniformity testing stage shows promising results in terms of temperature and Rh, with standard deviations of 0.46 and 3.55, respectively. In addition, the performance of the semispherical solar dryer in relation to the drying kinetics of arabica coffee beans is evaluated. Arabica coffee beans are dried from 49.59% (w.b.) to 10% (w.b.) moisture content within 69 h. In addition, the drying kinetics of coffee arabica beans are investigated. Three models are compared with experimental data on arabica coffee beans dried in a semispherical solar dryer. The Page model is selected to represent the thin layer drying behaviour of arabica coffee beans.
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MEASUREMENT OF SOIL REACTION FORCES BY A SINGLE FLAT WHEEL ON THE SLOPE SOIL BIN
(2022-01-01)
Cebro, Irwin Syahri
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Bulan, Ramayanty
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Sitorus, Agustami
The ability of a cage wheel to climb slopes depends on the grip of the lug on the sloped soil. A lug wheel that sinks deeper will generate more force when climbing. This study aimed to measure the soil reaction force on a single flat lug wheel in the slope soil bin so that the optimal lug angle is obtained to enhance the ability of cage wheels to climb sloped soil. The experiment was carried out using a model of a single flat lug wheel (length of 10 cm, width of 3.5 cm, radius of 30.4 cm, and slope of 30o). Measurements of soil reaction forces on the lug were carried out on five lug angles (-15°, 0°, 15°, 30°, 45°) at three various sinkage depths (2.5 cm, 5 cm, 7.5 cm) at a rotational speed of 11 rpm. The moisture content, density, porosity used in this study were 45.61% (d.b.), 1.02 (g/cm), 44.02%. In addition, the particle size distribution of sand, clay, and silt are 8.038%, 13.396%, and 78.564%, respectively. The limits of soil consistency used in this study, including the plastic limit, water limit, and index limit, were 64.57%, 80.86%, and 16.29%, respectively. A set of measurement and data logger equipment is used to measure the reaction forces of the soil. The results showed that at the angles of -15° and 0° the lugs were able to penetrate the soil faster and deeper to produce a greater pulling force. This implies that a smaller angle of inclination will be more beneficial to the tractors wheel applied for climbing than a greater angle of inclination.

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