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Access type: UnknownAuthor: search.filters.author.Arianti, Nunik Destria

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    An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data
    (2023-09-01)
    Arianti, Nunik Destria
    ;
    Saputra, Edo
    ;
    Sitorus, Agustami
    Pre-processing near-infrared (NIR) spectral data is indispensable in multivariate analysis, since the measured spectra of complex samples are often subject to overwhelming background, light scattering, varying noises, and other unexpected factors. Various pre-processing methods have been developed to remove or reduce the interference of these effects. Until now, most applications of NIR spectra pre-processing in multivariate calibration have been trial-and-error, with selecting a proper method depending on the nature of the data, expertise, and practitioner experience. Thus, it is usually challenging to determine the best pre-processing method for a given data. In order to tackle these problems, this study proposes a new concept of data pre-processing, namely, automatically generating a pre-processing strategy (AGoES). This concept belongs to the ensemble pre-processing method, where machine learning algorithms (PLSR, SVM, k-NN, DT, AB, and GPR) built on differently preprocessed data are combined by 5-fold cross-validation and grid search optimization. To investigate our concept, a public NIR spectral dataset was used to predict three responses, including dry matter content (DM), organic matter content (OM) and ammonium nitrogen content (AN) from manure organic waste. The results show that SVM is the best algorithm combined with the AGoES pre-processing to predict DM and AN with a ratio of prediction to deviation (RPD) of 3.619 and 2.996, respectively. The AB tandem with AGoES pre-processing is the best strategy for predicting OM with an RPD of 3.185. Therefore, in the framework of the AGoES concept, it is unsupervised pre-processing, more simple, and feasible to apply multivariate analysis using machine learning algorithms.
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    Development of a chopper with performance optimization for areca nut frond using response surface methodology
    (2023-09-01)
    Bulan, Ramayanty
    ;
    Mustaqimah
    ;
    Arianti, Nunik Destria
    ;
    Yunus, Azwar
    ;
    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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    Influence of Biopores Infiltration Holes on the Level Erosion in Oil Palm Plantations Area
    (2022-04-01)
    Devianti
    ;
    Irwansyah
    ;
    Yunus, Yuswar
    ;
    Arianti, Nunik Destria
    ;
    Thamren, Dewi Sartika
    The extension of oil palm plantations in Indonesia has encroached on land with a slope of more than 15%, which impacts the possibility of erosion and landslides. However, conservation efforts to reduce erosion are yet to be fully established. Therefore, the present work studies the effect of biopores infiltration holes on erosion at oil palm plantations area with land slopes greater than 15%. Besides, surface runoff with erosion is modeled to find the relationship. The method used in this study is to combine mechanical conservation methods in the form of biopores infiltration holes in an area that already has cover crops, such as Mucuna Bracteata. Two experimental plots were designed, namely (i) plots without biopores infiltration holes and (ii) plots with biopores infiltration holes. The results showed that soil erodibility in the oil palm plantations area was of a high of 0.65. Applying biopores infiltration holes in the field reduced surface runoff and erosion rates by 31.81% and 29.66%, respectively. The relationship of surface runoff with erosion rates on the land shows a very close case where the coefficient of determination in each plot is 0.96 and 0.92.