This paper presents the spectroscopic dataset, pre-processing, calibration, and predicted model database of Fourier transform infrared (FTIR) spectroscopy used to detect adulterated coconut milk with water. Absorbance spectral data were acquired and recorded in wavelength range from 2500 to 4000 nm for a total of 43 coconut milk samples. Coconut milk ware prepared in three forms of adulteration. Coconut milk comes from traditional markets and instant coconut milk in Indonesia. Spectra data may also be pre-processed to increase prediction accuracy, robustness performance using normalize, multiplicative scatter correction (MSC), standard normal variate (SNV), 1st derivative, 2nd derivative, and combination of 1st derivative and MSC. Calibration models and cross-validation to forecast those adulteration parameters use two regression algorithms, i.e., principal component regression (PCR) and partial least square regression (PLSR). By looking at its statistical metrics, prediction efficiency can be measured and justified (correlation coefficient (r), correlation of determination (R2), and root mean square error (RMSE)). Obtained FTIR datasets and models can be used as a non-invasive method to predict and determine adulteration on coconut milk.

Moisture content in the process of drying is often unknown when carrying out the drying process, especially the fluidized dryer. A lot of experimental designs are needed when observing the drying phenomenon more deeply. It is because to stop and repeat drying process from the beginning again when the sample is taken to test its moisture content needed more experiments. Therefore, this paper presents development of a non-intrusive moisture measurement system prepared for fluidization type dryers. The method used in to conduct this research consists of (i) structural design analysis and (ii) functional (mechanical and electrical systems) and (iii) simple testing of the water content measurement system of constructed material. Test parameters observed include errors in measuring and fluctuating sensor signals against vibration applied to the weighing system. The results showed that non-intrusive moisture content measurement system for fluidized dryers based on the ESP8266 microcontroller had been successfully developed and worked normally. The measurement system has been calibrated with a coefficient of determination (R2) close to one. Measurement error resulting from the effect of vibration on this system shows a very satisfactory value of 6.89%.

Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis–SWNIR) spectroscopy (400–1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis–SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications.

Hand tractor is the driving source of popular agricultural equipment in Indonesia. Therefore, the hand tractor can be used as a source of power harvesting machine. One of the harvest tools that need to be designed today is the harvesting machine for Chinese spinach (Ipomoea reptans Poir.). The most important unit of a harvesting machine is the knife. The success of a harvesting machine design is found in the shape and material of the blade used. Experimental research was more expensive and more time. Therefore, this paper describes a conceptual design of knife for Chinese spinach harvesting machine through a CAD approach. The best blade shape is determined by the angle of the blade and the strength of the blade is best determined by the material. The simulated blade angle is 15°, 45° and 60° to 1060 Alloy meter, AISI 316 SS, Phenol-Formaldehyde Resins (PF). The maximum force required for cutting of Chinese spinach is given 0.1019 kgf. The simulation results using FEM show that the greater the angle of the blade will be greater to receive stress and displacement. The simulation results also show that the use of AISI 316 SS material can be used as a blade of Chinese spinach harvesting machine.

Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

The application of mechanization for waste handling in oil palm plantations should be done due to abundant waste of oil palm fronds (OPF). This study aims to examine the feasibility of applying mechanization in the handling of oil palm fronds with a dynamic model approach in the province of Aceh, Indonesia. The mechanization implementation model is designed with the placement scenario of its centralized and decentralized processing equipment. The results show that the mechanization system approach can be implemented in handle oil palm fronds waste. The management of the equipment with the current centralization shows the best scenario model. The results are expected to be a reference for oil palm planters to be able to apply their waste treatment with a mechanized approach of centralization.