Jabodetabek has been termed Indonesia’s most populous urban area, and it is predicted to generate around 9.069 million tons/year by 2023. On the other hand, this area has promising agricultural prospects and requires a great deal of energy. Tractors, generators, and other agricultural equipment are necessary to increase productivity. Therefore, it can potentially convert waste into energy as an effective alternative fuel for agriculture. This study looked at ways to recover the waste to energy (WtE) with various technologies (incineration, gasification, pyrolysis, landfilling, and anaerobic digestion). The assessment indicators focus on the type of waste, energy production efficiency, energy produced, technological readiness, capacity, byproducts, costs, air pollution, water pollution, and soil pollution. Each WtE technology is assessed using a scale between 1 (worst) and 5 (best) on each performance indicator concerning related references. Based on the analysis, the anaerobic digester is the most attractive WtE technology to generate an alternative fuel for agriculture in the Jabodetabek area. This is an environmentally friendly and economical technology because the technology readiness level is relatively mature. Thus, this review will assist policymakers and implementing authorities in determining the potential of the right WtE technologies for effective alternative fuel generation for agriculture, particularly in the metropolitan region of Jabodetabek.

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia.

Porang is well known with its high glucomannan content. Determination of the chemical content of porang flour is commonly carried out by chemical methods in the laboratory, which are known to take a long time and expensive. This study aims to determine the chemical content of porang flour using near infrared spectroscopy (NIRS). The reflectance of porang flour was measured using an FT NIR spectrometer at a wavelength of 1000 to 2500 nm. The chemical content (water and glucomannan content) of porang flour was determined by oven and spectrophotometry as the reference method. The reflectance was transformed to absorbance and several spectral pre-treatments such as normalisation, detrending, standard normal variate (SNV), orthogonal signal correction (OSC), and multiplicative scatter correction (MSC) with various PLS and PCR factors number were applied to obtain the best chemical content prediction of porang flour with NIRS. PLS and PCR were used for calibration of NIR spectra and chemical content of porang flour. The best calibration for determining the moisture content of porang flour was obtained using NIR spectra with normalisation pre-treatment and 8 PLS factor (r=0.92; SEC=0.15%; CV=2.13%; RPD=2.68). The best calibration for determining the glucomannan content of porang flour was NIR spectra with detrending pre-treatment at PLS factor 7 (r=0.86; SEC=0.54%; CV=0.90%; RPD=1.62). A lower accuracy was obtained for predicting water and glucomannan content using PCR calibration method. For water content, the best calibration was obtained by NIR spectra with normalisation pre-treatment and 12 PCR factors (r=0.91; SEC=0.16%; CV=2.21%; RPD=2.59). For glucomannan content, the best calibration was NIR spectra using detrending pre-treatment at 9 PCR factors (r=0.81; SEC=0.62%; CV=0.94%; RPD=1.55). This study shows that NIRS can be used to determine the chemical content of porang flour.

The study used near-infrared reflectance spectroscopy and hybrid calibration methods to predict oil and free fatty acid content of oil palm fruitlets non-destructively. The reflectance and chemical content of oil palm fruitlets were measured and the calibration between near infrared spectra and chemical content was performed using hybrid calibration methods. The developed hybrid calibration model was validated to predict oil and free fatty acid of other oil palm fruitlets. The best models to predict oil and free fatty acid of oil palm fruitlets respectively were the hybrid model of Partial Least Square-Artificial Neural Network with 25 factor components (R2 = 0.96; SEP= 2.21%, RPD = 4.79) and 19 factor components (R2 = 0.96; SEP= 0.25%, RPD = 4.24) using Savitzky-Golay first derivative spectra pre-treatment.

An accurate method for determining palm fruit maturity is needed by the palm oil industry to increase productivity and quality of crude palm oil. This study developed a portable instrument based on the AS7265x sensor (410–940 nm) to determine oil palm maturity accurately. Reflectance from 270 oil palm fruits at various maturity were measured, pre-processed, and classified into three maturity levels using PCA. PCA with SNV pre-treatment explained 97% of the variance. Classification was validated using SVM, RF, and KNN, with KNN achieving 100% accuracy. The instrument with SNV-PCA-KNN method has the potential to be used for oil palm fruit maturity classification.