Most municipal solid waste (MSW) is found to be dominated by organic debris, which has excellent potential as an energy source. However, the main problems of this material are poor planning, urban expansion, and lack of management skills. All these problems are presently being encountered by the regional governments of Jakarta, Bogor, Depok, Tangerang, and Bekasi city (known locally as Jabodetabek), Indonesia. In the MSW management system, a vital planning protocol is reportedly assessing suitable landfill sites for energy generation, although this selection process is still a complex task that should consider various factors, such as environmental, social and safety, and economic variables. Therefore, this study aims to examine various factors in determining a suitable location for landfills. It also aims to identify the various factors required for MSW energy generation. Based on this study, a multicriteria decision analysis (MCDA) approach was applied to weigh the factors determining the appropriate location. This approach is popular in decision-making due to evaluating the complexity of multidimensionality factors. The results showed that 3 factors and 14 subfactors were formulated and structured in the MCDA hierarchy, with their information obtained to create pairwise comparisons by 10 involved experts. In this study, the MCDA output was the weight value associated with a systematic priority level, indicating that the environment was the highest factor in determining a suitable landfill site for energy generation. In addition, the weight factors were used for overlay analysis, in determining the suitable site for future energy generation studies.

Obtaining soil water content and soil workability data using remote sensing technology with passive sensors has some limitations due to cloud cover, cloud shadow, haze and smoke. This study proposes a method for computing soil water content and soil workability over large areas, faster and in near real-time based on Sentinel-1A (SAR) data. Sample data collected from sugarcane plantations in the Kediri and Sidoarjo districts in East Java, Indonesia, were used to develop a mathematical model of the proposed method using multi-polynomial regression. The performance indicators of the model (RMSE, MAPE and accuracy) were calculated with the results of RMSE = 0.213 and 0.250, MAPE = 16.39% and 18.79%, and accuracy = 83.6% and 81.2% for the training and testing models, respectively. The distribution of soil water content and soil workability can be computed and visualized using a spatial map. The future contribution of this work is to develop a decision support system for the selection of appropriate machinery for sugarcane field operations based on the principles of precision agriculture.

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.

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.

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.

The measurement of the macronutrient values of an oil palm plantation is a complex and tedious task, particularly when dealing with large plantation areas. This situation complicates the process of the conventional measurement of nutrients by taking samples of oil palm leaves in the area being observed, causing delays in fertilizer recommendation and a lack of spatial diversity observation. Precision agriculture (PA) principles and approaches, which focus on assessing temporal and spatial variability, can be used to improve conventional measurement methods in terms of both accuracy and speed. This research aims to determine macronutrients, specifically nitrogen (N), phosphorus (P), and potassium (K) contents in oil palm leaves based on PA principles using the integration of remote sensing technology and machine learning to quickly obtain the macronutrient status from oil palm plantation areas. The Sentinel‐1A and Sentinel‐2A imagery data were analyzed and extracted to produce selected features, which are most influencing in the correlation between the imagery data and the leaf macronutrient values obtained from laboratory analysis. The random forest regression (RFR) model is used to produce correlation functions to compute macronutrient values. The use of the two satellites is to cope with cloud and smoke interference. The prototype system developed, named PreciPalm (Precision Agriculture Platform for Oil Palm), has been validated and implemented based on 2000 leaf sampling units representing several oil palm plantation areas in Indonesia, including Java, Sumatra, and Kalimantan. The observed system performance resulted in the measurement accuracy of 95.02%, 93.50%, and 82.52% for the nutrients N, P, and K, respectively. The novelty of PreciPalm is that it provides an ecosystem to transparently measure and observe the macronutrient status of oil palm in a timely, visual, spatial, and location‐specific manner, thereby improving oil palm nutritional management with more certainty and precision.