Fake news or commonly known as a hoax has become one of the most visible cybercrime. Hoax news dissemination harms the social community, such as raising hatred towards something both individuals and groups. This paper is to classify amongst hoaxes and valid news utilizing Extreme Gradient Boosting (XGBoost) method in this research based on Indonesian news. The dataset used is Indonesian news about Indonesia itself and the world from 2015 to early 2020. The study used 500 news data including 250 valid news and 250 hoax news, divided into 80% training data and 20% test data. The result of this study shows that the machine learning model created using XGBoost has an accuracy value of 89%, with the precision value of 90% and recall value 80%.

POSBINDU PTM Sejahtera is a health post that aims to increase awareness of the elderly in preventing non-communicable diseases. According to Departemen Kesehatan, this disease can be caused by food consumption. The food consumed must include vegetables and fruit to enhance the concept of active aging. However, these recommendations are not comparable with the POSBINDU PTM screening data. Screening data mentioned that only 3.5% of 73 elderly people consume vegetables and fruit three times a day. Factors that inhibit the elderly in consuming healthy food are the officers only giving food abstinence advice and expensive food staples. The problem of this optimization model can be solved by the artificial intelligence algorithm, Particle Swarm Optimization (PSO). The results of this study, PSO can provide varied food recommendations at a minimal price (optimization model). The calorie and carbohydrate content gets a value of <10% of the nutritional needs of the elderly, while the protein and fat content produce a greater difference of > 10%. The average price of foodstuffs produced by the PSO algorithm is Rp.50,965.

Currently, hydroponic vegetables have become a trend because of its efficient construction requires a minimum resource management. Determining the correct type of hydroponic vegetable before planting would affect the yield of the vegetables produced. However, the experiments conducted in this research resulted in deadlocks to determine the exact type of vegetable cultivated at the farm where the type of hydroponic vegetable depends on several factors that affect the quality and quantity, size weights, the number of leaves and the weight of plants. A decision support system is applied as a solution to the problem and IoT is performed to gain criteria data input. AHP method is conducted to measure criteria such as raw water PH, PPM of a nutritional solution, air temperature and sunlight illumination intensity and to find alternatives determined namely, lettuce, Pakcoy, Mustard greens, Spinach, Kale, Celery, and Chinese Kale. Results showed that Pakcoy in the first rank with a value of 0.25% and the second is spinach with a value of 0.16%, the Decision support system has proven to determine the type of vegetable on hydroponic vegetables.

Nutritious Garden Trilogy University is an agricultural land managed by urban farmers as well as students who are also lecturers to practice teaching and learn in the cultivation of types of plant commodities. Cultivation of plant species done by examining the climatic factors of plants on the land. The sustainability reason is one of the obstacles to make sure the results of cultivation in the teaching and learning process. The changing climate makes urban farmers get trouble to determine types of plants to be planted. This study will develop a system to forgive the recommendation of the type of plant according to the change of climate. The climate changes recorded using the internet of things sensors which consist of temperature, humidity, light intensity, and wind speed. The data entered will be processed using the triple exponential smoothing method as a forecast to predict future weather, then classified using the k-nearest neighbor to get the types of plants. The results of forecasting testing from sensors using the mean absolute percentage error obtained values of 9.53% temperature, 16.44% humidity, 3.73% light intensity, 19.42% wind speed.