This study examines the problem of the success rate of computer course program participants at LKP Mitra Prestasi which is still an obstacle for institutional administrators. By applying a hybrid model approach in machine learning, which combines Random Forest and Long Short-Term Memory (LSTM) algorithms through the assembly merging technique, this study aims to develop a predictive model that is able to estimate the success rate of participants and recognize variables that contribute to the achievement of learning outcomes. The probability outputs from both models are used as an input feature for the Logistic Regression meta-classifier that studies the optimal combination of predictions to produce final graduation decisions. This approach leverages the Random Forest's advantages in recognizing static features and LSTM in recognizing temporal patterns. Research data was obtained from 100 course participants which included demographic information, academic performance, as well as data on the order of attendance and assignment submission. The research findings show that the hybrid model with the stacking ensemble built succeeded in achieving 95% accuracy, 95% precision, 100% recall, and 97.44% F1 score, with the main variables affecting graduation being final exam scores, attendance percentage, and accuracy of assignment submission.

Urban farming in Pakintelan Village, Gunungpati District, Semarang, was initiated to overcome food security challenges and improve local economic welfare through the use of limited land in urban areas. The main problem faced is the low level of community participation in agricultural activities and a lack of understanding of sustainable agricultural practices. This program involves UNPAND KKN students in providing training in farming techniques, organic pest management, and the use of compost fertilizer. The methods applied include direct training and mentoring by extension workers, routine monitoring and evaluation, and outreach through local communities. Apart from that, the program also encourages the processing of harvests into value-added products and the formation of small farmer groups to strengthen community involvement. As a result, this program has succeeded in increasing community participation, maintaining environmental sustainability, and contributing to the economy with the potential to use harvests for consumption or sale. It is hoped that the replication of the program in other areas will expand its impact, making urban farming a sustainable and inclusive model of urban agriculture.