This study compares the performance of two deep learning models, namely Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN), in the task of recognizing human activity from videos. Human activity recognition is an important field in computer vision with many applications, such as security monitoring, human-computer interaction, and social media-based video analysis. ConvLSTM is a model that combines convolution operations with long-term memory LSTM, thus capable of capturing spatial and temporal information simultaneously. This approach is ideal for processing video data sequences that have spatial and temporal dimensions. On the other hand, LRCN combines the power of spatial feature extraction from Convolutional Neural Network (CNN) and temporal sequence modeling through Recurrent Neural Network (RNN), specifically LSTM, to understand movement patterns in videos. The study used the UCF50 dataset consisting of 50 activity classes, but was limited to five classes for the focus of the experiment. The dataset was divided into 80% for training and 20% for testing, and the model was drilled for 50 epochs using early stopping to prevent overfitting. The results show that both models have high training performance. ConvLSTM achieved a training accuracy of around 98% and a validation accuracy of 90%, while LRCN achieved a training accuracy of 99.5% and a validation accuracy of 88%. Although ConvLSTM demonstrated good stability on the validation data, further testing using TikTok videos as real-world data showed that LRCN had a higher confidence level in recognizing activities, with most predictions achieving confidence scores above 80%. This difference in performance indicates that while ConvLSTM excels in generalizing on training data, LRCN is more robust to real-world data variations.

This community service aims to develop superior agribusiness products based on local wisdom in Batuputih Laok Village, Batuputih Subdistrict, Sumenep Regency. The implementation method includes five main stages: needs identification and analysis, program and action plan development, training and counseling, program implementation, and monitoring, evaluation, and follow-up. The results show a significant increase in agricultural productivity and quality due to the integration of modern agricultural technology with local knowledge. Corn productivity increased by 30%, and the quality of tobacco produced improved, leading to higher selling prices. Support from the government, research institutions, the private sector, and active community participation were key factors in the program's success. In conclusion, this program successfully improved farmers' welfare and strengthened the village's agribusiness capacity sustainably.

Upaya melakukan analisis emosi pada teks komentar mahasiswa dalam evaluasi pembelajaran sangat penting dilakukan. Komentar dalam kuesener umumnya tidak diolah, padahal  data tersebut mengandung informasi dalam mengungkap emosi mahasiswa dalam proses pembelajaran.  Untuk itu deteksi dan klasifikasi emosi pada opini mahasiswa  dapat memperbaiki hasil kuesioner. Penelitian ini bertujuan menerapkan metode klasifikasi emosi pada teks komentar mahasiswa berbasis pada leksikon emosi  dari NRC Emolex.  Jenis emosi yang akan dideteksi adalah 8 jenis emosi, yaitu marah (Anger), antisipasi (anticipation), jijik (disgust), takut (fear), bahagia (joy), sedih (sadness), terkejut (surprise)   dan yakin (trust) . Data diambil dari komentar dan saran mahasiswa pada kuesioner pada Universitas  AKPRIND Indonesia tahun 2020-2022  sebanyak 4000 data yang telah dilabeli secara manual.   Tujuan lain dari studi ini adalah melihat sejauh mana efektivitas leksikon emosi Emolex untuk klasifikasi emosi teks kuesioner akademis. Hasil penelitian menunjukkan rata-rata akurasi sebesar 56,2%. Dari yang diketahui label emosinya 3 prosentase tertinggi ada pada label Sadness (19,2%), Joy(16,7%) dan Fear (13,5%) yang masing-masing memiliki akurasi 72%, 68% dan 68%. Dari penelitian terungkap bahwa kinerja Emolex untuk klasifikasi emosi masih kurang memuaskan dan memerlukan pengembangan leksikon lebih jauh lagi.