Stingless bee (Trigona sp.) cultivation presents significant potential for livestock diversification, improved food security, and rural economic development. Although this practice offers clear advantages, including low maintenance requirements, minimal environmental risk, and the high nutritional and commercial value of its honey, its adoption in Lempake Subdistrict, North Samarinda, East Kalimantan, remains limited. This situation is primarily due to a lack of community awareness and technical knowledge in apiculture. In response, a community service program was conducted on 10 May 2025 by lecturers from the Faculty of Agriculture, Mulawarman University, in collaboration with local farmer groups and supported by the Samarinda City Government. The program provided structured training through field visits, lectures, interactive discussions, and practical demonstrations at selected apiaries. Post-training evaluations showed a substantial improvement in participants’ understanding of colony management, hygienic honey harvesting, and the economic prospects of stingless bee products. Additionally, the program encouraged entrepreneurial interest and supported the development of value-added functional food products based on local resources. These findings indicate that targeted training and institutional collaboration are effective in promoting sustainable stingless beekeeping practices. Future initiatives should focus on continued technical support and the integration of apiculture products into local and digital markets to enhance competitiveness and economic impact.

A smart village is a modern development effort to overcome problems. A bright town is a principle that includes concept development and technology implementation. This principle helps villages overcome issues that occur in villages, such as environmental problems, sanitation, environmental pollution, infrastructure, technological backwardness, and others. For this reason, this final report discusses the implementation of smart villages in Teratak Village, Muara Kaman District, Kutai Kartanegara Regency. Research for Smart Village activities uses descriptive research methods consisting of activity targets, strategies, and implementation. The target of community service and Smart Village activities is, of course, the entire community of Teratak Village. All activities in the Smart Village are carried out in collaboration with officials, village officials, village organizations, and school residents, not forgetting the Teratak Village community. As for its implementation, Smart Village uses several different methods. Smart Village is an activity that consists of six main programs where the programs are grouped into superior group work programs, namely making directional signs, healthy living education, planting TOGA (Family Medicinal Plants), reactivation of social media accounts, activation of posyandu and dengue fever education. , and waste collection activities. Apart from that, there are also seven other work programs, namely socialization of TOGA planting, making village maps, digital literacy education, developing village profile websites, socializing the importance of fish in children's growth and development, stunting education, and socializing the use of waste with eco-bricks.

Increasing community literacy in environmental management is a strategic step to realize sustainable development. One practical approach that can be implemented is through community-based education. This article examines how local communities can become agents of change in increasing public awareness and understanding regarding environmental issues, such as waste management, natural resource conservation, and adaptation to climate change. Through a participatory approach, community-based education encourages direct community involvement in designing and implementing environmental programs that suit local needs. Case studies from various regions show that collaboration between communities, government, and the private sector can increase the effectiveness of educational programs and create long-term impacts. The work programs are processing organic and inorganic waste, processing agricultural land, planting trees, preventing dengue fever and making anti-mosquito spray, providing information related to Hoaxes and phishing link modes, and collecting data on MSMEs. Apart from that, there are also work programs regarding learning posts, introducing waste banks, making street signs, and creating village websites. The results obtained from this activity are an approach to the community through social activities carried out with residents and increasing community insight through the outreach and training programs provided. Apart from that, village facilities can be improved by creating street signs and village websites. It is hoped that the results of this study can become a practical guide in developing a sustainable community-based education model for environmental management.

The news about the proposal of the government of the Republic of Indonesia regarding the postponement of the 2024 elections is certainly an interesting discussion. In this research, sentiment analysis will be carried out on the issue of postponing the election. In this study, a dataset obtained using the crawling technique was obtained in the amount of 1280 tweet data about the postponement of the 2024 election. Data labeling in this study uses lexicon-based techniques with Indonesian dictionaries. By applying this technique, the details of the data in the positive class are 67.7%, namely 157 opinion data, and 32.3% negative, namely 75 opinion data. The sentiment classification system's training and test data yield a 9:1 ratio when the Naïve Bayes Classifier method is applied, and word weighting using TF-IDF yields an accuracy value of 91.67%, precision of 90.91%, recall of 100%, and f1-score of 95.24%.

Penilaian merupakan suatu proses yang dilakukan untuk memperoleh informasi yang diperlukan dalam mengambil keputusan tertentu. Namun, masyarakat sering mengalami kesulitan dalam memilih bus. Tujuan dari penelitian ini adalah untuk mengatasi masalah tersebut dengan menggunakan sistem pendukung keputusan yang menggabungkan metode AHP dan TOPSIS. Pendekatan ini telah terbukti meningkatkan p terhadap bus. Sistem pendukung keputusan berbasis web ini bertujuan untuk membantu masyarakat dalam memilih moda transportasi sesuai dengan kebutuhan mereka. Hasil penelitian menunjukkan bahwa penilaian terhadap bus merupakan masalah utama, dengan tingkat kejadian tertinggi mencapai 0,7494 yang memilki nilai oleh bus PT. NPM Medan. Pada penelitian ini memiliki 4 kriteria yaitu waktu perjalanan, keamanan, biaya/harga dan kemudahan. Penelitian ini memiliki 12 alternatif yaitu bus CV. Kota Pinang Baru, PT. Barumun, CV. Batang Pane Baru, PO. Medan Jaya, PT. Sentosa Intra, PT. Chandra, Bilah Pane Putra, Putra Melayu, PT. Rapi, PT. NPM Medan, Eldivo dan PT. ALS (Antar Lintas Sumatera). Diharapkan penelitian ini dapat memberikan kontribusi yang signifikan bagi masyarakat dalam memilih moda transportasi yang tepat serta memberikan informasi yang berharga bagi lembaga Terminal Amplas Medan.
 

Smarthome adalah sebuah sistem yang telah direncanakan dan dikembangkan dengan tujuan menyederhanakan beragam proses yang terjadi di dalam rumah. Dalam konteks ini, salah satu aspek utama yang diterapkan di rumah pintar adalah pemantauan pencahayaan ruangan. Pencahayaan merupakan faktor kunci dalam kenyamanan dan fungsionalitas kehidupan sehari-hari. Oleh karena itu, peran lampu dalam lingkungan rumah sangat dibutuhkan. Seringkali penghuni rumah cenderung lalai dalam mematikan lampu ketika tidak digunakan, hal ini dapat menyebabkan peningkatan konsumsi energi yang tidak efisien dan pemborosan sumber daya. Untuk mengatasi masalah ini, dibutuhkan teknologi yang mampu memantau dan mengendalikan pencahayaan secara otomatis. Pada penelitian ini, sebuah sistem pemantauan dan klasifikasi kondisi lampu dikembangkan dengan menerapkan metode naïve bayes. Dataset yang digunakan dalam penelitian ini terdiri dari 672 data latih dan 168 data uji. Pengujian dilakukan, dengan confusionmatrix, menghasilkan tingkat accuracy sebesar 98.71%, precision sebesar 98.16%, recall sebesar 98.42%, dan F-1 score sevesar 98.29%. Hasil penelitian ini menunjukkan bahwa metode naïve bayes sangat efektif dalam sistem pemantauan dan klasifikasi kondisi lampu lampu pada ruangan.