Claim Missing Document
Check
Articles

Pembuatan Konten Promosi di Media Sosial untuk Produk Susu Kambing Etawa Zabidi, Yasrin; Nurdin, Riani; Astuti, Marni; Santoso, Prasidananto Nur; Gunawan, Gunawan; Rengganis, Esa; Mauidzoh, Uyuunul; Poerwanto, Eko; Budiwidodo, Sidik
Mestaka: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2026): APRIL 2026
Publisher : Pakis Journal Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58184/mestaka.v5i2.922

Abstract

In today's digital era, promotional activities must follow digital marketing trends. One form of digital marketing is utilizing social media to promote products. The problem faced by the Etawa Goat Milk MSME was the lack of engaging promotional content on social media to introduce the product and increase its awareness among the wider public. Utilizing social media aims to make it easier for brands to connect with customers. The solution to this problem is to create engaging promotional content for publication on social media. The goal of this activity is to create engaging promotional content for publication on Instagram posts, stories, and reels, Facebook, TikTok, and WhatsApp.
Radar-based gesture recognition simulation for unmanned aerial vehicles command interpretation Dermawan, Denny; Kurniawan, Freddy; Astuti, Yenni; Setiawan, Paulus; Lasmadi, Lasmadi; Mauidzoh, Uyuunul; Sudibya, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1227-1235

Abstract

Radar-based gesture recognition has emerged as a robust alternative to vision-based systems, particularly in environments where lighting and privacy pose challenges. This study presents a simulation approach for recognizing hand gestures to control unmanned aerial vehicles (UAVs) using radar signals. Five discrete gestures, i.e., TakeOff, Land, MoveForward, TurnLeft, and stop, were defined and modeled in MATLAB to generate synthetic radar signals. From each sample, four time-frequency domain features were extracted: duration, maximum amplitude, dominant frequency, and root mean square (RMS). A dataset of 500 samples (100 per class) was classified using three supervised learning models: support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree. The k-NN classifier achieved the highest accuracy of 96%, demonstrating the feasibility of lightweight classifiers for gesture recognition using low-complexity features. These results highlight the potential of radar-based interfaces to replace traditional remote controls in UAV operation. The proposed simulation framework contributes to the development of intuitive, non-contact human-machine interaction systems.