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All Journal REKADATA (Rekayasa Data dan Kecerdasan Artifisial) Rekam Kegiatan Pengabdian Masyarakat (REKAGAMA)
Erniza Erniza
ASMI Lhokseumawe
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Pemberdayaan UMKM Lokal Melalui Pelatihan Digital Marketing dan Branding Produk Erniza Erniza
REKAGAMA Vol. 1 No. 1 (2025): REKAGAMA (Rekam Kegiatan Pengabdian Masyarakat)
Publisher : CV Mazaya Cahaya Utama

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Abstract

Usaha Mikro, Kecil, dan Menengah memiliki peran penting dalam mendukung perekonomian daerah, namun masih menghadapi berbagai tantangan dalam memanfaatkan pemasaran berbasis digital. Banyak pelaku usaha belum memiliki kemampuan untuk mempromosikan produk secara efektif melalui media sosial, membuat konten yang menarik, atau membangun identitas merek yang kuat. Kondisi ini menyebabkan jangkauan pemasaran menjadi terbatas dan daya saing produk kurang optimal di tengah meningkatnya persaingan pasar online. Berdasarkan permasalahan tersebut, kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberdayakan pelaku Usaha Mikro, Kecil, dan Menengah lokal melalui pelatihan pemasaran digital dan penguatan strategi pencitraan merek. Program pelatihan dilaksanakan melalui metode penyuluhan, demonstrasi, praktik terstruktur, serta pendampingan intensif yang memungkinkan peserta memahami dan menerapkan keterampilan secara langsung. Materi pelatihan meliputi dasar-dasar pemasaran digital, pemanfaatan media sosial, teknik pembuatan konten kreatif, fotografi produk sederhana, serta perancangan elemen pencitraan merek seperti logo, warna, dan narasi merek. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta dalam memanfaatkan berbagai platform digital sebagai sarana promosi serta kemampuan untuk merancang identitas merek yang lebih konsisten. Dampak kegiatan terlihat dari meningkatnya motivasi peserta untuk mengembangkan usahanya secara berkelanjutan dan memperluas jangkauan pemasaran. Luaran yang dihasilkan mencakup modul pelatihan pemasaran digital, portofolio konten promosi peserta, serta rancangan identitas merek yang dapat digunakan sebagai dasar pengembangan usaha di masa mendatang. Secara keseluruhan, kegiatan ini memberikan kontribusi nyata dalam mendukung peningkatan daya saing Usaha Mikro, Kecil, dan Menengah lokal melalui pemanfaatan teknologi digital.
A BIG DATA ANALYTICS APPROACH FOR FORECASTING AGRICULTURAL COMMODITY PRICES Erniza Erniza
REKADATA Vol. 1 No. 1 (2025): Rekayasa Data dan Kecerdasan Artifisial (REKADATA)
Publisher : CV Mazaya Cahaya Utama

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Abstract

Agricultural commodity prices play a crucial role in economic stability and food security, particularly in developing countries such as Indonesia. Price volatility in key commodities such as rice, chili, and shallots often affects household expenditure, trade balance, and national inflation. Conventional forecasting methods are limited in capturing the complexity and scale of agricultural market data, which is often generated from multiple heterogeneous sources including government reports, wholesale markets, and social media. Big Data Analytics provides an opportunity to address these challenges by integrating large-scale datasets and applying advanced forecasting techniques to generate more accurate predictions. This study proposes a Big Data Analytics framework for forecasting agricultural commodity prices. The framework consists of four main stages: data acquisition from public datasets and online sources, data preprocessing and transformation using distributed computing systems, analytical modeling with machine learning algorithms, and visualization of price forecasts through interactive dashboards. The research implemented Apache Spark for data processing and applied time series forecasting models, including ARIMA and Long Short-Term Memory (LSTM) neural networks, to predict short-term price fluctuations. The experimental results indicate that LSTM outperformed ARIMA in terms of accuracy, with a Mean Absolute Percentage Error (MAPE) of 6.5% compared to 9.8% for ARIMA. Visualization of the forecasts provided clear insights into potential price increases, enabling policymakers, traders, and farmers to make proactive decisions. The novelty of this research lies in the integration of a distributed Big Data processing framework with predictive modeling tailored to agricultural commodity markets in Indonesia. In conclusion, the proposed Big Data Analytics approach demonstrates significant potential to improve forecasting accuracy and support decision-making in agricultural economics. The findings highlight the importance of adopting Big Data-driven solutions for enhancing national food security and market stability.
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