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Pemanfaatan teknologi blockchain pada kelompok tani melalui solusi inovatif Supply Chain Finance (SCF) Anita Sindar Sinaga; Nuraisana Nuraisana; R. Mahdalena Simanjorang; Amalia Rossa; Dini Auliah
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 10, No 3 (2026): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v10i3.39577

Abstract

Abstrak Keterbatasan akses pembiayaan menjadi kendala utama bagi kelompok tani pada musim tanam padi. Pada musim panen tanpa ada monitoring dari pemerintahan desa harga gabah maupun beras menjadi tidak stabil. Blockchain menawarkan sistem pencatatan transaksi yang terdesentralisasi, transparan, dan sulit dimanipulasi. Pengawasan harga penjualan padi dapat dimonitoring langsung pada kelompok tani desa melalui pencatatan transaksi secara transparan dan real time. Pengawasan harga padi di tingkat desa umumnya dilakukan secara manual melalui papan informasi kelompok tani (posko), musyawarah warga atau rembug desa. Penting edukasi bagi pengurus kelompok tani mengenai cara kerja buku besar digital dan pentingnya integritas data. Pemerintah Desa (Pemdes) dapat bertindak sebagai salah satu pengawas dalam sistem. Pemdes mendapatkan data harga yang akurat dan tidak bisa dimanipulasi, sehingga bisa langsung mendeteksi jika harga gabah di bawah harga pembelian pemerintah. Kegiatan pemanfaatan teknologi terkini seperti Blockchain digabungkan dengan sistem pembiayaan SCF pada bidang pertanian dapat meningkatkan perputaran uang dikalangan petani sehingga kesejahteraan petani tercapai. Capaian dari penggunaan teknologi ini pada pengawasan harga padi dan beras dikalangan anggota kelompok tani pada setiap musim. Kata kunci: solusi inovatif; kelompok tani; teknologi blockchain; SCF. Abstract Limited access to financing is a major obstacle for farmer groups during the rice planting season. During the harvest season, without monitoring from the village government, the price of unhusked rice and rice is unstable. Blockchain offers a decentralized, transparent, and difficult-to-manipulate transaction recording system. Monitoring of rice sales prices can be directly monitored by village farmer groups through transparent and real-time transaction recording. Monitoring of rice prices at the village level is generally carried out manually through farmer group information boards (posko), community meetings or village discussions. Educating farmer group administrators about how digital ledgers work and the importance of data integrity is crucial. Pemdes can act as one of the supervisors in the system. Pemdes obtains accurate, unmanipulated price data, enabling them to immediately detect if the price of unhusked rice is below the government's purchase price. Utilizing the latest technology, such as Blockchain, combined with the SCF financing system in agriculture, can increase cash flow among farmers, thereby achieving farmer welfare. The achievements of using this technology in monitoring the price of rice and paddy among members of farmer groups in each season. Keywords: innovative solutions; farmer groups; blockchain technology; SCF.  
Predictive Analytics of Food Retail Seasonal Trends with Advanced Forecasting Modeling Anita Sindar Sinaga; Dameria Esterlina Br Jabat; Amalia Rossa; Dini Auliah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13173

Abstract

Food sales in food retailers generally increase on certain days. Three food categories served as data sources in this study: staple foods, ready-to-eat foods, and dairy products. Predictive analysis of seasonal trends in food retailers shows that macroeconomic factors, seasonal patterns, and religious holiday indicators play a significant role in shaping sales. Staples is the highest-revenue category, while frozen foods has the lowest volume of the three. Each highlighted sector, including dairy, is expected to experience a measurable increase in turnover over the coming period. All models exhibit varying accuracy in predicting 2026 sales compared to actual 2025 sales, evaluated using MAPE, RMSE, and MAE for key products. Moving Average and LSTM tend to be conservative, while ETS and ARIMA are more optimistic but remain limited by limited data. Random Forest also struggles to capture complex relationships. Prophet stands out for its ability to incorporate exogenous variables and handle seasonality, although caution is needed when interpreting future values. The MAPE values ranged from 1.89% to 5.34%, indicating excellent predictive accuracy, as MAPE values below 10% are generally considered high accuracy. The low RMSE and MAE values also indicate a relatively small difference between the 2026 prediction and the actual 2025 values.