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Diversifikasi Produk Susu Sapi Lokal melalui Pengolahan Keju Mozzarella dan Soft Candy di Ciawi, Bogor Irma Isnafia Arief; Nurul Hidayati; Zaenal Abidin; Tjut Awaliyah Zuraiyah
Agrokreatif: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 12 No. 2 (2026): Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agrokreatif.12.2.228-240

Abstract

Ciawi, Bogor, represents a region with high potential in the agricultural sector, particularly dairy farming, where approximately 20% of local farmers are engaged in milk production. This condition provides opportunities for developing home industries that transform cow’s milk into value-added products. In response, Institut Pertanian Bogor (IPB), as a leading educational and research institution in Indonesia, has collaborated with PT. Bogor Sari Nutrisi (a local MSME/ Micro, Small and Medium Entreprises) and CV. Wahyu Farm Sejahtera (a dairy farm) to establish a program aimed at producing mozzarella cheese and soft candy from locally sourced cow’s milk. The project was implemented through a comprehensive situational analysis of local challenges and opportunities, dissemination of university-based innovations to industry, and collaborative partnerships with local enterprises. The initiative has resulted in improvements in production capacity and product quality, positioning mozzarella cheese and soft candy as potential regional specialty products. These findings demonstrate that innovation in milk processing can enhance the added value of local agricultural commodities, expand market opportunities, and contribute positively to regional economic development.
Prediction and Analysis of Factors Affecting Marketplace Sales Using a Bidirectional LSTM Model for Inventory Estimation Indah Cahyani; Tjut Awaliyah Zuraiyah; Aries Maesya; Fitri Mintarsih
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.96

Abstract

Forecasting weekly sales at the product level can help marketplace sellers avoid both excess inventory and stock shortages. The purpose of this study is to investigate the sales forecasting of the Shopee store antianshop by applying the Bidirectional Long Short-Term Memory (BiLSTM) method in conjunction with correlation-based feature selection to predict the inventory. The raw dataset contained 5,426 weekly product records from Shopee Seller Centre covering 22 May 2023 to 31 May 2025; 5,312 records remained after preprocessing. For each product, the data were ordered by week and converted into ten-week input sequences. Pearson correlation showed that Add to Cart (r = 0.5835$) and Enter Cart (r = 0.5579$) were the strongest retained predictors of weekly Units Sold. BiLSTM was then compared with a unidirectional LSTM under the same experimental settings for ten products. LSTM recorded slightly lower average errors, with MAE of 1.7462 units, RMSE of 2.3977 units, and non-zero MAPE of 69.57%, while BiLSTM produced 1.8505 units, 2.4611 units, and 71.18%, respectively. The results indicate that BiLSTM was competitive but did not outperform the simpler LSTM model consistently. We combined the two models in a Streamlit dashboard that shows product forecasts and weekly inventory guidance. Generalizability of the findings should be done cautiously since the analysis used one store, relatively short product histories, and no systematic hyperparameter search.