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All Journal bit-Tech Al Khidma: Jurnal Pengabdian Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu
Trimono Trimono
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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BUDIKDAMBER: Inovasi Budidaya Lele dan Sayuran dalam Ember Untuk Masyarakat RW 04 Kelurahan Sambikerep, Kota Surabaya Nevia Desinta Putri; Arrum Marwani; Mochammad Abudrrochman Faiz; Bagus Widduro; Trimono Trimono
Al Khidma: Jurnal Pengabdian Masyarakat Al Khidma Vol. 6 No. 1 Januari - Maret 2026
Publisher : Sekolah Tinggi Ilmu Al-Qur'an (STIQ) Amuntai Kalimantan Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35931/ak.v6i1.5799

Abstract

Budikdamber (Budidaya Ikan dan Sayuran dalam Ember) merupakan inovasi budidaya akuaponik sederhana yang memadukan budidaya ikan lele dan sayuran dalam satu wadah ember di wilayah RW 04 Kelurahan Sambikerep, Kota Surabaya. Program ini bertujuan meningkatkan ketahanan pangan rumah tangga melalui pemanfaatan lahan terbatas secara efisien, sekaligus mendukung upaya penurunan stunting dengan menyediakan sumber protein hewani dan sayuran segar. Pelaksanaan kegiatan dilakukan dengan pendekatan partisipatif melalui pelatihan dan pendampingan oleh mahasiswa Universitas Pembangunan Nasional Veteran Jawa Timur kepada masyarakat setempat. Hasilnya menunjukkan peningkatan pengetahuan dan keterampilan warga dalam budidaya ikan lele dan tanaman kangkung secara mandiri, dengan antusiasme tinggi. Budikdamber terbukti sebagai solusi praktis dan berkelanjutan untuk meningkatkan konsumsi gizi keluarga di lingkungan perkotaan. Disarankan adanya dukungan lanjutan dari pemerintah lokal dan perluasan sosialisasi agar program dapat direplikasi ke wilayah lain. Inovasi ini memiliki potensi besar dalam mendukung ketahanan pangan serta pemberdayaan masyarakat di kawasan padat penduduk.
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Cindi Adam; Mohammad Idhom; Trimono Trimono
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/zwvk1v20

Abstract

The development of artificial intelligence (AI) has driven innovation in financial analysis, including the prediction of volatile stock prices. This study aims to predict the stock price of PT Garudafood Putra Putri Jaya Tbk using an ARIMA model with Outlier handling as an initial approach towards a more adaptive prediction system. Daily closing price data from Yahoo Finance was analyzed through stationarity testing, ARIMA model identification, log-return-based Outlier detection, and performance evaluation using RMSE, MAE, and MAPE. The results show that ARIMA Outlier performs better than the basic ARIMA. The standard ARIMA produces a MAPE of 1.32% and an AIC of –899.46, while ARIMA with three dummy Outliers achieves a MAPE of 1.16% and an AIC of –900.37. The 14-day forecast shows a stable pattern in the range of Rp 370–371. In the test data, the basic ARIMA provided the best accuracy in mid-August, while ARIMA Outlier achieved the highest accuracy at the end of August with a prediction of Rp 370.2, which was very close to the actual price of Rp 370.4. These results show that handling Outliers improves the accuracy of the model, so that ARIMA Outlier can be used as a starting point for the development of an AI-based financial prediction system.
Stacked LSTM Integrated with Big Data Pipelines for Automated Food Beverage Stock Price Prediction Ilil Musyarof Asfiani; Dwi Arman Prasetya; Trimono Trimono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3687

Abstract

Stock price volatility in the Food and Beverage (F&B) sector presents persistent challenges for investors and decision-makers, particularly in emerging markets. This study proposes an automated stock price prediction framework whose primary contribution lies in the system-level integration of a Stacked Long Short-Term Memory (LSTM) model with a scalable big data orchestration pipeline, rather than in introducing a new forecasting algorithm alone. The system targets three Indonesian F&B companies PT Indofood CBP Sukses Makmur Tbk, PT Mayora Indah Tbk, and PT Garudafood Putra Putri Jaya Tbk using historical daily stock price data. The dataset spans multiple years of trading records retrieved from the Yahoo Finance API, and predictions are generated for a seven-day forecasting horizon. Methodologically, the approach combines a multi-layer LSTM architecture with Apache Spark for distributed data preprocessing, Apache Airflow for automated workflow orchestration, and PostgreSQL for structured data storage. This integration enables scheduled data ingestion, reproducible model training, and continuous forecasting within an end-to-end analytics pipeline. Model performance is evaluated using error-based metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), and is benchmarked against a conventional single-layer LSTM without pipeline orchestration. Empirical results show that the proposed pipeline-based Stacked LSTM achieves lower prediction error, with MAPE values ranging between approximately 1.1% and 2.2% across the evaluated stocks, indicating improved stability and accuracy. Overall, the findings demonstrate enhanced forecasting reliability and deployment readiness through automated pipelines.
Co-Authors Arrum Marwani Bagus Widduro Cindi Adam Dwi Arman Prasetya Ilil Musyarof Asfiani Mochammad Abudrrochman Faiz Mohammad Idhom Nevia Desinta Putri