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All Journal Jurnal Informatika Kaputama (JIK) Jurnal Teknik Informatika (JUTIF)
Asy’ari, Fajar husain
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Computer Science & IT Control & Systems Engineering

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IMPLEMENTASI ALGORITMA DEEP LEARNING TERINTEGRASI CNN DAN LSTM UNTUK PREDIKSI HARGA KOMODITAS PANGAN DI PASAR INDONESIA Safitri, Melina Dwi; Proborini, Ellen; Asy’ari, Fajar husain
Jurnal Informatika Kaputama (JIK) Vol 9 No 2 (2025): Volume 9, Nomor 2, Juli 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v9i2.1006

Abstract

Pertumbuhan perekonomian suatu negara perlu diperhatikan, dikarenakan perekonomian berpengaruh pada tingkat kesejahteraan rakyat, inflasi negara dan proses pengambilan keputusan. Menurut Badan Pusat Statistik Nasional pada tahun 2021 indonesia mengalami pertumbuhan sebesar 3,69%, hal ini harus ditingkatkan supaya Indonesia bisa menjadi negara maju. Ada beberapa sektor yang dapat mempengaruhi pertumbuhan ekonomi seperti pertambangan, pertanian, perbankan, hingga pangan. Sektor pangan sangat penting bagi suatu negara karena sektor pangan merupakan sektor yang berkaitan erat dengan kehidupan sehari -hari warga negara, namun sektor ini mengalami beberapa kendala seperti kurangnya pasokan, harga pangan yang tidak stabil, tidak stabilnya harga pangan dipengaruhi oleh beberapa faktor salah satunya yaitu oleh petani yang mengalami gagal panen, harga bahan produksi yang meningkat. Untuk mengurangi ketidakstabilan harga bahan pangan, maka diperlukan sebuah sistem yang dapat memprediksi harga pangan berbasis data. Penelitian ini melakukan prediksi terhadap data harga pangan di Indonesia berbasis time series menggunakan algoritma CNN-LSTM. Hasil MAPE terendah yang didapatkan dalam penelitian ini adalah 7,1078% dengan skema model 1 tanpa Maxpoolig1D dan menggunakan optimizers SGD.
An Intelligent IoT-Based Hydroponic Irrigation System for Strawberry Cultivation Using Extreme Gradient Boosting Decision Model Bijanto, Bijanto; Abidin, Zainal; Asy’ari, Fajar Husain; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5173

Abstract

Most existing implementations rely on static rule-based or fuzzy logic control, which lack adaptability to dynamic environmental changes and often require manual tuning by experts. These limitations are particularly challenging for small-scale farmers who face constraints in technical knowledge, infrastructure, and operational flexibility. To address these issues, this study proposes an intelligent hydroponic irrigation system that embeds the Extreme Gradient Boosting (XGBoost) algorithm as a decision-making model. The system collects real-time sensor data including temperature, humidity, and light intensity, and uses the trained XGBoost classifier to determine irrigation needs with binary output (FLUSH or NO). The system was implemented on a vertical hydroponic setup for strawberry cultivation, and evaluated over a 21-day observation period. The results show that the XGBoost-based model was effective in maintaining consistent vegetative growth, with plants in upper-tier pipes achieving an average height above 25 cm by the end of the cycle. This demonstrates that the model could support responsive and resource-efficient irrigation control. Beyond technical performance, the research highlights the urgency of adopting data-driven smart farming systems to ensure sustainable food production, optimize limited resources, and empower small-scale farmers with accessible and scalable solutions. However, the proposed XGBoost model is still limited to local crops; therefore, when introducing new plant types or additional sensor inputs, parameter adjustments and retraining are required to maintain accuracy. Future improvements may include dynamic model retraining and integration with real-time feedback systems to enhance system autonomy and resilience in broader agricultural settings.
Co-Authors Ali, Rabei Raad Bijanto, Bijanto Proborini, Ellen Safitri, Melina Dwi Zainal Abidin