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All Journal bit-Tech Digital Transformation Technology (Digitech)
Ira Safitri
Institut Teknologi Sumatera
Computer Science & IT Library & Information Science

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Prediksi Hasil Panen Tanaman Pangan Sumatera dengan Metode Machine Learning Ardika Satria; Rizty Maulida Badri; Ira Safitri
Digital Transformation Technology Vol. 3 No. 2 (2023): Artikel Periode September 2023
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v3i2.2852

Abstract

Pertanian di Indonesia menjadi sektor yang sangat penting karena bertujuan untuk meningkatkan produksi pangan dan industri serta mendorong produksi ekspor. Indonesia memiliki hasil pertanian bahan pokok seperti padi, jagung, ubi jalar, ubi kayu, kacang tanah dan kedelai. Salah satu wilayah paling produktif yang menghasilkan bahan pangan dalam memenuhi kebutuhan pokok adalah pulau Sumatera. Potensi iklim di pulau Sumatera sangat cocok untuk kegiatan pertanian karena curah hujan yang merata hampir sepanjang tahun. Produksi tanaman pangan di Sumatera meningkat sejak tahun 1993. Perluasan lahan pertanian juga naik signifikan. Sementara itu, hasil pertanian di Sumatera sangat rentan terhadap perubahan iklim global terutama kenaikan suhu dari tahun ke tahun. Prediksi hasil pertanian perlu dilakukan agar memberikan hasil yang maksimal pada produksi bahan pokok di Sumatera. Penelitian ini bertujuan untuk mengetahui algoritma terbaik dalam melakukan prediksi hasil pertanian komoditas tanaman pangan yaitu padi, jagung, kacang tanah, kedelai, ubi kayu dan ubi jalar di pulau Sumatera dengan pendekatan model regresi machine learning. Sehingga, penelitian ini diharapkan dapat menjadi acuan dalam pengembangan penelitian terkait bidang pertanian di pulau Sumatera. Metode yang digunakan adalah Machine Learning, yaitu Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extra Tree (ET), Support Vector Machine (SVM), dan Artificial Neural Network (ANN). Nilai koefisien R2 untuk produksi padi masing-masing adalah 0,897; 0,893; 0,957; 0,968; 0,928; dan 0,909. Sedangkan pada produksi bahan pokok lainnya masing-masing adalah 0,754; 0,786; 0,721; 0,913; 0,509; dan 0,90. Model Extra Tree mendapat nilai koefisien R2 tertinggi dan lebih akurat dibandingkan model lainnya.
Forecasting the Inflation Rate in Lampung Province Using the ARIMA Method Ira Safitri; A Rafi Paringgom Iwari
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2335

Abstract

Inflation is one of the key indicators that reflects the economic stability of a region. Inflation instability can directly impact the purchasing power of the population, increase poverty rates, and create imbalances in macroeconomic policies. In Lampung Province, inflation fluctuations have become a significant issue requiring attention, particularly in the context of regional economic planning and policy-making. This study forecasts the inflation rate using the Autoregressive Integrated Moving Average (ARIMA) method, which is known to be effective in analyzing time series data and providing accurate short-term estimates. The data used comprises monthly inflation rates from 2006 to 2023, obtained from the Central Bureau of Statistics (BPS) of Lampung Province. Five ARIMA model configurations were tested: ARIMA(3,1,2), ARIMA(3,1,1), ARIMA(2,1,1), ARIMA(1,1,1), and ARIMA(5,1,1). Based on the evaluation of the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE), the ARIMA(1,1,1) model was identified as the best-performing model, with the lowest AIC value and a MAPE of 0.57. The model also passed diagnostic tests, including residual normality and white noise assessment using the Ljung-Box test. The forecasting results indicate a gradual upward trend in inflation, with predicted rates of 0.23% in January 2024, 0.29% in February 2024, and 0.30% in March 2024. These findings provide early indications that inflation in Lampung Province tends to increase in the short term, and can serve as a basis for formulating more targeted regional inflation control policies.
Co-Authors A Rafi Paringgom Iwari Ardika Satria Rizty Maulida Badri