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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Syarifuddin Elmi
universitas sains dan teknologi indonesia
Computer Science & IT

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Prediksi Emisi Co2  Di Indonesia Menggunakan Pendekatan Hybrid Arima Dan LSTM Syarifuddin Elmi; Rini Yanti; Mardainis; Hadi asnal
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/vtjtfp90

Abstract

Climate change has emerged as a pressing global issue, with carbon dioxide (CO2) emissions serving as a major contributor to global warming. In Indonesia, the expansion of industrial activities, transportation, and the reliance on fossil fuel-based energy have significantly accelerated CO2 emission levels. In this context, the need for accurate emission forecasting has become increasingly important as a basis for formulating data-driven mitigation policies. This study aims to develop a predictive model for CO2 emissions in Indonesia using a hybrid approach that combines AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) methods. ARIMA is employed to capture linear patterns in historical time series data, while LSTM is used to model the non-linear and complex dynamics often present in environmental data. The emission data used spans from 1970 to 2023, with training and testing data separated chronologically in an 80:20 ratio. The evaluation results show that the ARIMA model alone yielded suboptimal performance (RMSE: 2342.5139, MAE: 2341.5775, MAPE: 414.77%), whereas the LSTM model significantly improved prediction accuracy (RMSE: 49.3307, MAE: 45.5498, MAPE: 7.94%). The hybrid ARIMALSTM model achieved the best results, with an RMSE of 31.5778, MAE of 25.0335, and MAPE of 4.34%. These findings indicate that the combination of both methods substantially enhances prediction performance compared to standalone models. The implications of this research are twofold: academically, it contributes to methodological development in environmental data analysis; practically, it offers valuable insights for policymakers in formulating more effective and sustainable carbon emission reduction strategies in Indonesia. 
KLASIFIKASI PENJUALAN WALMART MENGGUNAKAN ALGORITMA C4.5 Iftar Ramadhan; Rangga Febrio Waleska; Syarifuddin elmi; Lusiana Efrizoni; Rahmaddeni
BETRIK Vol. 15 No. 02 (2024): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/pjbkse24

Abstract

Penelitian ini bertujuan untuk memprediksi penjualan Walmart dengan menggunakan algoritma C4.5, sebuah metode pohon keputusan yang populer dalam data mining. Prediksi penjualan merupakan aspek krusial bagi strategi bisnis Walmart untuk mengoptimalkan persediaan dan meningkatkan keuntungan. Dataset yang digunakan dalam penelitian ini mencakup data historis penjualan Walmart yang terdiri dari berbagai variabel seperti store, date, weakly sales, holiday flag, temperature, fuel price, uci, unemployment dan faktor-faktor lain yang mempengaruhi penjualan. Dari data variabel tersebut akan melakukan klasifikasi pada data penjualan walmart dari 6.345 record. Hasil pengujian metode dengan evaluasi modeling menunjukkan bahwa metode C4.5 mendapatkan hasil acuracy 0.94, precision 0.43, dan recall 0.75.
Co-Authors Hadi Asnal, Hadi Iftar Ramadhan Lusiana Efrizoni Mardainis Rahmaddeni Rangga Febrio Waleska Rini Yanti