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Dwi Arman Prasetya
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Faktor Pemengaruh Tingkat Keparahan Kebakaran Hutan di Kalimantan Selatan pada Tahun 2023 Menggunakan Geographically Weighted Regression M Naswan Izzudin Akmal; Dwi Arman Prasetya; Aviolla Terza Damaliana
Prosiding Sains dan Teknologi Vol. 5 No. 1 (2026): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 5 - Februari 2026
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kebakaran hutan di Kalimantan Selatan menunjukkan peningkatan tajam dalam frekuensi kejadian pada tahun 2023, bertepatan dengan kondisi kering yang terkait dengan fenomena El Niño. Meskipun jumlah kejadian kebakaran tinggi, tingkat keparahan kebakaran bervariasi di berbagai lokasi. Penelitian ini menganalisis faktor lingkungan yang mempengaruhi tingkat keparahan kebakaran hutan menggunakan Geographically Weighted Regression (GWR) untuk memodelkan variasi spasial. Keparahan kebakaran diukur menggunakan Differenced Normalized Burn Ratio yang diperoleh dari citra satelit. Prediktor lingkungan meliputi suhu udara, curah hujan total, ketinggian, dan kelembaban vegetasi, yang diperoleh dari data reanalisis ERA5 Land, SRTM, dan citra Landsat. Hasil menunjukkan bahwa keparahan kebakaran menunjukkan korelasi spasial positif, menunjukkan hubungan yang bervariasi secara spasial antara faktor lingkungan dan keparahan kebakaran. Dibandingkan dengan model Ordinary Least Squares global, model GWR memberikan daya penjelas yang lebih baik dan mengurangi autokorelasi spasial pada residual. Curah hujan total dan kelembaban vegetasi menunjukkan pengaruh spasial yang paling konsisten terhadap tingkat keparahan kebakaran, sementara suhu dan ketinggian memiliki efek yang lebih lemah dan lebih lokal. Temuan ini menunjukkan bahwa pemodelan spasial berguna untuk memahami variasi tingkat keparahan kebakaran hutan dan untuk mendukung strategi pengelolaan kebakaran yang spesifik lokasi.
Classification of Wind Instrument Sound Arrangements Using Recurrent Neural Network (RNN) Method Dwinggrit Oktaviani Putri; Dwi Arman Prasetya; Wahyu Syaifullah J.S
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2879

Abstract

Wind instruments such as saxophone, clarinet, trumpet, and others possess unique characteristics in terms of frequency, amplitude, and waveform, serving as distinct acoustic signatures for each instrument. In the digital era, the classification of musical instruments for composition and arrangement is often still carried out manually, which can lead to inefficiencies and potential errors in the music production workflow. Automating this process can significantly enhance the speed, accuracy, and consistency of instrument sound classification, especially for large-scale or real-time applications. This study aims to develop a classification system for wind instrument sounds using the Recurrent Neural Network (RNN) method, leveraging acoustic features such as Mel-Frequency Cepstral Coefficients (MFCC) and spectrograms. The RNN architectures employed in this research are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), chosen for their ability to capture temporal dependencies in sequential data such as audio signals, making them well-suited for sound-based classification tasks. The dataset consists of 1,200 audio files (.wav) comprising four wind instrument classes: trumpet, baritone, mellophone, and tuba, with 300 samples each. Experimental results show that the LSTM model achieved an accuracy of 95%, while the GRU model reached 99%. Compared to previous studies that reported lower accuracy or focused on broader instrument categories, this research demonstrates a significant improvement in wind instrument classification. The results highlight the effectiveness of RNN-based models in learning the temporal dynamics of audio signals, offering a reliable solution for automated instrument classification in digital music systems.
Application Of Hybrid ARIMAX-ANN In Forecasting The Price Of Chili Bird's Eye Dina Magdalena Manurung; Aviolla Terza Damaliana; Dwi Arman Prasetya
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3091

Abstract

Chili peppers are a vital horticultural commodity in Indonesia, especially within the culinary industry, due to their high economic value and demand. In Medan, the demand for chili peppers is notably high, yet production limitations often lead to significant price fluctuations. These price variations are influenced by multiple factors, including weather conditions, such as rainfall, and increased demand during national holidays. This study focuses on predicting the prices of both green and red bird's eye chili, which are widely consumed for their distinct spicy flavor. The data used in this study consists of daily chili prices spanning from January 1, 2019, to February 28, 2025, along with external variables such as precipitation and national holiday weeks. To predict the price fluctuations, a Hybrid ARIMAX-ANN model was employed, combining the linear ARIMAX model and the non-linear ANN model to better capture the complex price patterns. The findings revealed that the optimal model for green bird's eye chili was Hybrid ARIMAX(4,0,0)-ANN(6,64,1) with a MAPE of 3.98%, while for red bird's eye chili, the Hybrid ARIMAX(4,0,0)-ANN(6,64,1) model achieved a MAPE of 4.15%. This model was then applied to forecast the chili prices for the next 5 days, and the predictions demonstrated similar price trends for both green and red bird's eye chili. The results highlight the effectiveness of the Hybrid ARIMAX-ANN model in providing accurate chili price forecasts, which could be useful for better price management and planning in the agricultural sector.
Co-Authors Aviolla Terza Damaliana Dina Magdalena Manurung Dwinggrit Oktaviani Putri M Naswan Izzudin Akmal Wahyu Syaifullah J.S