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All Journal bit-Tech TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi SAINTEK
Dwi Arman Prasetya
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance 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.
Prediksi Gangguan Kesehatan Mental pada Kalangan Mahasiswa Menggunakan Metode Pseudo-Labeling dan Algoritma Regresi Logistik Anggraini Puspita Sari; Dwi Arman Prasetya; Firza Prima Aditiawan; Muhammad Muharrom Al Haromainy
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp40-48

Abstract

Mental illness is a health condition that alters a person's thoughts, feelings, or behaviors, leading to distress and difficulty in maintaining a normal life. Mental health issues should not be taken lightly due to the challenges associated with diagnosis. Many students tend to experience mental health problems at various stages of their education, from diploma programs to doctoral studies. This situation becomes more critical as students approach the end of their studies and anticipate future prospects. This article explores the mental health status of students through symptoms, using logistic regression methods for prediction based on the dataset used. In this study, two types of data are employed: labeled dataset and unlabeled dataset, which are combined to create a semi-supervised learning approach. Labeled dataset is classified using a logistic regression algorithm, while unlabeled dataset employs the pseudo-labeling method. The analysis and modeling of the dataset indicate that the comparison between labeled and unlabeled dataset can significantly affect accuracy and processing time. Furthermore, the use of the pseudo-labeling method with the logistic regression algorithm is well-suited for the mental health case study, achieving an accuracy of 98% with a labeled to unlabeled dataset ratio of 1:2.
Co-Authors Anggraini Puspita Sari Aviolla Terza Damaliana Dina Magdalena Manurung Dwinggrit Oktaviani Putri Firza Prima Aditiawan M Naswan Izzudin Akmal Muhammad Muharrom Al Haromainy Wahyu Syaifullah J.S