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Techne : Jurnal Ilmiah Elektroteknika
Published by Universitas Kristen Satya Wacana
ISSN : 14128292     EISSN : 26157772     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
urnal Ilmiah Elektroteknika Techné (p-ISSN: 1412-8292, e-ISSN: 2615-7772) adalah jurnal ilmiah yang diterbitkan oleh Fakultas Teknik Elektronika dan Komputer, Universitas Kristen Satya Wacana. Kajian ilmu yang tercakup dalam Jurnal Ilmiah Elektroteknika Techné adalah bidang-bidang Elektronika dan Komputer, baik yang menyangkut perangkat keras maupun perangkat lunak. Jurnal Ilmiah Elektroteknika Techné terbit online pertama kali tahun 2010. Jurnal ini terbit dua kali setahun, yaitu pada bulan April dan Oktober. Jurnal Ilmiah Elektroteknika Techné menganut prinsip Open Access, sehingga semua artikel yang dipublikasikan dapat diakses secara bebas oleh setiap pengunjung.
Arjuna Subject : -
Articles 13 Documents
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Search results for , issue "Vol. 23 No. 1 (2024)" : 13 Documents clear
The Performance of Machine Learning Model Bernoulli Naïve Bayes, Support Vector Machine, and Logistic Regression on COVID-19 in Indonesia using Sentiment Analysis Dirgantara, Wahyu; Fairuz Iqbal Maulana; Subairi; Rahman Arifuddin
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 1 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i1.446

Abstract

The COVID-19 pandemic has significantly impacted Indonesia, necessitating a deeper understanding of public sentiment towards the crisis. This study investigates the performance of three prominent machine learning models: Bernoulli Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression, in analyzing sentiments related to COVID-19 in Indonesia. Utilizing a dataset comprising social media posts, the research aims to classify sentiments into positive, and negative categories, providing insights into the public's perception of the pandemic and associated measures. Sentiment analysis serves as a powerful tool to capture the collective emotions and opinions of the populace, which are pivotal in shaping public health responses and policies. The accuracy of LR and SVM is 99%, whereas Bayesian has an accuracy of 98%. We conclude that Logistic Regression and Support Vector Machine are the best model for the above dataset. This research evaluates these models' accuracy and reliability in the context of the Indonesian language, which influence sentiment interpretation. The findings of this study will contribute to the fields of natural language processing and public health by highlighting the efficacy of machine learning models in sentiment analysis during a health crisis. Moreover, the results will assist policymakers and health officials in understanding public sentiment, enabling them to tailor communication and interventions more effectively.
Studi Kelayakan Teknis dan Ekonomi Pembangkit Listrik Tenaga Surya Rooftop di Hotel Rayz Universitas Muhammadiyah Malang Effendy, Machmud; Nasar, M.; Abduh, Moh.; Suwignyo; Ad, Azhar; Sm., Lintang; R.A., Fariz
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 1 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i1.455

Abstract

Pembangkit listrik tenaga surya (PLTS) merupakan salah satu pembangkit energi listrik terbarukan yang ramah lingkungan. Penelitian ini bertujuan untuk memanfaatkan PLTS sebagai sumber energi listrik dan sekaligus mendapatkan analisis kelayakan teknis dan ekonomi PLTS atap on grid di Hotel Rayz Universitas Muhammadiyah Malang (UMM). Analisa kelayakan PLTS ini memanfaatkan atas atap seluas 267,2 m2. Kapasitas instalasi maksimal yang direkomendasikan sebesar 37,4 kWp. Energi yang dihasilkan sistem PLTS pada kelistrikan Hotel Rayz-UMM dalam setahun sebesar 55.341 kWh. Nilai kelayakan teknis ditinjau dari efektifitas pembangkit (ACEGE) memperoleh sebesar 10,8%, sedangkan nilai performance ratio (PR) dihasilkan sebesar 60,1%. Hasil analisis ekonomi menunjukkan bahwa nilai net present value (NPV) negative sebesar -Rp. 149.199.260,7, nilai benefit cost ratio (BCR) sebesar 1,8 dan nilai payback period (PP) sebesar 13,65. Dari hasil analisa teknik dan ekonomi tersebut, PLTS atap di Hotel Rayz-UMM belum layak dibangun.
Advancing Natural Gas Price Predictions with ConcaveLSTM Diqi, Mohammad; Wanda, Putra; Hamzah; Ordiyasa, I Wayan; Fathinah, Azzah
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 1 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i1.456

Abstract

This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatility and complexity of energy markets, accurate forecasting models are crucial for effective decision-making. The research employs a comprehensive dataset from 1997 to 2020, focusing on the daily price of natural gas in US Dollars per Million British thermal units (Btu). Through rigorous testing across various model configurations, the study identifies optimal settings for the ConcaveLSTM model that significantly improve prediction accuracy. Specifically, configurations utilizing 50 input steps with neuron counts of 100 and 300 exhibit superior performance, as evidenced by lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), alongside higher R-squared (R2) values. These findings validate the ConcaveLSTM model's potential in financial forecasting and highlight the importance of parameter tuning in enhancing model efficacy. Despite certain limitations regarding dataset scope and market variability, the results offer promising insights into developing advanced forecasting tools. Future research directions include expanding the dataset, incorporating additional market influencers, and conducting comparative analyses with other forecasting models. This study contributes to the evolving field of machine learning applications in financial market predictions, offering a foundation for further exploration and practical implementation in the energy sector.

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