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All Journal Jurnal Pembina JITCoS : Journal of Information Technology and Computer System
Risky, T. Tanzil Azhari
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Computer Science & IT Control & Systems Engineering Education Engineering Environmental Science Languange, Linguistic, Communication & Media Other

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Edukasi Green Computing di SMP Negeri 3 Pancur Batu sebagai Sarana Sosialisasi Hemat Energi dan Teknologi Ramah Lingkungan Risky, T. Tanzil Azhari; Putra, Fahrialdy Febriansyah; Zebua, Jelita Rahmah; Batubara, Qisti Azraladiba; Lubis, Vima Zikra Adha
Jurnal Pembina Vol. 1 No. 1 (2025): Mei 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/pembina.v1i1.29

Abstract

Perkembangan teknologi informasi yang pesat menimbulkan konsumsi energi yang besar dan dampak lingkungan yang signifikan. Green computing atau komputasi hijau menjadi solusi penting untuk mengurangi jejak karbon teknologi informasi. Kegiatan sosialisasi green computing dilaksanakan di SMP Negeri 3 Pancur Batu dengan tema "Hemat Energi, Selamatkan Bumi: Belajar Teknologi Hijau" untuk meningkatkan kesadaran siswa tentang penggunaan teknologi yang ramah lingkungan. Metode yang digunakan meliputi ceramah, demonstrasi, dan praktik langsung tentang prinsip-prinsip green computing. Peserta kegiatan berjumlah 45 siswa kelas VII dan VIII. Evaluasi kegiatan menggunakan kuesioner dengan skala 1-4 untuk mengukur pemahaman dan antusiasme peserta. Hasil evaluasi menunjukkan tingkat kepuasan dan pemahaman peserta mencapai 94,7%. Kegiatan ini berhasil meningkatkan kesadaran siswa tentang pentingnya teknologi hijau dalam kehidupan sehari-hari dan memberikan pemahaman praktis tentang cara menghemat energi melalui penggunaan teknologi yang efisien.
Modeling and Simulation of Indoor Temperature Dynamics Using Random Forest and Multi-Layer Perceptron Methods Risky, T. Tanzil Azhari; Faiza, Nayla; Hasibuan, Mhd Fikry Hasrul; Nasution, Mhd Syahru Ramadhan
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.39

Abstract

Modeling and simulating indoor temperature changes is crucial for improving the energy efficiency of HVAC systems in smart buildings. This study created and compared two models, Random Forest and Multi-Layer Perceptron (MLP), to study indoor temperature changes and make 24-hour temperature predictions. The dataset used contained 97,606 readings from IoT sensors on Kaggle, which were then processed into 38,334 observations with a 5-minute interval. The feature engineering process included creating lag features, moving statistics, and temperature differences in order to capture the time patterns and thermal properties of the building. The Random Forest model showed better results with MAE of 0.146°C, RMSE of 0.285°C, and R² of 0.986, far better than the MLP which had MAE of 0.470°C, RMSE of 0.731°C, and R² of 0.907. A 24-hour simulation proved the Random Forest's ability to make step-by-step predictions, achieving an MAE of 0.057°C and an R² of 0.993 without any cumulative errors. Random Forest was able to capture dynamic temperature changes (29.5-35°C), while MLP provided more stable results (32.5-35°C). The results of the study show that Random Forest is more efficient in modeling temperature changes, with the potential for HVAC energy savings of 15-25% through more precise settings based on predictions.
Co-Authors Batubara, Qisti Azraladiba Faiza, Nayla Hasibuan, Mhd Fikry Hasrul Lubis, Vima Zikra Adha Nasution, Mhd Syahru Ramadhan Putra, Fahrialdy Febriansyah Zebua, Jelita Rahmah