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All Journal Journal of Information Systems and Informatics Jurnal ilmiah teknologi informasi Asia
Mas Diyasa, I Gede Susrama Susrama
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Computer Science & IT

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Analyzing the Relationship Between Meteorological Parameters and Electric Energy Consumption Using Support Vector Machine and Cooling Degree Days Algorithm Azizah, Nabila Wafiqotul; Puspaningrum, Eva Yulia; Mas Diyasa, I Gede Susrama Susrama
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.719

Abstract

Nowadays, electricity is increasing rapidly. This increase is caused by several factors, one of which is meteorological factors. Meteorological parameters have various types, but this research uses three types in the form of temperature, humidity, and wind speed. The selection of these three types is due to the fact that they have a very close relationship with human life. In line with that, this research uses datasets obtained from the official websites of BMKG (Meteorology, Climatology and Geophysics Agency) and PLN (State Electricity Company). On this occasion, researchers used several methods, namely Cross-Industry Standard Process for Data Mining (CRISP-DM), Cooling Degree Days (CDD), and Support Vector Machine (SVM). The CRISP-DM method is useful for describing the data mining cycle so that the process can be more organized. The SVM algorithm is useful for predicting electricity consumption based on meteorological parameters in January to April 2024, while the CDD method is useful for knowing the correlation of meteorological parameters to electricity consumption in winter. In line with this, this research produces predictions of electricity consumption based on meteorological parameters in January 2024 to April 2024 with an average range of 20.9 Watts per day. In addition, trends and predictions during model evaluation obtained a precision value of 0.796, recall of 0.793, F1 score of 0.793, MAPE of 17.2%, RMSE of 0.41, MAE of 0.167 and accurate of 0.98. These values indicate that the performance of the accuracy model is very high.
Analisis Sentimen Penggunaan Galon BPA Menggunakan Seleksi Fitur Chi-Square Dan Algoritma Support Vector Machine Aurelia, Cenditya Ayu; Trimono, Trimono; Mas Diyasa, I Gede Susrama Susrama
Jurnal Ilmiah Teknologi Informasi Asia Vol 18 No 2 (2024): Volume 18 nomor 2 2024 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

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Abstract

Air Minum Dalam Kemasan (AMDK) menjadi elemen utama bagi keseimbangan tubuh. Adanya berita tentang bahaya galon yang mengandung BPA menimbulkan kekhawatiran di masyarakat terutama di platform media sosial Twitter sehingga menimbulkan keresahan masyarakat terhadap dampak negatif yang disebabkan dari penggunaan galon BPA. Hal tersebut menciptakan perdebatan antara dua pihak yang terdiri dari masyarakat yang mendukung penggunaan galon BPA dan masyarakat yang mendukung penggunaan galon non-BPA dari produk air minum tertentu. Penelitian ini melakukan analisis sentimen untuk mengelompokkan pendapat masyarakat terkait penggunaan galon menggunakan algoritma Support Vector Machine dan seleksi fitur Chi-Square. Hasil dari penelitian menunjukkan bahwa penerapan seleksi fitur Chi-Square meningkatkan akurasi hingga 0.95 pada kernel Linear dan RBF dengan 239 prediksi yang tepat dan 13 prediksi yang tidak tepat
Co-Authors Aurelia, Cenditya Ayu Azizah, Nabila Wafiqotul Eva Yulia Puspaningrum Trimono, Trimono