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All Journal Jupiter Brilliance: Research of Artificial Intelligence
Galih Mahalisa
Universitas Islam Kalimantan MAB Banjarmasin
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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Prediksi Prospek Harga Real Estate di Masa Pandemi dengan 3 Atribut Berbasis Algoritma Linear Regression gita syafarina; Tri Wahyu Qur’ana; Galih Mahalisa
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 14 No 2-c (2022): Jupiter Edisi Oktober 2022
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./5793/5.jupiter.2022.10

Abstract

Investment is one of the most sought after by some people even during the pandemic, various types of investments range from gold, stocks, and property to real estate. real estate (real estate) or real estate is a legal term that includes land along with anything that remains on the land, such as buildings or projects. Real estate business is a type of activity that has great potential for the long term. Therefore, many people are interested in this field, including investing. This study will process a dataset using a method to get a real estate price prediction by testing using the Linear Regression method on 3 features that affect real estate prices. the accuracy value obtained is 67.8%. Therefore, the price of real estate prospects during the pandemic does not increase or decrease in terms of selling prices
Air Pollution Standard Index (APSI) Detection Application Based on the Flask Model Galih Mahalisa; Nurarminarahmah
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3194

Abstract

Air pollution is a global environmental problem that threatens human health and ecosystems. The Air Pollution Standards Index (APSI) is an important metric for measuring air quality and informing the public about the pollution level in an area. In the digital era, web-based applications have become an effective tool for providing real-time APSI information to the public. This research introduces an Air Pollution Standard Index (APSI) detection application based on the Flask model using the SVM (Support Vector Machine) algorithm to predict APSI. This application collects air quality data from various sensors distributed throughout the region and uses SVM (Support Vector Machine) to process the data. APSI prediction results are then presented to users via an easy-to-use web interface. The main advantage of this application is its ability to provide real-time APSI information so that users can take appropriate action according to the level of air pollution in their area. This application can help the public and environmental authorities proactively deal with air pollution and protect human and environmental health. APSI Prediction Accuracy: Through SVM model training, this application can predict the Air Pollution Standard Index (APSI) with sufficient accuracy. While there is potential to improve accuracy through more data collection and model updates, initial results are promising
Co-Authors gita syafarina Nurarminarahmah Tri Wahyu Qur’ana