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All Journal ComEngApp : Computer Engineering and Applications Journal Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics Algoritme Jurnal Mahasiswa Teknik Informatika
Novitasari, Dian C Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation Other

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PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION Andriyani , Mayrisa; Nurwilda , Siti; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14859

Abstract

Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees.
KLASIFIKASI KUALITAS UDARA MENGGUNAKAN METODE EXTREME LEARNING MACHINE (ELM) Jannah, Rachma Raudhatul; Sholahuddin, Muhammad Zulfikar; Haq, Dina Zatusiva; Novitasari, Dian C Rini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3066

Abstract

Air quality is a critical factor affecting both ecological and human well-being. Air pollution is a global epidemic that poses a threat to human health and the environment. High population density resulting from industrial expansion and the increased number of motor vehicles are two primary causes of declining air quality in metropolitan areas. Air pollutants include surface ozone (O3), dust particles (PM 10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). Researchers have begun exploring the use of Extreme Learning Machine (ELM) to classify air quality. The ELM method assesses air quality as either very good or poor. In this study, we compare datasets to evaluate the effectiveness of hidden node parameters using the split method. Our tests indicate that the split method impacts accuracy, sensitivity, and specificity. The ideal model with a 70:30 split ratio and 15 hidden nodes achieved a 90% success rate.  
Co-Authors Abdulloh Hamid Adzan, M. Sailul Ahmad Hanif Asyhar Ambadar, Panreshma Rizkha Andriyani , Mayrisa FAJAR SETIAWAN Fatmawati Fatmawati Hani Khaulasari Haq, Dina Zatusiva Hartanto, Salsabila Jannah, Rachma Raudhatul Lutfi Hakim Moh. Hafiyusholeh Nurwilda , Siti Pramesti, Diah Devi Puspitasari, Wahyu Tri Putri, Risma Madurahma Ramadanti , Alvin Nuralif Rimuljo Hendradi Rinda Nariswari Rizal Amegia Saputra Sholahuddin, Muhammad Zulfikar Sri Wahyuni Wulandari, A’isyah Yuniar Farida, Yuniar