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All Journal JETT (Jurnal Elektro dan Telekomunikasi Terapan) Journal of Health Policy and Management Journal of Maternal and Child Health Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Penkomi : Kajian Pendidikan dan Ekonomi MITZAL (Demokrasi, Komunikasi dan Budaya) : Jurnal Ilmu Pemerintahan dan Ilmu Komunikasi J-ESA (Jurnal Ekonomi Syariah) Taroa : Jurnal Pengabdian Masyarakat Jurnal Atma Inovasia Cakrawala: Jurnal Pengabdian Masyarakat Global Prosiding Seminar Informasi Kesehatan Nasional Amal: Jurnal Ekonomi Syariah Lentera Leuser Journal of Environmental Studies Journal of Regional Economics and Development Infolitika Journal of Data Science Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Socious Journal Journal of Ekonomics, Finance, and Management Studies JPEMAS: Jurnal Pengabdian Kepada Masyarakat JARSI
Ulhaq, Muhammad Zia
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Journal : Infolitika Journal of Data Science

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Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach Ulhaq, Muhammad Zia; Farid, Muhammad; Aziza, Zahra Ifma; Nuzullah, Teuku Muhammad Faiz; Syakir, Fakhrus; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.211

Abstract

The current climate change is causing the upwelling phenomenon to occur frequently in lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating net cage fish farmers to suffer losses. From existing studies, temperature sensors are used to determine the current condition of a body of water experiencing upwelling or not. Therefore, this study applies clustering to historical climate data from 2017-2023 using a semi-supervised learning approach that produces two labels: "potential for upwelling" and "no potential for upwelling." In the clustering process, the data is divided into two clusters using K-Means Clustering, and Support Vector Machine (SVM) is chosen to classify them. The performance of the proposed algorithm is expressed with accuracy, precision, recall, and F1-score values of 0.99, 0.995, 0.970, and 0.985, respectively. The analysis results show that this model has excellent performance in identifying upwelling potential. By using this method, information about upwelling potential can be obtained more quickly and accurately, allowing fish farmers to take appropriate preventive measures. This study also shows that the combination of K-Means Clustering and Support Vector Machine (SVM) can be effectively used to analyze historical climate data and generate useful predictions.
Co-Authors Afran Afriani, Jaitun Ahmad Zamani, Ahmad Akbar, Zulhiddin Al Fajar, Muhammad Rasyad Al Iqlima Al-Ifansyah, M. Alfi Syahrin, Alfi Alyatus Syifa Amanda Ananda, Zafira Putri Ani Nur Fauziah, Ani Nur Anista R, Jodang Setia Adi Annisa, Nadiva Armanda, Fellynia Rizky Atmojo, Joko Tri Aziza, Zahra Ifma Bahriyah, Euis Nurul Bhisma Murti Devi Pramita Sari Diah, Muhammad Dianti, Nurul Jumar Emelda, Hardianti Octapiani Eti Poncorini Pamungkasari FATIMAH FATIMAH Fatimah, Fatma Siti Firmansya Firmansyah, Denny Hanan, Dafif Harmin Hatta, Harmin Hesty Latifa Noor Husniah, Dina Ibrahim Ibrahim Ihwan P. Syamsudin Illahi, Rizqi Imam Muhammad Amhar Indriani, Ida Inul Daratista Iransayah Ismail Ismail Khalifa Humaira Lala, Andi Larisu, Zulfiah Maria Ulfa Mastura, Mastura Mawar Ambarwati Melindawati Melindawati Merung, Arteurt Yoseph Muhajir, Mia Aulia Muhammad Farid Muhammad Rasyad Al Fajar Muhammad Rasyad Al, Fajar Muhammad Rasyad Al-fajar Muslem Muslem, Muslem Mutia, Eka Septi Seliani Natasya, Awi Nurjayanti Nurjayanti Nursyafika, Nursyafika Nurul Husna Nuzullah, Teuku Muhammad Faiz Putri Rahyu Putri, Imram Radne Rimba Putriana Putriana Rafiuddin Rafiuddin, Rafiuddin Rahmah Rahmalia Rahmalia Rahmatan Setiadin Robing, Robing Rokhmatun, Puji Nur Runi Asriani Safrida Safrida Sagaf, Umar Salman, Nurul Fajraini Salsa Nabila Salsabila, Athaya Sarwhadamana, Raden Jaka Sasmita, Novi Reandy Sherina Molek Siti Maesaroh Siti Mukarramah Sri Mawarni Sunaryanti, Sri Sayekti Heni Syahraeni, Syahraeni Syakir, Fakhrus Tianda, Agus Umar Sagaf Wahyuningsih, Irman Widiyanto, Aris Zahriah, Zahriah