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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jambura Journal of Biomathematics (JJBM) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS The Indonesian Journal of Computer Science Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Wijaya, Madona Yunita
Department Of Mathematics, Faculty Of Science And Technology, Universitas Islam Negeri Syarif Hidayatullah Jakarta, South Tangerang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : The Indonesian Journal of Computer Science

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Prediksi Curah Hujan di DKI Jakarta Menggunakan Model Hybrid (DWT-SVR-Prophet) Ramadita, Mutiara; Mahmudi; Wijaya, Madona Yunita
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4357

Abstract

Rainfall has a significant influence in the planning and management of various industries such as forestry, agriculture, and water resources. This research aims to develop a Hybrid model that combines Discrete Wavelet Transform (DWT), Support Vector Regression (SVR) and Prophet models to predict rainfall in the DKI Jakarta area more accurately. Using DWT, rainfall data is divided into high and low frequency components, then the prediction results from each model are combined. At Kemayoran Station, the Hybrid model (High Frequency SVR and Low Frequency Prophet) provides the best performance with SMAPE: 36.44%. At Tanjung Priok Station, the non-Hybrid model gave the best results, with Prophet without DWT achieving SMAPE: 29,82%. This study provides a clear understanding of how effective rainfall prediction models are in DKI Jakarta, which helps water resources planning and management.
Evaluasi Performa Metode Exponential Smoothing pada Data Runtun Waktu Hierarkis Alkadrie, Syarifah Syila; Wijaya, Madona Yunita; Fitriyati, Nina
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4783

Abstract

Penelitian ini bertujuan untuk menyalakan metode Simple Exponential Smoothing (SES), Double Exponential Smoothing (Metode Holt), dan Triple Exponential Smoothing (Holt-Winters) dalam memperkirakan jumlah wisatawan di Australia dari tahun 1998 sampai dengan tahun 2016. Data yang digunakan memiliki struktur hierarki dengan empat tingkat: Australia, negara bagian, kawasan, dan tujuan kunjungan. Pendekatan bottom-up diterapkan untuk menghasilkan ramalan pada tingkat hierarki teratas dengan menggabungkan ramalan dari tingkat terendah. Evaluasi dilakukan dengan menggunakan metrik Symmetric Mean Absolute Percentage Error (SMAPE) pada setiap tingkat hierarki dan cakrawala peramalan. Hasil penelitian menunjukkan bahwa Metode Holt berkinerja terbaik pada tingkat Australia (SMAPE 3,26%–9,28%) dan tingkat negara bagian (6,96%–12,29%). Sementara itu, Holt-Winters mencapai kinerja terbaik pada tingkat wilayah (16,57%–21,43%) dan tingkat tujuan kunjungan (43,98%–47,63%). Penelitian ini menyoroti efektivitas Exponential Smoothing dalam menangkap pola dan tren musiman dalam hierarki data dan pentingnya pendekatan bottom-up dalam menghasilkan prakiraan yang konsisten di semua tingkat hierarkis.
Co-Authors Achmad Reza Fahcruroji Aisyah Muhayani Alamsyah, Mochammad Rafli Alkadrie, Syarifah Syila Andrew Fiade Aprilia, Ananda Bisyri, M. Alvi Devi Ila Octaviyani Hasanah, Sri Indriati Hidayat, Afnenda Rachmalia Inna, Suma Iqbal Rachadi Irene, Yanne Irma Fauziah Jasmin Nur Hanifa Khasanah, Dwi Windari Nur Kurnia, Hafsah Laili Nahlul Farih MAHMUDI Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Mega Maulina Muhammad Manaqib, Muhammad Nina Fitriyati Nina Fitriyati Nina Fitriyati Nur Inayah NURLIANA NURLIANA Ramadita, Mutiara Rizky Azriel Fahrezi Sari, Flori Ratna Suma’inna Suma’inna Yahya, Amelia Nur Yanne Irene Yanne Irene, Yanne Zulkifli, Dhea Urfina