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Pengaruh Siklon Tropis Cempaka Terhadap Curah Hujan Harian Di Wilayah Jawa Dan Madura

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Pengaruh Siklon Tropis Cempaka Terhadap Curah Hujan Harian Di Wilayah Jawa Dan Madura PENGARUH SIKLON TROPIS CEMPAKA TERHADAP CURAH HUJAN HARIAN DI WILAYAH JAWA DAN MADURA THE IMPACT OF TROPICAL CYCLONE CEMPAKA ON DAILY RAINFALL OVER JAVA AND MADURA ISLANDS Muhammad Najib Habibie1)*, Sri Noviati1), Hastuadi Harsa1) 1)Pusat Penelitian dan Pengembangan, Badan Meteorologi, Klimatologi dan Geofisika *Email: [email protected], [email protected], [email protected] Naskah masuk : 9 Februari 2018 Naskah diperbaiki : 16 Juli 2018 Naskah diterima: 7 Desember 2018 ABSTRAK Siklon tropis memiliki pengaruh yang sangat luas terhadap beragam parameter meteorologi. Analisis terhadap kejadian siklon tropis dapat dilakukan pada berbagai macam parameter dengan berbagai teknik yang berbeda. Pada penelitian ini dibahas mengenai identifikasi pola data curah hujan di Jawa dan Madura bertepatan dengan terjadinya siklon tropis Cempaka pada akhir bulan November 2017 di perairan selatan Jawa. Analisis data dilakukan menggunakan Self-Organizing Map (SOM) dan Hierarchical Clustering. Hasil yang diperoleh menunjukkan terdapat 4 kelompok pola curah hujan dominan berkaitan dengan siklon tropis Cempaka. Analisis temporal dan spasial terhadap keempat kelompok tersebut menunjukkan bahwa siklon tropis Cempaka berpengaruh kuat terhadap sebaran curah hujan di Jawa dan Madura. Kata kunci: siklon tropis, curah hujan, self organizing maps ABSTRACT Tropical cyclones have strong influences on numerous meteorological parameters. An analysis of a tropical cyclone event can be conducted for various parameters by using different techniques. This study investigated the rainfall patterns over Java and Madura Islands during the occurrence of Cempaka tropical cyclone that struck the southern coast of Java at the end of November 2017. The Self-Organizing Map (SOM) and the Hierarchical Clustering (HC) were utilized as the analysis tools. The results showed that there were 4 dominant rainfall patterns related to the Cempaka tropical cyclone. The temporal and spatial analysis of those patterns showed that Cempaka tropical cyclone had strongly affected the distribution of rainfall in Java and Madura. Keywords : tropical cyclone, rainfall, self organizing maps 1. Pendahuluan berupa peningkatan curah hujan [1, 2, 3] dan kece- patan angina [4] serta adanya gelombang tinggi [5]. Siklon tropis adalah gangguan atmosfer berupa Dampak tak langsung adalah dampak lanjutan akibat aktifitas sirkulasi vorteks di atmosfer akibat adanya adanya peningkatan aktivitas meteorologi tersebut, tekanan rendah di suatu tempat. Pada umumya, di antaranya dapat berupa banjir [6], tanah longsor siklon tropis terjadi di daerah lintang menengah. [7, 8], erosi dan perubahan ekosistem pantai [9], Akan tetapi, tidak tertutup kemungkinan gangguan banjir pantai (coastal flooding) dan kerusakan ini terjadi pula di lintang rendah seperti di Indonesia. berbagai infrastruktur [5]. Meskipun probabilitas terjadinya sangat kecil, siklon tropis di Indonesia pernah terjadi setidaknya 6 kali, Pada umumnya, kemunculan siklon tropis akan di antaranya Siklon Tropis Vamei (26-31 Desember diikuti oleh kejadian cuaca ekstrim. Kejadian cuaca 2001), Siklon Tropis Inigo (30 Maret - 9 April ekstrim tersebut seringkali memiliki dampak yang 2003), Siklon Tropis Durga (22-24 April 2008), sangat besar terhadap perekonomian. Estimasi Siklon Tropis Kirrily (26-27 April 2009), Siklon kerugian secara ekonomi sebagai akibat dari siklon Tropis Cempaka (26-29 November 2017) dan Siklon tropis telah dikemukakan oleh Mendelshon dkk, Tropis Dahlia (29 November - 4 Desember 2017). 2012 [10] dengan menyertakan prediksi perubahan iklim terhadap kemunculan siklon tropis yang pada Siklon tropis secara umum memiliki dampak akhirnya akan merusak berbagai aset di sekitar langsung dan tak langsung. Dampak langsung siklon daerah lintasan siklon tropis. Menurut hasil tropis adalah kondisi meteorologi yang penelitian tersebut, wilayah yang mengalami menyertainya secara bersamaan pada saat siklon kerugian ekonomi paling besar adalah Amerika berlangsung. Dampak langsung ini antara lain Tengah. Di samping itu, wilayah Amerika Utara, PENGARUH SIKLON TROPIS CEMPAKA............................................................................Muhammad Najib Habibie, dkk 1 kepulauan Pasifik, dan Asia Timur juga memiliki memberi gambaran sebaran data menjadi 5 resiko kerugian ekonomi di atas rata-rata meskipun kelompok yaitu minimum, kuartil pertama, median, tidak sebesar wilayah Amerika Tengah. Penelitian kuartil ketiga, dan maksimum. Rentang antara sejenis mengenai dampak cuaca ekstrim terhadap kuartil pertama dengan ketiga disebut sebagai bidang ekonomi juga telah dilakukan diantaranya interquartil range (IQR). Penerapan box plot ini oleh Neumann dkk, 2015 [11] untuk wilayah pesisir pada umumnya tidak hanya menampilkan data Vietnam, untuk bidang ketahanan pangan dan suplai maksimum dan minimum pada batas atas dan rantai makanan di Australia oleh Beer, 2018 [12], bawahnya, akan tetapi menampilkan juga data untuk mitigasi resiko pengembangan wilayah pantai pencilan. Data pencilan didefinisikan sebagai data di New York oleh Lin & Shullman, 2015 [13]. yang nilainya 3 kali dari IQR, atau nilainya 3 kali Bahkan Yu dkk, 2017 [14] telah melakukan kajian atau lebih dari kuartil ke-3 atau kurang dari 3/4 nilai mengenai dampak cuaca ekstrim terhadap faktor kuartil pertama. Nilai IQR sendiri ditetapkan sosial-ekonomi, yang dalam hal ini berupa tingkat berdasarkan distribusi normal data dan nilainya kriminal selama cuaca ekstrim berlangsung. Di adalah 1,35 x σ. Dimana σ adalah simpangan baku samping itu, menurut Deng dkk, 2015 [15] dan yang dapat dirumuskan sebagai berikut: Zheng dkk, 2017 [16] terdapat pula masalah kese- 2 hatan yang ditimbulkan setelah kejadian siklon ∑푛 푥2−(∑푛 푥 ) /푛 σ = √ 푖=1 푖 푖=1 푖 ………….. (1) tropis. Dampak terpenting yang sangat perlu 푛−1 mendapat perhatian besar dari kejadian siklon tropis adalah kemungkinan timbulnya korban jiwa, seperti Nilai pencilan tidak selalu merupakan nilai yang dikemukakan oleh Su dkk, 2016 [17]. Oleh karena buruk, bisa jadi nilai ini merupakan nilai yang paling hal tersebut, maka pemahaman yang mendalam penting dan informatif dari suatu rangkaian data. mengenai fenomena siklon tropis perlu dimiliki untuk meminimalisir kerugian yang mungkin timbul. Self Organizing Map. Pola nilai curah hujan dibentuk dari data seluruh pos hujan. Pembentukan Kerugian secara ekonomi akibat kemunculan siklon pola ini dikerjakan oleh Self Organizing Maps tropis sering diakibatkan oleh banjir, seperti yang (SOM). SOM adalah salah satu algoritma jaringan diuraikan oleh Villarini, 2016 [18] bahwa kejadian syaraf tiruan pada bidang kecerdasan buatan [20, 21, banjir di sebelah Barat dan Timur Amerika yang 22]. Prinsip kerja utama SOM adalah menggali cenderung terjadi di bulan Oktober-Maret sangat informasi mengenai pola (juga disebut sebagai dipengaruhi oleh aktivitas siklon tropis. Dengan karakter, perilaku, atau ciri) yang terdapat pada data. asumsi bahwa siklon tropis memicu intensitas hujan Pola yang dimiliki oleh suatu data seringkali sukar yang tinggi, maka penelitian ini bertujuan meninjau dan membutuhkan waktu yang lama untuk karakteristik curah hujan di Jawa dan Madura diidentifikasi secara manual. Salah satu faktor yang sehubungan dengan kejadian siklon tropis Cempaka. menyebabkan hal tersebut adalah interaksi antar Siklon tropis Cempaka menerjang wilayah Indonesia variabel (parameter) dari data yang memiliki di bulan November 2017. Bibit siklon tropis ini hubungan non-linier. Faktor non-linieritas ini terdeteksi pada 26 November 2017 di Selatan Jawa memerlukan model matematis yang rumit dan Tengah [19]. panjang untuk diuraikan. SOM dapat digunakan sebagai model alternatif untuk mengatasi hal 2. Metode Penelitian tersebut dan menghasilkan penyederhanaan. Data. Data hujan yang digunakan dalam penelitian ini berasal dari pos pengamatan hujan yang berada di Pulau Jawa dan Madura. Data tersebut berupa curah hujan harian pada bulan November 2017, dimana siklon tropis Cempaka berlangsung. Jumlah stasiun yang digunakan dalam penelitian ini adalah 629 yang tersebar di seluruh Jawa dan Madura. Data tersebut disusun dalam bentuk matriks dengan ukuran 629 lokasi × 30 hari, dengan lokasi disusun sebagai baris dan hari sebagai kolom. Secara statistik, baris di matriks data tersebut dapat dianggap sebagai sampel dan kolom sebagai variabel (atau parameter). Bahan analisa terhadap matriks data tersebut adalah hasil pengelompokan oleh Hierarchical Clustering (HC) terhadap Gambar 1 diagram SOM pembentukan pola yang diberikan oleh SOM. Struktur SOM ditunjukkan oleh gambar 1. SOM Diagram box plot. Sebaran data hujan harian terdiri dari lapisan input dan output. Masing-masing dianalisis menggunakan metode box plot. Metode ini lapisan disusun oleh node. Node di lapisan input JURNAL METEOROLOGI DAN GEOFISIKA VOL. 19 NO. 1 TAHUN 2018 : 1 – 11 2 mewakili parameter dari matriks data, dalam hal ini oleh persamaan 4. Node-node di sekitar 푦푐 (node adalah nilai curah hujan November 2017 (30 tetangga) juga diperbarui dengan syarat bahwa parameter). Lapisan output SOM terdiri dari node indeks 푗, 푘 node-node tetangga tersebut berada di yang disusun dalam bidang dua dimensi dengan dalam radius 푁푐, dimana 푁푐 adalah besaran untuk susunan 8×8 dan dihubungkan oleh bobot (weight) menentukan apakah suatu node mengalami ke setiap node lapisan input. Node lapisan output pembaruan bobot atau tidak. Selain 푁푐, terdapat pula memiliki jumlah parameter sebanyak parameter parameter konstanta pembelajaran α (learning rate) sampel matriks data input, sehingga SOM juga
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