Judul: The Statistical Bias Correction for Rainfall Data around Lake Toba Using the Quantile Delta Mappings
Abstrak: Danau Toba, terletak di Sumatera Utara, merupakan danau tektonik dan vulkanik terbesar di Indonesia. Danau Toba memiliki iklim ekuatorial dengan ciri curah melimpah sepanjang tahun. Curah hujan tinggi ditambah dengan peningkatan tiap tahunnya akibat perubahan iklim menyebabkan rentannya terjadi cuaca ekstrim yang tidak dapat diprediksi sehingga merugikan warga sekitar. Dengan demikian, diperlukan suatu model prediksi curah hujan yang dapat mengantisipasi dampak curah hujan ekstrim tersebut. Salah satu model prediksi curah hujan adalah ERA5-Land. Namun model prediksi ini memiliki bias yang dapat dihindari. Metode yang dapat digunakan adalah koreksi bias statistik dengan quantile delta mapping (QDM) dengan mengkoreksi data model ERA5-Land terhadap data observasi BMKG. Metode QDM yang digunakan pada penelitian ini menggunakan dua jenis metode yaitu menggunakan sebaran perbulan dan lengkap. Hasilnya, kedua metode dapat memperbaiki bias pada Stasiun Silaen, Laguboti dan Doloksanggul, dan juga memperbaiki model di musim kemarau iklim ekuatorial di Mei, Juni, Juli, dan Agustus. Namun metode pertama lebih memperbaiki sebaran model di Silaen, dan Laguboti, sedangkan metode kedua memperbaiki sebaran model di Doloksanggul., Lake Toba, located in North Sumatra, is the largest tectonic and volcanic lake in Indonesia. Lake Toba has an equatorial climate characterized by abundant rainfall throughout the year. High rainfall, coupled with annual increases due to climate change, results in vulnerability to unpredictable extreme weather, causing harm to surrounding communities. Consequently, a rainfall prediction model is needed to anticipate the impacts of such extreme rainfall. One of the rainfall prediction models used is ERA5-Land. However, this prediction model has biases that can be avoided. A method that can be used is statistical bias correction using quantile delta mapping (QDM) by correcting ERA5-Land model data against BMKG observation data. The QDM method used in this study employs two types of methods: monthly and full distribution. The results show that both methods can improve biases at Silaen, Laguboti, and Doloksanggul stations, as well as improve the model during the equatorial dry season in May, June, July, and August. However, the first method improves the model distribution more in Silaen and Laguboti, while the second method improves the model distribution more in Doloksanggul.
Keyword: curah hujan, Danau Toba, quantile delta mapping

Judul: Comparison of Bias Correction Methods on ECMWF Precipitation Data as the Basis for Meteorological Drought Analysis
Abstrak: Kekeringan meteorologis disebabkan oleh curah hujan yang rendah dalam kurun waktu tertentu. Riau merupakan salah satu provinsi di Indonesia yang rawan mengalami kekeringan. Analisis kekeringan dapat dilakukan dengan menghitung indeks kekeringan salah satunya menggunakan metode Standardized Precipitation Index (SPI). Menghitung SPI memerlukan data curah hujan sebagai input yang dapat diperoleh melalui data observasi ataupun data model satelit jika ketersediaan data observasi terbatas. Namun, luaran data model masih memiliki bias terhadap data observasi sehingga perlu dikoreksi sebelum digunakan. Penelitian ini bertujuan membandingkan dua metode koreksi bias pada data curah hujan model European Center for Medium Range Weather Forecast (ECMWF) di Provinsi Riau tahun 2007-2016 dan mengidentifikasi metode yang menghasilkan luaran data terbaik, yaitu memiliki korelasi tertinggi dan nilai error (RMSE) terendah terhadap curah hujan observasi. Metode koreksi bias yang digunakan yaitu Quantile Mapping dan Rasio Rata-rata. Curah hujan hasil koreksi terbaik kemudian digunakan untuk menghitung SPI. Hasil analisis menunjukkan bahwa curah hujan ECMWF yang dikoreksi menggunakan metode Rasio rata-rata memberikan hasil terbaik dibandingkan metode Quantile Mapping karena memiliki korelasi tertinggi dan RMSE terendah. Berdasarkan analisis temporal SPI di Provinsi Riau (rata-rata seluruh grid) tahun 2007-2016 menggunakan curah hujan ECMWF terkoreksi metode Rasio Rata-rata, telah terjadi kekeringan sebanyak 17 kali dengan rata-rata durasi kejadian selama 2 bulan dan durasi maksimum hingga 4 bulan. Pada bulan Oktober tahun 2015 Provinsi Riau (bagian selatan) mengalami kekeringan yang diikuti oleh kejadian IOD positif (menandakan kekeringan di Indonesia) dengan nilai SPI mencapai -1,77 dan masuk ke dalam kategori sangat kering., Meteorological drought caused by low precipitation within a certain period of time. Riau is one of the provinces in Indonesia which is prone to drought events. Drought analysis can be done by calculating the drought index named Standardized Precipitation Index (SPI). The drought index (SPI) requires precipitation data as an input that can be obtained through observation data or satellite data model if the observation data are limited. However, satellite data model has a bias against observation data and needs to be corrected before being used as an input. This study aims to compare two different bias data correction methods on European Center for Medium Range Weather Forecast (ECMWF) precipitation data in Riau Province on 2007-2016 which shows the best corrected data output (highest correlation and lowest RMSE). The bias correction method that used in this study are Quantile Mapping and Average Ratio. The best corrected output of ECMWF precipitation data then used to calculate SPI. The results of this analysis show that the Average Ratio method has the better results than Quantile Mapping method as it has a lower error value (RMSE) and higher correlation (r). Based on temporal SPI analysis in Riau Province (all grids average) on 2007-2016 using corrected ECMWF precipitation by Average Ratio method shows that there were 17 times of drought with 2 months average duration and a maximum duration is 4 months. In October 2015, Riau Province (southern area) suffered drought which was followed by positive IOD (indicating drought in Indonesia) with -1,77 SPI value and classified as the very dry category.
Keyword: average ratio, meteorological drought, quantile mapping, SPI

Judul: Prenilasi Senyawa β-Diketon sebagai Prekursor 3-Prenilflavon
Abstrak: One group of flavonoids that has not been commonly synthesized is 3- prenylflavone. The main objective of this experiment was to synthesize precursor of 3-prenylflavone. Firstly, β-diketone was synthesized via 4 steps from phenol as starting compound with total yield of 24%. Prenylation of β-diketone with prenyl bromide and K2CO3 base produced 4 fractions which were separated by using preparative thin layer chromatography (PTLC) with n-hexane:EtOAc (8:2) as the eluent. Fraction with Rf = 0.80 was further separated by using preparative PTLC with n-hexane:EtOAc (99:1) as the eluent. Two fractions were obtained with Rf = 0.38 and 0.42. The first fraction has been characterized as diprenylated β-diketone with 49% yield. Application of protecting group on the phenolic OH group in β- diketone are needed to obtain monoprenylated β-diketone which can be further cyclized into 3-prenylflavone
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