Judul: Rainfall Classification Modelling in Bogor District Using
Random Forest Method.
Abstrak: ADE MUSTHOPIC. Pemodelan Klasifikasi Curah Hujan di Kabupaten Bogor Menggunakan Metode Random Forest. Dibimbing oleh FARIT MOCHAMAD AFENDI dan ANWAR FITRIANTO Kabupaten Bogor merupakan wilayah yang memiliki curah hujan cukup tinggi, dimana berdasarkan penelitian BMKG pada Februari 2021 menunjukan rata-rata curah hujan di Kabupaten Bogor berada di angka 300 mm/bulan. Tingginya curah hujan yang terus menerus dapat berdampak buruk dalam bidang pertanian, dan berpotensi terjadinya bencana alam. Penelitian ini bertujuan untuk melakukan klasifikasi terhadap curah hujan di Bogor dengan menggunakan komponen-komponen iklim sebagai variable pendukung menggunakan metode Random Forest. Kemudian dilakukan perbandingan kinerja model sebelum dan setelah dilakukannya penanganan data tidak seimbang dengan SMOTE. Sebelum dilakukannya SMOTE, nilai akurasi, sensitivitas, spesifisitas, skor F1 berturut-turut adalah 76%, 34%, 88%, and 40%. Sedangkan setelah dilakukan SMOTE nilai akurasi, sensitivitas, spesifisitas, skor F1 berturut-turut adalah 72%, 57%, 76%, and 49%. Dari data yang disimpulkan bahwa SMOTE tidak berpengaruh signifikan terhadap kinerja model klasifikasi. Kata Kunci : curah hujan, random forest, SMOTE, Bogor is one of the district which have high rainfall level, based on BMKG research in February 2021 shows that average rainfall level in February is 300 mm/month. However, continuous high rainfall which have negative impact on agriculture, and also has the potential impact for natural disasters. This research is purposes to conducting rainfall classifications modelling in Bogor using climate component as supporting variable using random forest method. Then a comparison of model performance will be carried out before and after handling unbalaced data with SMOTE. The result obtained were that the model before SMOTE had accuracy, sensitivity, specificity, f1-score respectively 76%, 34%, 88%, and 40%. Whereas after SMOTE the accuracy, sensitivity, specificity, and f1-score respectively 72%, 57%, 76%, and 49%. Based on the result, it can be conclude that SMOTE has no significant effect on the performance of the classification model.
Keyword: curah hujan, random forest, SMOTE

Judul: The Application of LSTM Method with Imbalanced Data Handling on Wet Hydrometeorological Disaster Event Classification in Bogor City
Abstrak: Banjir dan longsor adalah bencana dominan di Indonesia dengan tingkat kejadian tinggi setiap tahunnya. Kota Bogor memiliki tingkat kerentanan di atas 50% dan merupakan salah satu daerah dengan kerentanan bencana tertinggi di Jawa Barat. Penelitian ini menggunakan data parameter iklim dan data bencana historis di Kota Bogor yang memiliki urutan waktu, sehingga penelitian dilakukan dengan model Long Short-Term Memory (LSTM). Data yang digunakan memiliki rentang harian dari 1 Januari 2008 hingga 31 Desember 2023. Penelitian ini bertujuan untuk membandingkan metode penanganan data yang tidak seimbang dengan menerapkan pembobotan kelas dan Synthetic Minority Over-sampling Technique (SMOTE) dan memprediksi kejadian bencana dengan model terbaik. Hasil analisis membuktikan bahwa pembobotan kelas dan SMOTE dapat membantu model untuk mengklasifikasikan kelas minoritas, ditunjukkan dengan peningkatan signifikan dalam nilai sensitivitas dan recall. Model LSTM terbaik dengan teknik pembobotan kelas mencapai nilai sensitivity sebesar 0,4688, specificity sebesar 0,7387, precision sebesar 0,5412, recall sebesar 0,6037, dan F1-score sebesar 0,5247. Hasil prediksi menunjukkan bahwa banyaknya bencana hidrometerologi basah tertinggi terjadi pada bulan November dan Desember yang sejalan dengan karakteristik musim hujan di daerah tersebut., Floods and landslides are the dominant disasters in Indonesia with high incidence rates every year. Bogor city has a vulnerability level above 50% and is one of the areas with the highest disaster vulnerability in West Java. This study utilized climate parameter data and historical disaster data in the Bogor city which have sequence properties, so the research proceeds with the Long Short-Term Memory (LSTM) method. The data used have a daily range from January 1st 2008, to December 31th 2023. This research aims to compare the methods of handling unbalanced data by applying class weighting and Synthetic Minority Over-sampling Technique (SMOTE) and predicting disaster events with the best model. The analysis results proved that class weighting and SMOTE can help the model to classify the minority class, indicated by a significant increase in sensitivity and recall values. The best LSTM model with the class weighting technique achieved a sensitivity value of 0.4688, specificity of 0.7387, precision of 0.5412, recall of 0.6037, and an F1-score of 0.5247. The prediction results indicated that the highest number of wet hydrometeorological disasters occur in November and December, aligning with the rainy season characteristics in the area.
Keyword: data tidak seimbang, LSTM, hidrometeorologi basah, klasifikasi biner

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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