Judul: Deteksi Anomali Pada Transaksi Kartu Kredit Menggunakan Adaptive Synthetic Resampling(ADASYN) dan Support Vector Machine (SVM)
Abstrak: Munculnya anomali atau transaksi penipuan akibat banyaknya transaksi yang terjadi pada kartu kredit, menjadikan perlunya deteksi anomali pada transaksi kartu kredit agar tidak merugikan pihak nasabah maupun bank. Salah satu kendala deteksi anomali tersebut adalah pembentukan model matematis yaitu dataset yang memiliki distribusi kelas yang tidak seimbang. Salah satu contohnya adalah transaksi anomali kartu kredit yang mana jumlahnya lebih sedikit dibandingkan data transaksi normal yang terjadi. Walaupun transaksi penipuan hanya berjumlah sedikit, namun memiliki pengaruh yang sangat besar. Maka dari itu, diperlukan model untuk melakukan screening untuk memperkirakan transaksi penipuan. Tujuan penelitian ini adalah untuk menghasilkan data latih yang ideal dari dataset yang tidak seimbang menggunakan Adaptive Synthetic Sampling (ADASYN) agar dapat digunakan untuk proses pelatihan model Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa model SVM yang dilatih dengan mengunakan data SV yang diseimbangkan dengan ADASYN mampu mengklasifikasikan transaksi anomali dengan sangat baik., There are anomalies or fraudulent transactions due to the large number of transactions that occur on credit cards, making it necessary to detect anomalies in credit card transactions so as not to harm the customer or bank. One of the obstacles in detecting these anomalies is the formation of mathematical models, namely datasets that have an unbalanced class distribution. One example is anomalous credit card transactions, which are fewer in number than normal transaction data. Although the transactions are only small, they have a very large impact. Therefore, a model is needed to perform screening to estimate fraudulent transactions. The purpose of this research is to produce ideal training data from an unbalanced dataset using Adaptive Synthetic Sampling (ADASYN) so that it can be used for the Support Vector Machine (SVM) model training process. The results showed that the SVM model trained using SV data balanced with ADASYN was able to classify anomalous transactions very well
Keyword: Adaptive Synthetic Sampling, transaksi kartu kredit, support vector  machine

Judul: Deteksi Penipuan Kartu Kredit menggunakan Support Vector Machine dengan Optimasi Grid Search dan Genetic Algorithm
Abstrak: Transaksi kartu kredit mengalami peningkatan yang signifikan setiap tahunnya. Seiring dengan meningkatnya pemakaian kartu kredit maka meningkat pula risiko tindak penipuan oleh oknum yang tidak bertanggung jawab. Kemungkinan transaksi penipuan kartu kredit dapat terdeteksi melalui bantuan pembelajaran mesin. Permasalahan utama dalam mengolah data transaksi ialah dimensinya yang besar, kelas yang tidak seimbang, dan diperlukan proses pendeteksian dengan waktu komputasi yang singkat. Maka dari itu dibutuhkan model serta algoritma optimasi yang tepat untuk mengatasi permasalahan tersebut. Tujuan penelitian ini adalah membangun model pendeteksian transaksi penipuan kartu kredit yang mampu menghasilkan performa baik serta waktu komputasi singkat menggunakan metode support vector machine (SVM) dengan optimasi grid search dan genetic algorithm. Dari lima model yang dibangun, diperoleh bahwa model SVM menggunakan dataset SV dengan grid search sebagai teknik optimasi hyperparameter mampu melakukan pendeteksian dengan baik dan waktu komputasi yang singkat. Model berikut memiliki nilai F2-measure terbaik yaitu 99.96%.
Keyword: support vector machine, genetic algorithm, grid search

Judul: Biology and ecology of the buffalo fly, haematobia exigua de meijere (diptera:muscidae)
Abstrak: The life history of the buffalo fly was observed at room temperature of 27°-29.5°C and at a relative humidity of 48-68 percent. The eggs hatched after 17-22 hours, with an average of 18 hours. First instar larvae measured 1.25 + 0.16 mm long and 0.27 + 0.037 mm wide. reach the second larval stage. It took 6-12 hours to the second instar larvae measured 2.676 +0.187 mm long and 0.353 +0.028 mm wide. The second instar took about 12-24 hours to reach the third larval stage. The third instar measured 6.833 + 0.189 mm long and 0.919 + 0.051 mm wide. It took about 2-3 days for the third instar to become pupae. Duration of the pupal stage was about 5-7 days before the adults emerged. Under laboratory condition, the buffalo fly completed its life cycle in 9.5 to 10.5 days, whereas under field condition it took 9-14 days with an average of 10.5 days. The growth ratio which was based on the measurements of the cephalo-pharyngeal skeleton of each instar was 1.797. The preoviposition period was 8-12 days and mating usually occurred on the 6th or 7th day. The longevity of the male was 29 days, whereas it was 32 days in the female. The range of egg production under laboratory condition was between 20-139 per female fly. The belly and hind legs of the carabao were the most preferred habitat either during cloudy and cool dry or shiny and warm day. When the weather was clear and the carabao was in the shade, the flies tended to be more distributed particularly on the shoulder, dewlap, back and horns. The carabaos had relatively fewer Musca sp. and Stomoxys calcitrans than cattle; never the less carabaos had a higher infestation of buffalo fly than cattle. Oviposition occured either during the day or night but the buffalo flies were more active during the night than in the day time. The effect of various lengths of exposure time of manure in relation to the oviposition was profound. observed, the maximum number of the buffalo flies was occurred when the dung was exposed for 30 minutes. With increasing time of exposure the number and variety of insects also increase. It was evident that Sepsidae and psychodidae were the most dominant insects emerging from the dung. The number of buffalo flies also increased with increasing
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