Judul: Klasifikasi Kanker Payudara Menggunakan Arsitektur Inception-V3 dan Machine Learning
Abstrak: Kanker payudara adalah penyakit yang timbul diakibatkan oleh sel jaringan payudara yang tumbuh secara tidak normal dan terus menerus. Penyakit ini merupakan penyakit dengan jumlah kenaikan yang besar sekitar 13 juta pertahun, dengan tingkat kematian persentase 9,60% dari total 65.858 kasus. Deteksi kanker payudara sejak dini untuk pencegahan perlu dilakukan, dengan harapan kanker payudara lebih mudah ditangani dan disembuhkan bahkan bisa dicegah sebelum memasuki stadium lanjut. Pada penelitian ini membangun model dengan teknik transfer learning untuk klasifikasi kanker payudara. Terdapat 4 metode yang diujikan yaitu ekstraksi fitur Inception-V3 dengan metode klasifikasi Radial Basic Function Neural Network, Feed Forward Neural Network, Logistic Regression dan ekstraksi fitur dengan melakukan perubahan pada hyperparameter layer. Penelitian ini membandingkan ke empat model tersebut untuk mendapatkan yang terbaik untuk mengatasi masalah klasifikasi kanker payudara. Data yang digunakan pada penelitian ini adalah data citra kanker payudara dengan zoom scale 40X, 100X, 200X dan 400X. Dataset bersumber dari The Laboratory University of Parana dengan P&D Laboratory Pathological Anatomy and Cytopathology, Parana, Brazil. Hasil penelitian ini menunjukkan metode ekstraksi fitur Inception-V3 dengan metode klasifikasi Logistic Regression pada data zoom scale 40X memberikan akurasi terbaik (93,00%), presisi (94,00%), dan recall (91,00%) F1-score (92,00%). Skenario pelatihan Inception-V3 dengan fine tunning dilakukan perubahan hyperparameter layer: 164 dan epoch 100 menghasilkan akurasi terbaik yaitu dengan akurasi 90,00%, presisi 78,00%, recall 91,00% dan F1-score 82,00%.
Keyword: Algoritme Machine Learning, Convolutional Neural network, Inception-V3, Kanker Payudara, Transfer Learning

Judul: Classification of Histopathological Images of Breast Cancer with Methods CNN and Transfer Learning
Abstrak: Kanker payudara adalah kanker dengan jumlah kasus terbanyak pertama di dunia dan di Indonesia. Kanker payudara memiliki kontribusi sebesar 11,7% (2.261.419) dari total kasus baru kanker di dunia serta 16,6% (65,858) di Indonesia yang tercatat di tahun 2020. Pemeriksaan kanker umumnya menggunakan teknik biopsi dengan melakukan pengambilan sampel jaringan yang dicurigai terdapat sel kanker. Penelitian ini bertujuan untuk membuat model convolutional neural network dengan metode transfer learning dalam memprediksi status kanker payudara pada citra histopatologi. Data yang digunakan dalam penelitian ini bersumber dari dataset BreakHis yang terdiri atas 7,909 citra histopatologi jaringan tumor payudara dan terbagi menjadi dua kelompok utama, yaitu tumor jinak dan tumor ganas. Tahapan dalam penelitian ini meliputi praproses data, pembagian data, pelatihan model, dan evaluasi. Hasil dari penelitian ini adalah model CNN yang dapat membantu proses klasifikasi kanker payudara pada citra histopatologi. Penelitian ini menghasilkan dua model CNN berbeda dengan akurasi terbaik sebesar 96%., Breast cancer is the cancer with the highest number of incidences, both in Indonesia and in the world. Breast cancer contributes up to 11,7% (2,261,419) of the total new cases of cancer in the world and 16,6% (65,858) in Indonesia (recorded in 2020). Cancer examination generally uses a biopsy technique by taking tissue samples suspected of having cancer cells. This study aims to create a convolutional neural network model with transfer learning method in predicting breast cancer status on histopathological images. The data used in this study were sourced from the BreakHis dataset which consisted of 7,909 histopathological images of breast tumor tissue and was divided into two main groups, namely benign tumors and malignant tumors. The stages in this research include data preprocessing, data sharing, model training, and evaluation. The result of this research is a convolutional neural network model that can help classify breast cancer in histopathological images. Two different CNN models were created with the best accuracy of 96%.
Keyword: breast cancer, convolution neural network, histopathology, transfer  learning, cnn

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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