Judul: Klasifikasi Kanker Serviks Pada Citra Pap Smear Menggunakan  Model Convolutional Neural Network
Abstrak: Kanker serviks menempati peringkat kedua sebagai jenis kanker yang paling banyak menyerang wanita dengan angka kematian sebesar 273.000 kasus per tahun. Pemeriksaan pap smear adalah salah satu upaya pencegahan kanker serviks. Pemeriksaan secara manual yang dilakukan dengan intensitas tinggi akan berdampak pada tingginya kesalahan pengamatan akibat kesalahan manusia. Penelitian ini bertujuan untuk membuat model convolutional neural network dengan performa paling baik dalam mengidentifikasi kanker serviks pada citra pap smear. Data yang digunakan dalam penelitian ini bersumber dari SIPaKMeD yang terdiri atas 4049 citra sel dan terbagi menjadi 5 kelas berdasarkan tingkat keganasan kanker serviks. Tahapan dalam penelitian ini meliputi praproses data, pembagian data, hyperparameter tuning, pelatihan model, dan evaluasi. Hasil dari penelitian ini adalah tiga model prediksi kanker serviks. Model dengan performa paling baik diraih oleh AlexNet dengan akurasi sebesar 99,02%.
Keyword: convolutional neural network, kanker serviks, pap smear, cervical cancer, convolutional neural network, pap smear

Judul: Cervical Cancer Histopathology Image Classification with Transfer Learning
Abstrak: Kanker mulut rahim (serviks) adalah kanker dengan jumlah kasus terbanyak kelima pada wanita di seluruh dunia dan merupakan masalah kesehatan perempuan di Indonesia dengan angka kejadian dan kematiannya yang tinggi. Tes skrining pap smear manual yang berfungsi sebagai pencegahan dan deteksi awal kanker serviks masih rawan akan terjadinya kesalahan observasi. Penelitian ini bertujuan untuk membuat model convolutional neural network dengan metode transfer learning untuk identifikasi kanker serviks pada citra pap smear. Data yang digunakan pada penelitian ini adalah dataset open source SIPaKMeD yang mengandung 4049 citra sel serviks normal, abnormal, dan jinak. Penelitian ini dilakukan melalui beberapa tahapan yaitu praproses data, pembagian data, hyperparameter tuning, pembuatan model prediksi, dan evaluasi model. Penelitian ini menghasilkan tiga model CNN berbeda dengan akurasi terbaik sebesar 99,04%., Cervical cancer is a cancer with the fifth highest number of cases in women worldwide and is a health problem in Indonesia with high incidence and death rate. Manual pap smear screening tests that function as prevention and early detection of cervical cancer are still prone to observation errors. This study aims to create a convolutional neural network with transfer learning method to identify cervical cancer on pap smear images. The data used in this study are the open source dataset SIPaKMeD that contains 4049 normal, abnormal, and benign cervical cell images. The study was carried out through several stages which are data preprocessing, data splitting, hyperparameter tuning, prediction model making, and model evaluation. Three different CNN models were made, having the best accuracy of 99.04%.
Keyword: cervical cancer, convolutional neural network, feature concatenation, pap smear, transfer learning

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
Keyword: