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: 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: Sectoral Effects Of Monetary Policy
Abstrak: The purposes of this study are three folds: (1) identifying which channels are effective for transmission of monetary policy; (2) assessing whether the transmission mechanisms at industry level are consistent with that at aggregate level; and (3) explaining why and how a channel becomes effective. The analyses are facilitated impulse response function (IRF) derived from a vector autoregression (VAR) model. Three channels are identified in this study: interest rate, real credits and exchange rate channels. The IRF results show that exchange rate is the only effective channel in affecting aggregate output and at least in five industries (the manufacturing industry; the construction industry; the trade, hotel, and restaurants industry; the transport and communication industry; and the finance, real estate, and business services industry). Meanwhile, the other two channels are not effective at aggregate and all industry. This finding suggests that there is some form of consistency between aggregate effects and industry effects. Moreover, the effect of real depreciation tend to be contractionary and thus supporting the idea of Krugman and Taylor (1976) among others. Output contraction seems to be associated with inflationary effect of the depreciation. This is a truly Keynesian type demand side story: following a real currency depreciation, purchasing power tends to weaken which then soaks the aggregate demand.
Keyword: transmission of monetary policy, industry level, IRF