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"ABSTRAKThe agricultural sector has been given the largest contribution to economy Indonesia. Besides being able to absorb large numbers of workers, the agricultural sector also has an important role in reducing poverty vulnerability in rural households. This study aims to analyze the role of institutions and technological use in food security which proxied by the food production index in Indonesia, both in the short and long term. The analytical technique for estimating short term and long term relationships in this study is the Autoregressive Distributed Lag ARDL model using time series data from 1978 until 2014. The results showed that in the short term land availability, technological use in technology tractor machines and the availability of electricity had a positive and significant effect on food production in Indonesia. Whereas in the long term, land availability and GDP per capita are positive and significantly enhances food security. In addition, the institutional framework proxied by political rights and civil liberties has significant positive and negative effects in the long term. Variable land availability is the biggest factor in increasing food security in Indonesia so that a policy that effectively regulates agricultural land use needs to be made. Interaction between the society, farmers and the government is also needed to create synergies and contributions related to food production. The provision of social security to farmers, especially when there is a shock, and the policy of procurement of agricultural technology must also be considered to maintain national food security in the long run."

"The book provides a panoramic approach to social exclusion, with emphasis on structural causes (education, health, accidents) and on short term causes connected with the crisis which started in 2008. The picture emerging, based on econometric analysis, is that the crisis has widened the risk of social exclusion, from the structural groups, like disabled people and formerly convicted people, to other groups, like the young, unemployed, low skilled workers and immigrants, in terms of income, poverty, health, unemployment, transition between occupational statuses, participation, leading to a widening of socio-economic duality. It has also been stressed the relevance of definitions of socio-economic outcomes for the evaluation of the crisis, and their consequences to define interventions to fight socio-economic effects of the economic downturn. The adequacy of welfare policies to cope with social exclusion, especially during a crisis, has been called into question."

"Pemantauan harga komoditas strategis merupakan pekerjaan yang penting karena kontribusi signifikan yang dimiliki oleh komoditas strategis terhadap perhitungan laju inflasi. Untuk membantu menyelesaikan pekerjaan ini, dibutuhkan metode prediksi terbaik yang mampu memprediksi pergerakan harga komoditas pangan strategis. Penelitian ini memiliki tujuan untuk menemukan model prediksi terbaik di antara Long-Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU), dalam memprediksi harga harian sepuluh komoditas pangan strategis: bawang merah, bawang putih, beras, cabai merah, cabai rawit, daging ayam, daging sapi, gula pasir, minyak goreng, dan telur ayam. Model ARIMA digunakan sebagai standar model klasik dalam penelitian kali ini. Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE), GRU memberikan hasil prediksi harga harian paling baik pada enam dari total sepuluh komoditas dan LSTM memberikan hasil prediksi terbaik pada empat komoditas sisanya. Model terbaik pada tiap komoditas berhasil mengurangi angka MAE dari ARIMA sekitar 3% hingga 43%. Ketika model mempelajari data, GRU berhasil menyelesaikan prosesnya lebih cepat daripada LSTM pada delapan komoditas. Model peramalan terbaik yang ditemukan pada penelitian kali ini dapat digunakan untuk memperbaiki metode peramalan klasik yang telah digunakan dalam memprediksi harga harian pangan Indonesia, sehingga dapat membantu pemerintah dalam memformulasikan kebijakan dan peraturan terkait manajemen stabilitas harga pangan.

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Managing strategic commodities prices in the market is considered an important task since they have a significant contribution to the calculation of the inflation rate. To aid this task, it is necessary to find the best forecasting model that can predict commodities daily price. This paper aims to find the best prediction model between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in forecasting the daily price of ten Indonesia’s strategic commodities: shallot, garlic, rice, chili pepper, cayenne pepper, broiler meat, topside beef, granulated sugar, cooking oil, chicken egg. This research used ARIMA as a benchmark model. Based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), GRU gave the best result in predicting the daily price of six out of ten commodities. It is found that the best model for each commodity managed to reduce the MAE score from ARIMA by around 3% until 43%. GRU managed to finish faster than LSTM in training eight commodities data. The best forecasting method found in this research can be used to improve the classic method to forecast the daily price of Indonesia’s food commodities in assisting the government in formulating policies and regulations related to food price management."

"Respecting the autonomy of disabled people is an important ethical issue for providers of long-term care. In this influential book, George Agich abandons comfortable abstractions to reveal the concrete threats to personal autonomy in this setting, where ethical conflict, dilemma and tragedy are inescapable. He argues that liberal accounts of autonomy and individual rights are insufficient, and offers an account of autonomy that matches the realities of long-term care. The book therefore offers a framework for carers to develop an ethic of long-term care within the complex environment in which many dependent and aged people find themselves. Previously published as Autonomy and Long-term Care, this revised edition, in paperback for the first time, takes account of recent work and develops the author's views of what autonomy means in the real world. It will have wide appeal among bioethicists and health care professionals."

"Saham merupakan salah satu surat berharga yang diterbitkan dan dijual oleh perusahaan,yang telah memenuhi syarat, di Bursa Efek Indonesia. Prinsip dasar yang dimiliki olehsaham adalah High Risk High Reward, yang menggambarkan bahwa saham memangdapat memiliki hasil yang besar, namun memiliki risiko yang tinggi pula. Denganprinsip High Risk High Reward, tentunya para investor harus lebih hati-hati dalammenentukan langkah yang akan mereka lakukan. Salah satu cara yang dapat digunakanuntuk mengurangi risiko, yaitu melakukan prediksi tren harga saham menggunakanMachine Learning. Menggunakan data historis saham pada Bursa Efek Indonesia,yaitu open, high, low, dan close price, algoritma Machine Learning dapat melakukanprediksi tren harga saham yang selanjutnya akan digunakan sebagai strategi investasipara investor. Terdapat banyak metode Machine Learning yang dapat digunakan untukmelakukan prediksi, salah satu metode yang dapat digunakan adalah Recurrent NeuralNetwork yaitu Long Short Term Memory (LSTM). Pada metode LSTM, data historisharga saham akan dibawa ke depan melalui seluruh gerbang LSTM yaitu: ForgetGate, Input Gate, dan Output Gate. Selanjutnya akan dicari nilai loss dari model,setelah didapat nilai loss, model akan ditinjau kembali setiap tahapannya, dimulai daribelakang. Langkah pengulangan tesebut dilakukan agar mendapat variabel Weight danBias yang optimal. Kemudian, tingkat akurasi dari metode tersebut akan ditentukanmenggunakan: Root Mean Square Error (RMSE) dan Mean Absolute Error (MAE).Penelitian ini menggunakan data historis perusahaan yang termasuk pada Indeks LQ45dan dapat diambil melalui website, finance.yahoo.com. Dari penelitian ini, diketahuibahwa, masing-masing masalah memiliki model terbaiknya, untuk penyelesaian masalahtersebut.

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Stock is a part of ownership of a company, that have fulfill the requirement to be sold at

Bursa Efek Indonesia. The basic principal of stock market is High Risk High Reward,

which describe that stock market indeed have a chance to get a great profit, but it also

come with a high risk. This principal is the reason that all investor must be cautious in

deciding their move. There’s many method to do this, with one of the being, forecasting

the stock market trend with machine learning. With the historical data, that include

open, high, low, dan close price, the machine learning algorithm, could forecast the stock

market direction for the next days, which will be one of the deciding factor for investor to

choose their move. Nowadays, there’s many machine learning method that can be used to

forecast, one of them is the branch method of Recurrent Neural Network, which is, Long

Short Term Memory (LSTM). LSTM use the historical data, and bring them forward to,

Forget Gate, Input Gate, Memory State, Output Gate. Then the loss value of the model

will be calculated. After all the process the model will be re-evaluated. The re-evaluation

step is to update all the weights and biases in the model. Then the accuracy of the model

will be evaluated with Root Mean Squared Error (RMSE) and Mean Absolute Error

(MAE). This study uses the historical data of the companys that’s included in the index

LQ45, and the data is taken from the website, finance.yahoo.com. From this research, it

is known that every problem has their own preference model to solve."

"Meningkatnya jumlah investor dari tahun ke tahun di pasar modal berbagai negara mengakibatkan proses pengambilan keputusan dalam membeli saham menjadi salah satu hal yang penting. Tahapan ini merupakan tahapan yang penting karena akan memengaruhi tingkat kekayaan atau pendapatan yang akan diterima oleh seorang investor. Dalam membantu proses pemilihan saham tersebut, seorang investor dapat menggunakan analisa teknikal atau analisa fundamental dalam prosesnya. Namun seiring dengan perkembangan teknologi dan juga kemudahan dalam mengakses data harga indeks saham, maka proses prediksi selanjutnya dapat dilakukan dengan menggunakan analisis big data dalam prosesnya. Penelitian ini akan dilakukan proses prediksi indeks harga saham dengan menggunakan ARIMA dan juga algoritma Long Short-Term Memory untuk pengolahan datanya dan metode web scraping untuk metode pengumpulan data harga indeks saham. Hasil dari penelitian menunjukkan nilai MAPE 1.243% untuk indeks JKSE, 1.005% untuk indeks KLSE, 1.923% untuk indeks PSEI, 1.523% untuk indeks SET.BK dan 3.7944% untuk indeks STI.

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The increasing number of investors from year to year in the capital markets of various countries has made the decision-making process in buying shares become one of the essential things. This stage is crucial because it will affect the level of wealth or income that an investor will receive. In helping the stock selection process, an investor can use technical analysis or fundamental analysis. However, along with technological developments and the ease of accessing stock index price data, the next prediction process can be carried out using big data analysis. This research will carry out the stock price index prediction process using ARIMA and the Long Short-Term Memory algorithm for data processing and web scraping methods for stock index price data collection methods. The study results showed that the MAPE value was 1.243% for the JKSE index, 1.005% for the KLSE index, 1.923% for the PSEI index, 1.523% for the SET.BK index and 3.7944% for the STI index."

"Skripsi ini meneliti efek penerbitan SBN Domestik Tradable terhadap Pinjaman Rupiah dari sektor perbankan Indonesia dengan menggunakan pendekatan model Autoregressive Distributed Lag (ARDL) untuk menguji hubungan kointegrasi long-run dan short-run antara variabel-variabel independen dan Pinjaman Rupiah. Hasil penelitian menunjukkan bahwa ditemukan SBN Domestik Tradable memiliki hubungan yang negatif dan signifikan terhadap Pinjaman Rupiah, atau dengan kata lain SBN Domestik Tradable menghasilkan crowding out effect terhadap Pinjaman Rupiah. Namun, tidak seluruh institusi perbankan merasakan crowding out effect. Kelompok bank yang memiliki modal inti sampai dengan Rp 30 triliun saja yang merasakannya

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This study examines the effect of the issuance of Tradable Domestic Government Bonds on Rupiah Loans from the Indonesian banking sector by using the Autoregressive Distributed Lag (ARDL) approach to examine the long-run and short-run cointegration relationships between independent variables and Rupiah Loans. The results show that Tradable Domestic Government Bond has a negative and significant relationship to Rupiah Loans, or in other words the issuance of Tradable Domestic Government Bonds crowding out Rupiah Loans. However, not all banking institutions receive the crowding out effect. It just happen to groups of banks with core capital of up to Rp 30 trillion."

"Penyakit Demam Berdarah Dengue (DBD) merupakan salah satu penyakit yang penyebarannya sangat cepat. Dengan memprediksi angka insiden penyakit tersebut, diharapkan dapat membantu pemerintah dalam mengatasi penyakit ini. Seiring berkembangnya ilmu pengetahuan, salah satu metode untuk memprediksi penyakit DBD adalah machine learning. Penelitian dilakukan dengan memanfaatkan salah satu metode dalam machine learning yaitu Long Short-Term Memory (LSTM) dalam membangun model prediksi insiden DBD. Pada penelitian sebelumnya, LSTM telah digunakan dalam memprediksi angka insiden DBD di 20 kota di negara China. Pada skripsi ini model LSTM diterapkan untuk memprediksi angka insiden DBD di DKI Jakarta dengan menggunakan data cuaca dan insiden DBD. Hasil implementasi LSTM dalam memprediksi angka insiden DBD menunjukkan bahwa model terbaik diperoleh dengan menggunakan proporsi data training-testing 90%-10% dengan RMSE dan MAE berdasarkan data test. Nilai RMSE pada wilayah Jakarta Pusat, Jakarta Timur, Jakarta Barat, Jakarta Utara, dan Jakarta Selatan adalah 5,218412, 9,570137, 10,527401, 6,496117, dan 5,952310. Nilai MAE pada wilayah yang sama secara berturut-turut adalah 4,016646, 7,791134, 8,405053, 5,279802, dan 4,416999.

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Dengue Hemorrhagic Fever (DHF) is a disease that spreads very fast. By predicting the incidence of the disease, it is expected to help the government in overcoming this disease. As the development of science, one method to predict DHF is machine learning. The study was conducted by utilizing one method in machine learning that is Long Short Term-Memory (LSTM) in building a DHF incident prediction model. In previous studies, LSTM has been used in predicting the incidence of DHF in 20 cities in China. In this thesis the LSTM model is applied to predict the number of DHF incidents in DKI Jakarta by using weather data and DHF incidents. The results of LSTM implementation in predicting the number of DHF incidents showed that the best model was obtained using the proportion of training data-testing 90% -10% with RMSE and MAE based on test data. The RMSE values in the Central Jakarta, East Jakarta, West Jakarta, North Jakarta and South Jakarta areas are 5.218412, 9.570137, 10.527401, 6.496117, and 5.952310. MAE values in the same region are 4,016646, 7.791134, 8.405053, 5.279802, and 4.416999."