Judul: Higiene Pekerja Kantin di Dalam Kampus Institut Pertanian Bogor Dramaga, Bogor
Abstrak: The aim of this study was to know the factors related with hygiene practices among food handlers at canteen in Bogor Agriculture University. Survey of KAP (knowledge, attitude, and practices) were used to know the relationship of knowledge and attitudes with their practices as its factors. Thirty nine food handlers were taken sampling randomly from sixty seven canteen in Bogor Agriculture University and become respondents for questionnaire about hygiene in handling food which is divided in to three aspect: personal hygiene, hygiene of production, and hygiene of facilities. The data were analyzed using the SPSS 16.0. A majority respondents have good knowledge (94.9%), attitude (100%), and practices (97.4%). Statistic analysis showed significant difference (p<0.05) between the relationship of knowledge, attitude, and main occupation with their practices
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Judul: Kondisi Sanitasi Tangan Food Handler pada Warung Makanan di Sekitar Kampus Institut Pertanian Bogor Dramaga Bogor
Abstrak: The aim of this study was to assess of hand sanitation of food handlers and identify risk factors on hygiene practices. Total of 42 food kiosks around the Bogor Agricultural University campus in Dramaga Bogor were selected randomly as unit samples and one of food handlers in each kiosks was selected as a respondent. The research was conducted with using questionnaires and counting of microorganism number with RODAC method. The result showed that most of food handlers were in the age range of adults and old (73.8%). More than half of respondents had attended school until senior high school (57.1%), new experience of trading (69.1%), no attended trainings (71.4%), and no subjected to inspections (71.4%). Generally the food handlers had good knowledge, moderate attitude, and good practices. There was a significant correlation (r=0.752; p<0.05) between knowledge and practices. Only few of food handlers (16.6%) were categorized in acceptable criteria of total plate counts of hand surfaces. The identified risk factors on hygiene level were young age (OR=9.80), adult age (OR=8.40), and low level education (OR=12.14). This condition could be a potential risk for consumer health.
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Judul: Implementation of Long Short-Term Memory for Gold Prices Forecasting
Abstrak: Emas merupakan salah satu bentuk investasi dan disebut sebagai aset safe haven karena kestabilannya dalam kondisi pasar yang tidak stabil. Peramalan harga emas menjadi penting bagi investor sebagai alat bantu pengambilan keputusan. Penelitian ini bertujuan untuk melihat pengaruh validasi silang dalam memperoleh hyperparameter LSTM (optimizer, learning rate, dan epoch) terbaik yang akan digunakan dalam peramalan. LSTM sebagai bagian metode deep learning dapat digunakan dalam peramalan selain menggunakan metode klasik. Algoritma ini merupakan pengembangan dari recurrent neural network yang banyak digunakan dalam peramalan deret waktu. LSTM dianggap unggul dan mampu meminimalkan kesalahan, serta mampu melakukan peramalan untuk periode jangka panjang dibanding metode lain. Walk forward validation dengan skenario pergeseran jendela (tahunan, semester, dan kuartal) sebagai bentuk validasi silang digunakan untuk melihat keakuratan metode. Data yang digunakan berasal dari World Gold Council dengan periode data harian untuk tanggal 1 Januari 2003 sampai dengan 20 Januari 2023. Optimizer yang digunakan adalah adam dan RMSProp dengan nilai learning rate 0,01; 0,001; dan 0,0001, serta nilai epoch 100, 500, dan 1000. Model terbaik adalah model dengan nilai MAPE terkecil sebesar 12,41% pada proses validasi menggunakan optimizer RMSProp, nilai learning rate 0,01, dan epoch 100. Hasil peramalan menunjukkan kecenderungan harga emas untuk meningkat dalam delapan tahun mendatang., Gold is a form of investment and is referred to as a safe haven asset because of its stability in unstable market conditions. Gold price forecasting is important for investors as decisions making tool. This study aims to study the effect of cross-validation in obtaining the best LSTM hyperparameters (optimizer, learning rate, and epoch) to be used in forecasting. LSTM as part of deep learning methods can be used in forecasting beside the classical method. Algorithm in the LSTM is developed based on a recurrent neural network that is widely used in time series forecasting. It was found that the LSTM is superior compared to other methods for its ability to minimize errors and to forecast for long-term periods. Walk forward validation with window shift scenarios (annual, semester, and quarter) as a form of cross validation is used to see the accuracy of the method. The data used came from the World Gold Council with daily data periods for January 1, 2003, to January 20, 2023. The optimizer used is adam and RMSProp each with learning rate values of 0.01; 0.001; and 0.0001, and with epoch values of 100, 500, and 1000. The best model was the model with the smallest MAPE value of 12.41% in the validation process using an optimizer RMSProp, a learning rate of 0.01, and epoch value of 100. Forecasting results show a tendency for gold prices to increase in the next eight years.
Keyword: deep learning, forecasting, gold, hyperparameter, LSTM