Judul: Sumber-sumber pertumbuhan industri pengolahan makanan di Indonesia (Analisys Total Factor Productivity)
Abstrak: Isu ketahanan pangan telah memasuki paradigma baru, dengan adanya konsep sustainable food security (konsep ketahanan pangan berkelanjutan). Dalam hal ini selain peranan yang sangat mendasar dari industri pertanian, yang perlu diperhatikan juga adalah peranan penting dari industri pengolahan makanan sebagai industri yang dapat mengoptimalkan hasil-hasil pertanian. Selain itu industri pengolahan merupakan penyumbang terbesar terhadap Produk Domestik Bruto (PDB), dan industri pengolahan makanan memiliki kontribusi yang paling dominan dalam industri pengolahan. Industri pengolahan makanan juga merupakan bagian integral dari subsistem agribisnis hilir dalam sistem agribisnis. Dalam menganalisis peranan industri pengolahan makanan digunakan metode Total Factor Productivity, untuk menghitung akumulasi faktor dan tingkat efisiensi teknis dari industri tersebut. Dengan demikian dapat dilihat bagaimana ketergantungan industri pengolahan makanan terhadap akumulasi faktor dan bagaimana pengaruh teknologi (efisiensi teknis) terhadap industri pengolahan makanan. Hasil perhitungan pertumbuhan bersama dengan faktor produksi yang lain diregresi dengan menggunakan metode Pool Least Square (PLS). Pada saat krisis memang industri pengolahan merupakan industri yang paling mengalami kehancuran dan mendapatkan dampak yang sangat signifikan dari krisis. Akan tetapi pada masa pemulihan industri pengolahan, khususnya produk makanan pokok, merupakan sektor industri yang paling cepat pulih dan kembali tumbuh secara pesat. Dalam periode 1994-1996, industri pengolahan mengalami pertumbuhan nilai output sebesar 20.97 persen, dan mengalami penurunan nilai rata-rata pertumbuhan menjadi 11.29 persen ketika krisis melanda perekonomian. Namun dengan cepat pertumbuhan rata-rata kembali meningkat pada periode 2000-2002 sebesar 35.84 persen.
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Judul: Analisis faktor-faktor produksi yang memengaruhi output industri pengolahan dan pengawetan daging di Indonesia periode 1983-2008 (Pendekatan Total Factor Productivity
Abstrak: Industrial sector is the prime mover in economic development and drivers of economic growth. One of the industries is the food and beverage industry. Industrial food and beverage processing industry is one that contributes substantially in the formation of GDP. Developments in food and beverage industry is very important because one of the industries with the largest number of companies as well as the largest production value, which is processed in the food industry. Food is one of the basic needs that is very important in human life, one type of food is meat. Consumption patterns of carbohydrate-based to be protein-based and tendency to buy read- to-cook or ready-to-eat ingredients are driving consumption of processed and preserved meat increased in Indonesia. Good potential market in Indonesia encourage processing and preserving industrial of meat to always improve its production. This study aims to analyze the productivity of factors of production, Total Factor Productivity (TFP), and the factors that influence output and preserving meat processing industry in Indonesia.
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Judul: Sistem Pengklasifikasian Entitas pada Pesan Twitter Menggunakan Ekspresi Regular dan Naïve Bayes
Abstrak: The widespread use of social networks like Twitter encourages some institutions to find out the entities that are discussed and the sentiment toward the entities. This research aims to build a system that can extract entities in tweets and classify the sentiment from the tweets. Entity extraction process is done using regular expressions. Writing regular expressions can capture the diverse entities. Sentiment classification is performed using naïve Bayes classification with Multinomial and Bernoulli models. Sentiment in this research consists of three classes, namely positive, negative, and neutral. Before entering the classification, tweets are normalized by replacing the non-standard words with the standard ones. The test results show that regular expression is quite effective for the entity extraction process. As for the results of the classification, naïve Bayes method produces high accuracy (96.75% for Multinomial and 96.33% for Bernoulli). However, the accuracy obtained depends on the neutral class which amounts to 97.88% for Multinomial and 99.95% for Bernoulli, while the positive class are 85.66% and 33.13% and negative class are 33.67% and 0%.
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