Judul: Water Footprint Analysis on Subsurface Irrigation Systems in Greenhouse for Pakcoy (Brassica rapa L.) Cultivation
Abstrak: Air menjadi salah satu unsur pokok yang dibutuhkan tanaman pakcoy untuk tumbuh dan berkembang. Banyaknya kebutuhan air dalam menghasilkan produk dapat dikaji dengan pendekatan water footprint. Penelitian ini bertujuan menganalisis nilai water footprint pada budidaya Pakcoy (Brassica rapa L.) dengan berbagai skenario tinggi muka air. Penelitian dilakukan di dalam greenhouse Kinjiro Farm, Bogor, Jawa Barat pada bulan Maret - Mei 2023. Terdapat tiga perlakuan pada pengaturan tinggi muka air yaitu Tinggi Muka Air 1 (TMA 1) dengan ketinggian air 13 cm, Tinggi Muka Air 2 (TMA 2) dengan ketinggian air 10 cm, dan Tinggi Muka Air 3 (TMA 3) dengan ketinggian air 7 cm. Nilai water footprint TMA 1, TMA 2, dan TMA 3 berturut-turut sebesar 0,84 m3/kg, 0,63 m3/kg, dan 0,68 m3/kg. TMA 2 memiliki nilai water footprint terendah karena menghasilkan produksi tertinggi. Rata-rata pertumbuhan tinggi tanaman pakcoy, jumlah daun, dan berat hasil panen pakcoy tertinggi terjadi pada kondisi TMA 2, dengan rata-rata tinggi tanaman sebesar 19 cm dan rata-rata jumlah daun sebanyak 15 helai daun pada minggu terakhir serta berat total panen sebesar 250 gram. Perlakuan pada sistem TMA 2 merupakan perlakuan yang paling optimal dibandingkan dua perlakuan yang lain., Water is one of the main elements needed by the pakcoy plant to grow and develop. The amount of water needed to produce products can be assessed using the water footprint approach. This study aims to analyze the value of the water footprint in pakcoy cultivation with various scenarios of high water levels. The research was conducted in the greenhouse of Kinjiro Farm, Gunung Batu from March to May 2023. There were three treatments for setting the water level, namely Water Level 1 (TMA 1) with a water level of 13 cm, Water Level 2 (TMA 2) with a height water 10 cm, and Water Level 3 (TMA 3) with a water level of 7 cm. The water footprint values of TMA 1, TMA 2, and TMA 3 are respectively 0.84 m3/kg, 0.63 m3/kg and 0.68 m3/kg. The highest average growth of pakcoy plant height, number of leaves, and weight of the highest pakcoy harvest occurred in TMA 2, with an average plant height of 19 cm and an average number of leaves of 15 leaves in the last week and a total harvest weight of 250 grams. The treatment on the TMA 2 system is the most optimal treatment compared to the other two treatments.
Keyword: Greenhouse, Irigasi Bawah Permukaan, Pakcoy, Tinggi muka air, Water footprint

Judul: Water Footprint Analysis of Paddy Cultivation Using Subsurface Irrigation in a Greenhouse
Abstrak: Air menjadi salah satu unsur pokok yang dibutuhkan tanaman padi untuk tumbuh dan berkembang. Banyaknya kebutuhan air dalam menghasilkan produk dapat dikaji dengan pendekatan water footprint. Penelitian ini bertujuan menganalisis nilai water footprint budidaya padi dengan berbagai perlakuan pada kondisi lahan. Penelitian dilakukan di dalam greenhouse Kinjiro Farm, Gunung Batu pada bulan Maret hingga Juli 2022. Terdapat tiga perlakuan pada kondisi lahan berdasarkan ketinggian muka airnya yaitu sistem irigasi tergenang (IT) dengan permukaan air berada di atas permukaan tanah, irigasi basah (IB) dengan permukaan air sejajar dengan permukaan tanah, dan irigasi kering (IK) dengan permukaan air berada di bawah permukaan tanah. Nilai water footprint IT, IB, dan IK berturut-turut sebesar 1,92 m3/kg, 1,66 m3/kg, dan 6,14 m3/kg. Nilai produktivitas air tertinggi dihasilkan oleh IB sebesar 0,60 kg/m3. Rata-rata pertumbuhan tinggi padi, jumlah daun, dan jumlah anakan padi tertinggi terjadi pada perlakuan IB. Perlakuan IB merupakan perlakuan jenis kondisi lahan yang paling optimum dalam budidaya padi di dalam greenhouse., Water is one of the basic elements for rice plants to grow and develop. Total of water requirements in producing products can be assessed using the water footprint approach. This study aimed to analyze the value of the water footprint in paddy cultivation with various scenarios. The study was conducted in the Kinjiro Farm greenhouse, Gunung Batu from March to July 2022. There were three treatments on land conditions based on the water level, namely the flooded irrigation system (IT) with water surface higher than the soil surface, wet irrigation (IB) with water surface as high as the soil surface, and dry irrigation (IK) with water surface lower than the soil surface the soil surface. The water footprint values of IT, IB, and IK were 1,92 m3/kg, 1,66 m3/kg, dan 6,14 m3/kg, respectively. The highest water productivity value was produced by IB at 0,60 kg/m3. The highest average growth of rice height, number of leaves, and number of tillers occurred in the IB field scenario. The IB scenario is the most optimum type of land condition scenario for rice cultivation in a greenhouse.
Keyword: dry irrigation, flooded irrigation, greenhouse, water footprint, wet irrigation

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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