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"The book focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. The present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. "

"[ABSTRAKTelah diakukan penelitian guna mendelineasi zona prospek sistem panasbumi Daerah „P‟ menggunakan pemodelan multi dimensi data magnetotelurik terintegrasi data geologi dan geokimia. Daerah panasbumi „P‟ secara fisiografi termasuk pada Busur Banda Dalam tak bergunungapi disusun oleh komplek batuan malihan sekis berumur Perm-Trias. Gejala adanya sistem panasbumi pada daerah penelitian ditandai dengan kemunculan manifestasi permukaan berupa enam mata air panas bersuhu (37-67oC), pH (6-7) dan bertipe klorida-bikarbonat. Pembentukan sistem panasbumi diduga berkaitan dengan aktivitas tektonik kuat akibat tumbukan lempeng Pulau Seram dengan Lempeng Benua Australia (Plate Collision) yang memicu pembentukan batuan intrusi di kedalaman sebagai sumber panas. Guna mengetahui informasi subsurface daerah penelitian, dilakukan survei magnetotelurik. Selanjutnya hasil dari data MT akan diintegrasikan data geologi dan geokimia. Pengolahan data MT dimulai dari time-series data hingga mendapatkan kurva resistivitas-frekuensi dan fase, lalu dilakukan filtering noise, rotasi arah strike dan koreksi static shift untuk mendapatkan kualitas kurva MT baik. Selanjutnya dilakukan pemodelan inversi 1D, 2D dan 3D. Temperatur reservoir diduga sekitar 160-180oC termasuk temperatur sedang. Hasil penelitian ini memperlihatkan lapisan konduktif (<15 Ωm) dengan ketebalan ± 500-1000 m diindikasikan sebagai Clay Cap dari sistem panasbumi. Zona resistivitas tinggi (>300 Ωm) dan berbentuk updome, berada di bawah area kemunculan manifestasi (MAP1, MAP2, MAP3, MAP4, dan MAP5) mengindikasikan heat source berada di utara kemudian menerus ke arah tenggara membentuk updome. Model konseptual terpadu sistem panasbumi dibentuk dari integrasi data geologi, geokimia, dan geofisika. Sistem panasbumi daerah penelitian merupakan hidrotermal heat sweep plate collision dengan temperatur sedang, luas area prospek dan rekomendasi titik pemboran diperkirakan ± 3 km2 di sekitar zona Upflow, potensi sumber daya hipotetik dengan metode volume lump parameter menggunakan binary cycle ± 34 MWe.

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ABSTRACTA study for delineating geothermal system of prospect area “P” has been done by using multi-dimensional modeling of magnetotelluric data. Physiographycally, geothermal prospect of “P” area is located at non-volcanic Banda inner arc hosted by Malihan Sekis rock complex with Perm-Trias age. The existance of geothermal system in this area is indicated by the presence of thermal manifestations in form of 6 chloride-bicarbonate hot springs with temperature in the the range of 37 – 67oC, and pH of 6-7. The development of geothermal system is most probably associated with strong tectonic activity caused by the collision between Seram island plate and Australian plate that ignite the occurence of intrussive body as heat source. In order to know the subsurface information of prospect area, magnetotelluric (MT) survey has been done. The processing of MT data was started from time-series data, continued by noise filtering, rotation of strike orientation and static shift correction to obtain better MT curve. The data were then being inversed by means of 1-Dimensional, 2-Dimensional and 3-Dimensional inversion methods. Reservoir temperature is estimated to be around 160-180 oC and classified as moderate temperature. The result of MT data inversions shows the presence of conductive layer (<15 Ωm) with 500 – 1000 m thickness that is interpreted as clay cap og geothermal system. High resistivity zone (>300 Ωm) with updome shape appears underneath the manifestations occurence (MAP1, MAP2, MAP4, and MAP5), indicating that the heat source is located in northern part and elongate to souteast direction. The conceptual model of geothermal system was built based on integrated interpretation of geological, geochemical and geophysical data. The prospect area and recommendation of drilling location is estimated to be ± 5 km2 around upflow zone. Potential hypothetical resource with volume lump parameters method using binary cycle ± 34 MWe., A study for delineating geothermal system of prospect area “P” has been done by using multi-dimensional modeling of magnetotelluric data. Physiographycally, geothermal prospect of “P” area is located at non-volcanic Banda inner arc hosted by Malihan Sekis rock complex with Perm-Trias age. The existance of geothermal system in this area is indicated by the presence of thermal manifestations in form of 6 chloride-bicarbonate hot springs with temperature in the the range of 37 – 67oC, and pH of 6-7. The development of geothermal system is most probably associated with strong tectonic activity caused by the collision between Seram island plate and Australian plate that ignite the occurence of intrussive body as heat source. In order to know the subsurface information of prospect area, magnetotelluric (MT) survey has been done. The processing of MT data was started from time-series data, continued by noise filtering, rotation of strike orientation and static shift correction to obtain better MT curve. The data were then being inversed by means of 1-Dimensional, 2-Dimensional and 3-Dimensional inversion methods. Reservoir temperature is estimated to be around 160-180 oC and classified as moderate temperature. The result of MT data inversions shows the presence of conductive layer (<15 Ωm) with 500 – 1000 m thickness that is interpreted as clay cap og geothermal system. High resistivity zone (>300 Ωm) with updome shape appears underneath the manifestations occurence (MAP1, MAP2, MAP4, and MAP5), indicating that the heat source is located in northern part and elongate to souteast direction. The conceptual model of geothermal system was built based on integrated interpretation of geological, geochemical and geophysical data. The prospect area and recommendation of drilling location is estimated to be ± 5 km2 around upflow zone. Potential hypothetical resource with volume lump parameters method using binary cycle ± 34 MWe.]"

"Indonesia merupakan negara tropis yang memiliki dua musim yaitu musim kemarau dan musim hujan. Akan tetapi, kondisi cuaca setiap harinya tidak dapat diprediksi karena pengaruh perubahan iklim yang terjadi. Hujan bisa terjadi kapan saja di musim kemarau dan begitu juga sebaliknya. Hal ini menyebabkan perbedaan suhu di setiap harinya. Penelitian ini bertujuan untuk menganalisa pengaruh perubahan suhu dan cuaca terhadap penggunaan listrik di gedung kampus pendidikan khususnya penggunaan pendingin ruangan. Untuk itu dilakukan pemilihan fitur data cuaca menggunakan Pearson Correlation Coefficient (PCC) dan user behaviour modelling dengan menggunakan dua metode yaitu Long Short Term Memory (LSTM) dan penambahan lapisan convolutional neural network (CNN) pada metode LSTM. Setelah dilakukan modelling, maka akan dilakukan prediksi dan akurasinya akan untuk menentukan metode mana yang lebih cocok untuk kasus ini. Metode gabungan CNN-LSTM mendapatkan skor tertinggi dengan skor R2 sebesar 0,83 dan MAE sebesar 136,55. Penggunaan data cuaca terbukti memiliki pengaruh terhadap akurasi model. Meskipun cuaca di Indonesia tidak menentu dan penggunaan pendingin ruangan yang tidak teratur, faktor cuaca seperti rata-rata suhu masih memiliki pengaruh terhadap penggunaan pendingin ruangan di gedung institusi pendidikan.

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Indonesia is a tropical country that has two seasons, namely the dry season and the rainy season. However, daily weather conditions cannot be predicted due to the influence of climate change. Rain can occur at any time in the dry season and vice versa. This causes temperature differences on a daily basis. This study aims to analyze the influence of temperature and weather changes on electricity use in educational campus buildings, especially the use of air conditioning. For this reason, weather data will be selected using Pearson Correlation Coefficient (PCC) and user behavior modeling using two methods, namely Long Short Term Memory (LSTM) and the addition of a convolutional neural network (CNN) layer to an LSTM method. After modeling, predictions will be made and the accuracy will be measured to determine which method is more suitable for this case. The combined CNN-LSTM method got the highest score with an R2 score of 0.83 and MAE of 136.55. The use of weather data has proven to have an influence on model accuracy. Although the weather in Indonesia is erratic and the use of air conditioning is unmonitored, weather factors such as average temperature and rain still have an influence on the use of air conditioning in educational institution buildings."

"The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories. The book includes representative applications from diverse fields, ranging from academic linguistics (e.g., typology and corpus linguistics) over applied linguistics (e.g., lexicography and translation studies) to technical applications (in computational linguistics, natural language processing and information technology).This volume accompanies the Workshop on Linked Data in Linguistics 2012 (LDL-2012) in Frankfurt/M., Germany, organized by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). It assembles contributions of the workshop participants and, beyond this, it summarizes initial steps in the formation of a Linked Open Data cloud of linguistic resources, the Linguistic Linked Open Data cloud (LLOD)."