1604 SIMULATION PROGRAM DESCRIPTIONS. MILESTONE 11, TRACKING DATA PAPER TAPE GENERATION ROUTINE (SRADTPE)

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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ANALISIS DAYA TAMPUNG BEBAN PENCEMARAN AIR SUNGAI MENGGUNAKAN PENDEKATAN WATER QUALITY ANALISYS SIMULATION PROGRAM (WASP)7.3 (DAS SIAK BAGIAN HILIR KABUPATEN SIAK)

Jurnal Sains dan Teknologi ◽

10.31258/jst.v17.n1.p23-32 ◽

2019 ◽

Vol 17 (1) ◽

pp. 23

Author(s):

Ivnaini Andesgur ◽

Imam Suprayogi ◽

Pipi Handrianti

Keyword(s):

Water Quality ◽

Simulation Program

Daerah Aliran Sungai (DAS) Siak termasuk DAS kritis khususnya bagian hilir, dimana terjadinya penurunan debit dan kulitas air sungai yang dipicu oleh meningkatnya aktivitas disepanjang aliran sungai yang didominasi oleh kegiatan perkebunan. Penelitian ini bertujuan untuk menganalisis daya tampung beban pencemaran BOD, COD dan TSS pada DAS Siak bagian hilir menggunakan metode WASP 7.3; dan menentukan besar reduksi beban pencemarannya. DAS Siak bagian hilir dibagi menjadi 14 segment kemudian dilakukan simulasi beban pencemaran parameter BOD, COD dan TSS pada masing-masing segment. Pada simulasi ini digunakan debit minimum yaitu sebesar 151 m3/dt dilanjutkan dengan mereduksi beban pencemarannya sebesar 75%, 50% dan 25% sampai sesuai dengan baku mutu air peruntukan kelas II. Hasil Pemodelan WASP7.3 Daya Tampung Beban Pencemaran DAS Siak bagian hilir kabupaten Siak pada debit andalan minimum masing-masing untuk parameter BOD sebesar -68.877,82 kg/hari, COD sebesar -300.242,11 kg/hari dan TSS sebesar -169.065,78 kg/hari, dimana tanda minus (-) menandakan bahwa beban pencemar melebihi daya tampung sungai. Reduksi beban pencemar untuk parameter BOD sebesar 75%, COD sebesar 50%, dan TSS sebesar 25%. Parameter BOD merupakan parameter tertinggi dilakukan reduksi karena semua segment pada DAS Siak bagian hilir kabupaten Siak melebihi baku mutu peruntukkan kelas II. Kemampuan DAS Siak bagian hilir kabupaten Siak untuk menampung beban pencemar berdasarkan baku mutu peruntukkan kelas II setelah dilakukan reduksi masing-masing parameter pencemar adalah BOD 75% sebesar 12.134,95 kg/hari, COD 50% sebesar 12.958,94 kg/hari, dan TSS 25% sebesar 36.280,66 kg/hari.

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