50 research outputs found
Hari akhir zaman.
No full textBuku ini mengupas berbagai hal seputar hari kiamat, tandanya, kronologi, serta apa yang akan terjadi setelah hari akhir itu.iv, 60 hlm.: ilus.; 25 cm
PENGARUH TIDAK MAKSIMALNYA KERJA PELUMASAN DIESEL GENERATOR DI KM. PEKAN FAJAR
No full textBekti Supriyanto, 2018, NIT : 51145404. T, “Pengaruh Tidak Maksimalnya Kinerja Pelumasan Diesel Generator di KM. PEKAN FAJAR”, Skripsi Program Studi Teknika, Program Diploma IV, Politeknik Ilmu Pelayaran Semarang, Pembimbing I : H. Suwondo, M.M, M.Mar.E, Pembimbing II : Febria Nurjaman, M.T.
Based on the observation of lubrication function is very important above the ship, then the lubrication system must be given special attention in carrying out routine maintenance or lab testing in addition to other machinery. So that this lubrication system can work in accordance with its function on the ship so as not to disrupt the smooth operation of the ship. So the author poured in this thesis with the title “Pengaruh Tidak Maksimalnya Kerja Pelumasan Diesel Geerator di KM. Pekan Fajar” Based on the observation of lubrication function is very important above the ship, then the lubrication system must be given special attention in carrying out routine maintenance or lab testing in addition to other machinery. So that this lubrication system can work in accordance with its function on the ship so as not to disrupt the smooth operation of the ship. So the author poured in this thesis with the title. Furthermore the authors in data collection techniques is the method of observation, interview method, and literature review seta author gives the location /place of research. At the end of this thesis, the authors present the main substance of the conclusion, ie before there is better damage prevention by way of maintenance on the lubrication system. The treatment must be in accordance with the working hours (running hours) on the instruction manual book.
Kata Kunci : Lubricating, LO Cooler, LO Filter, LO Pump, Viskositas, fishbone, FT
Analisis Topik dan Author pada Jurnal Bahasa Inggris dengan Menggunakan Metode Author-Topik Model dan Gaussian Latent Dirichlet Allocation(GLDA)
No full textPenggunaan Author-Topic Model telah banyak dilakukan pada bidang machine learning khususnya natural language processing. Metode ini pertama kali digunakan untuk mengetahui bidang penelitian dari seorang penulis dengan melihat topik dari dokumen yang telah ditulisnya. Meskipun model ini banyak digunakan, penerapannya masih memiliki banyak kekurangan. Hal yang paling menonjol adalah ketidakmampuan Latent Dirichlet Allocation (LDA) sebagai dasar dari Author-Topic Model untuk menangkap kata yang belum pernah terlihat dalam data training pada saat melakukan percobaan dari suatu dokumen uji dalam menentukan distribusi kata suatu topik atau biasa disebut out of vocabulary(OOV). OOV membuat topik yang didapat dari suatu dokumen uji tidak sesuai dengan yang diinginkan. Hal tersebut dapat terjadi karena kata-kata penting yang terdapat pada dokumen uji tidak dapat ditangkap oleh LDA. Maka dari itu, pada tugas akhir ini menggunakan Gaussian Latent Dirichlet Allocation (GLDA) pada author-topic model untuk menyelesaikan permasalahan yang ada pada LDA dengan mengubah jenis kata menjadi vektor kata. Metode GLDA ini digunakan untuk pembanding dari penelitian sebelumnya. Pembuatan author-topic model terbaik dari kedua metode dilakukan dengan menghitung nilai perplexity dan topic coherence untuk setiap metode. Hasil yang didapat menggambarkan bahwa penggunaan GLDA dapat mendapatkan hasil modelling yang lebih stabil disetiap iterasi. Namun, hasil yang didapatkan dari kemampuan kedua model dalam memberikan prediksi terhadap author dari dokumen baru memiliki persamaan. Pada kedua model terlihat bahwa persentase prediksinya tidak berbeda jauh. Hal tersebut disebabkan karena penggunaan data dengan jenis kata yang terlali sedikit dan pembersihan data yang kurang baik. Sehingga membuat distribusi kata pada setiap topiknya terlihat tidak sesuai dengan topik dari dokumen tersebut.
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The use of Author-Topic Model has been widely used in the field of machine learning, especially natural language processing. This method was first used to find out the research field of an author by looking at the topic of the document he had written. Although this model is widely used, its application still has many disadvantages. The most prominent thing is the inability of Latent Dirichlet Allocation (LDA) as the basis of the Author-Topic Model to capture words that have never been seen in training data when conducting experiments from a test document in determining word distribution of a topic or commonly called out of vocabulary (OOV). OOV makes the topic obtained from a test document not as desired. This can happen because the important words contained in the test document cannot be captured by the LDA. Therefore, in this final assignment use the Dirichlet Allocation Latent Gaussian (GLDA) on the author-topic model to solve the problems that exist on the LDA by changing the type of word into a word vector. This GLDA method is used for comparison from previous studies. Making the best author-topic model of the two methods is done by calculating the perplexity value and topic coherence for each method. The results obtained illustrate that the use of GLDA can get more stable modeling results in each iteration. However, the results obtained from the ability of the two models to provide predictions to the author of the new document have similarities. In both models it can be seen that the percentage of predictions is not very different. This is due to too little use of data types and poor data cleaning. so that the distribution of words on each topic does not appear to fit the topic of the document
Identification of hydrothermal alterations using Dar-Zarouk parameters and concept of anisotropy for 2D resistivity data
No full textAttenuation relation for strong motion in Eastern Java based on appropriate database and method
No full textAnalysis of gravity data beneath Endut geothermal prospect using horizontal gradient and Euler deconvolution
No full textPractical aspects in surface biopotential electrode placement for smart clothing: A simulation study
No full textIn addition to physiological aspects, placement of surface biopotential electrodes for smart clothing should consider practical aspects due to their dynamic application environment. This study is aimed at finding the best places to put the electrode on areas where the measurement is practically reliable. Calculation was performed by using three practical aspects: 1) skin-shirt gap; 2) shirt movement, and 4) regional sweat rate. We employed 3DS Max software to simulate shirt behavior. The simulation result showed that generally practical satisfaction degrees are higher in the posterior. The quantitative approach may help smart clothing designers to choose the locations to place electrodes
Effectiveness of creative and productive instructional method towards students’ learning achievement in steel structure course
No full textEffects of the combination between bio-surfactant product types and washing times on the removal of crude oil in nonwoven fabric
No full textThe chemical bonds effect of Amaranthus hybridus L. and Dracaena Angustifolia on TiO2 as photo-sensitizer for dye-sensitized solar Cells (DSSC)
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