Judul: Clustering Scientific Publication Documents Based on Field of Expertise Using the DBSCAN Algorithm
Abstrak: Salah satu cara untuk memvalidasi keahlian para pakar adalah dengan dokumen-dokumen publikasi ilmiah yang dimiliki. Jika kepakaran divalidasi secara manual akan membutuhkan waktu yang tidak singkat. Pengelompokan dokumen publikasi ilmiah menjadi salah satu alternatif untuk memverifikasi keparakan seorang dosen. Penelitian ini bertujuan untuk membangun suatu model yang dapat mengelompokkan dokumen berdasarkan bidang kepakaran. Penelitian ini menggunakan algoritma Latent Dirichlet Allocation (LDA) untuk ekstraksi fitur dan Density-Based Spatial Application with Noise (DBSCAN) yang ditingkatkan dengan menambahkan metode Uniform Manifold Approximation and Projection (UMAP). Hasil penelitian ini berhasil mengelompokkan dokumen dengan nilai koefisien Silhouette sebesar 0,567. Model yang dibangun dievaluasi dengan mencocokkan klaster yang dihasilkan dengan klaim yang diberikan. Hasil yang diperoleh menunjukkan bahwa sebanyak 63,9% hasil pencocokan sesuai dengan klaim kepakaran dosen, 27,6% tidak sesuai dengan klaim kepakaran dosen, dan 8,4% tidak diketahui sesuai atau tidak dengan klaim kepakaran, One way to validate experts' expertise is through their scientific publications. If expertise is validated manually, it will take a long time. Grouping scientific publication documents is one alternative to verify the expertise of a lecturer. This research aims to build a model that can classify documents based on their field of expertise. This research uses Latent Dirichlet Allocation (LDA) algorithm to extract feature and Density-Based Spatial Application with Noise (DBSCAN) which is enhanced by adding Uniform Manifold Approximation and Projection (UMAP) method. The results of this study successfully clustered documents with a silhouette coefficient value of 0,567. The built model was evaluated by matching the resulting clusters with the given claims. The results showed that 63,9% of the matching results were in accordance with the lecturer's expertise claim, 27,6% were not in accordance with the lecturer's expertise claim and 8,4% were unknown whether or not it was in accordance with the expertise claim.
Keyword: DBSCAN, pakar, klasterisasi, validasi, dokumen

Judul: Grouping Scientific Publication Documents Based on Field of Expertise Using Agglomerative Hierarchical Clustering Method
Abstrak: Dengan setiap pakar memiliki keahlian yang berbeda, dan keahlian ini dapat diidentifikasi melalui dokumen-dokumen yang paling menunjukkan kemampuannya. Sebuah model klasterisasi diperlukan untuk mengklasifikasikan karya ilmiah secara otomatis ke dalam domain keahlian yang berbeda. Dengan adanya pengelompokan ini menjadi salah satu cara untuk mengklaim atau memvalidasi dari bidang kepakaran seseorang. Penelitian ini menggunakan algoritma Latent Dirichlet Allocation (LDA) untuk mengurangi dimensi data dan menggunakan metode Agglomerative Hierarchical Clustering yang ditingkatkan dengan dan T-distributed Stochastic Neighbor Embedding (T-SNE) sebagai klasterisasi dokumen publikasi ilmiahnya. Temuan penelitian ini secara efektif mengelompokkan dokumen, yang dibuktikan dengan nilai koefisien silhouette sebesar 0.544 dengan nilai yang lebih tinggi menunjukkan klaster yang lebih baik. Model yang dibangun dievaluasi dengan membandingkan cluster yang dihasilkan dengan klaim yang dinyatakan. Hasilnya, 25% hasil pencocokan tidak sesuai dan 75% sesuai., Each expert has different expertise, and this expertise can be identified through the documents that best demonstrate his or her skills. A clustering model is needed to automatically classify scientific papers into different domains of expertise. This clustering is one way to claim or validate one's area of expertise. This research uses the Latent Dirichlet Allocation (LDA) algorithm to reduce the dimensionality of the data and uses the Agglomerative Hierarchical Clustering method enhanced with and T-distributed Stochastic Neighbor Embedding (T-SNE) as its clustering of scientific publication documents. The findings of this study effectively cluster the documents, as evidenced by the silhouette coefficient value of 0.544 with higher values indicating better clusters. The built model was evaluated by comparing the resulting clusters with the stated claims. As a result, 25% of the matching results did not match and 75% matched.
Keyword: clustering, lda, t-SNE, dokumen, klasterisasi, agglomerative hierarchical clustering, documents

Judul: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
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