Judul: Text Summarization: BART, Random Forest, and Hybrid BART-RF Algorithm comparison
Abstrak: Data dan informasi berkembang secara kuantitatif dan kualitatif. Terdapat banyak teks pada internet dan pertumbuhan data menjadi lebih banyak dari yang dibutuhkan. Jumlah dokumen atau teks yang ada pada seluruh sumber sangat besar, maka pekerjaan merangkum menjadi sangat kompleks. Natural Language Processing (NLP) adalah subbidang pada computer science yang membahas pemrosesan dan analisis bahasa manusia. Pembahasan yang umum pada NLP yaitu pemrosesan percakapan, analisis bentuk kalimat, analisis sintaks, diskursus dan pembahasan terkait aplikasi teks seperti perangkuman (summarization), text generation, grammatical correction. Automatic Text Summarization (ATS) adalah salah satu tugas yang menantang pada NLP. ATS sangat sering digunakan pada text mining dan aplikasi analitis seperti information retrieval, information extraction, question answering dan sebagainya. ATS terdiri dari dua cara pendekatan umum yaitu abstraktif, ekstraktif dan hybrid. Pendekatan hybrid melakukan perangkuman dengan kombinasi dari abstraktif dan ekstraktif. Tujuan utama penelitian ini adalah menguji hasil performa algoritma BART dan Random Forest secara independen dan hasil performa secara kombinasinya dalam automatic text summarization. Digunakan algoritma Random Forest pada pendekatan ekstraktif, BART untuk pendekatan abstraktif dan kombinasi BART dan Random Forest untuk pendekatan hybrid. Penelitian menunjukkan bahwa secara individu, skor BART dan RF ROUGE cukup berbeda. Secara berturut-turut skor ROUGE RF pada R1, R2 dan RL adalah 1) 51.45 , 2) 45.52 dan 3) 54.58., skor ROUGE BART adalah 1) 32.78, 2) 16.17 dan 3) 32.19. Secara berturut-turut rata-rata pengukuran F ROUGE RF, BART dan RFxBART adalah 45.73, 21.38 dan 31.31. RF memiliki skor rata-rata tertinggi. ATS Hybrid RFxBART terbukti berkinerja lebih baik daripada BART default, tetapi lebih buruk daripada RF dalam hal skor ROUGE. Rata-rata ROUGE F RFxBART adalah 31,31. RFxBART memiliki skor sedang. Skor ini lebih baik daripada skor ROUGE default BART. RFxBART dapat menjadi alternatif pendekatan hybrid yang efektif., Data and information grows quantitatively and qualitatively. There is a lot of text on the internet and the growth of data is becoming more than needed. The number of documents or texts contained in all sources is very large, so the work of summarizing becomes very complex. Natural Language Processing (NLP) is a subfield of computer science that discusses the processing and analysis of human language. Common discussions in NLP includes conversation processing, sentence form analysis, syntax analysis, discourse and discussions related to text applications such as summarization, text generation, grammatical correction. Automatic Text Summarization (ATS) is one of the challenging tasks in NLP. ATS is often used in text mining and analytical applications such as information retrieval, information extraction, question answering and so on. ATS consists of two general approaches, which are abstractive, extractive and hybrid. The hybrid approach combines abstractive and extractive to do summarization. The main objective of this research is to test the performance results of the BART and Random Forest algorithms independently and their combined performance results in automatic text summarization. The Random Forest algorithm is used for the extractive approach, BART for the abstractive approach and a combination of BART and Random Forest for the hybrid approach. Research shows that individually, BART and RF ROUGE scores are quite different. Respectively the ROUGE RF scores on R1, R2 and RL are
Keyword: BART, NLP, perangkuman otomatis, random forest, rangkuman teks

Judul: Sistem Peringkas Teks Otomatis Menggunakan Algoritme Page Rank
Abstrak: With the rapid growth of World Wide Web, a huge amount of information is available and accessible online. People do not have the time to read everything, and yet they have to make critical decisions based on whatever information is available. Automatic text summarization is a technique to assist people in digesting the vast amount of information online. Recently, a number of graph-based approaches, such as PageRank, LexRank, and TextRank, have been suggested for text summarization. Automatic text sllnnnarization is a computerized process of distilling the most important information of a source (or sources) for making a brief version of text(s). This research implements graph-based sunamarization algorithm and similarity using graph-based ranking concept for ranking sentences. The process produces the output in the form of extractive summary that consists of high ranking sentences. The graph-based method applied is PageRank, combined with cosine and content overlap similarity. Evaluation of the summaries uses three human judges and their judgement is compared using kappa measure. The result of our experiment shows that the accuracy of PageRank and cosine similarity is better than PageRank with content overlap similarity. Similarity to title produced the best result than similarity with no title.
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Judul: Codiaeum Malesia
Abstrak: The Malesian genus Codiaeum has been revised based on the morphological character using available herbarium collections in Herbarium Bogor iense and loan specimens from Rijksherbarium Leiden. The study is based on the observation of 350 specimens. Eleven species have been recognized, namely Codiaeum variegatum, C. luzonicum, C. palawanense, C. trichocalyx, C. megalanthum, C. cilliatum, C. affine, C. tenerifolium dan C. hirsutum, C. macgregorii, C. ludovicianum. number of species is found in the Philippines The highest (8 species), Two species (C. tenerifolium and C. ludovicianum) endemic in Lousiade Archipelago of the New Guinea and C. affine endemic in Banguey Island. C. variegatum is widespread by nature or in cultivation almost in all of Malesian region. C. cillia tum is a species in the earliest level among the other species according to 20 selected characters.
Keyword: Codiaeum, Malesia