Dwi Hendratmo Widyantoro
Institut Teknologi Bandung

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Jurnal Informatika Vol 8, No 1 (2007): MAY 2007
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (183.665 KB) | DOI: 10.9744/informatika.8.1.pp. 54-62


Learning unlabeled data in a drifting environment still receives little attention. This paper presents a concept tracker algorithm for learning concept drift that exploits unlabeled data. In the absence of complete labeled data, instance classes are identified using a concept hierarchy that is incrementally constructed from data stream (mostly unlabeled data) in unsupervised mode. The persistence assumption in temporal reasoning is then applied to infer target concepts. Empirical evaluation that has been conducted on information-filtering domains demonstrates the effectiveness of this approach.
Analisis Pembangunan Korpus Berpasangan Untuk Pembangkitan Parafrasa Pada Makalah Ilmiah Ilyas, Ridwan; Widyantoro, Dwi Hendratmo; Khodra, Masayu Leylia
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 2 No 1 (2018): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (154.731 KB) | DOI: 10.26874/jumanji.v2i1.44


Pembangunan mesin yang dapat membangkitkan kalimat baru dengan tingkat semantik yang tinggi namun secara penulisan berbeda (parafrasa) membutuhkan sumberdaya bahasa berupa korpus parallel. Proses pembangunan korpus memerlukan analisis awal sesuai dengan domain dari mesin yang akan dibuat. Pada penelitian ini dilakukan analis dalam pembangunan korpus berpasangan pada makalah ilmiah. Kalimat-kalimat pada makalah ilmiah memiliki karakteristik yang berbeda dengan domain lain seperti berita atau media sosial. Dari hasil proses ekstraksi awal didapatkan 590.402 kalimat isi dan 23.584 kalimat abstrak. Hasil dari penelitian ini dapat menjadi kandidat korpus yang dilakukan dengan proses terkomputerisasi.
Winner-Takes-All based Multi-Strategy Learning for Information Extraction Widyantoro, Dwi Hendratmo; Muludi, Kurnia; Kuspriyanto, Kuspriyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7935-7945


This paper proposes a winner-takes-all based multi-strategy learning for information extraction. Unlike the majority of multi-strategy approaches that commonly combine the prediction of all base learnings involved, our approach takes a different strategy by employing only the best, single predictor for a specific information task. The best predictor (among other predictors) is identified during training phase using k-fold cross validation, which is then retrained on the full training set. Empirical evaluation on two benchmarks data sets demonstrates the effectiveness of our strategy. Out of 26 information extraction cases, our strategy outperforms other information extraction algorithms and strategies in 16 cases. The winner-takes-all strategy in general eliminates the difficult situation in multi-strategy learning when the majority of base learners cannot make correct prediction, resulting in incorrect prediction on its output. In such a case, the best predictor with correct prediction  in our strategy will take over for the overal prediction.