Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Journal of ICT Research and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science
Dwi Hendratmo Widyantoro
School Of Electrical Engineering And Informatics, Institut Teknologi Bandung Jalan Ganesha 10, Bandung 40132,
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
EXPLOITING UNLABELED DATA IN CONCEPT DRIFT LEARNING Widyantoro, Dwi Hendratmo
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

Abstract

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.
Shared-hidden-layer Deep Neural Network for Under-resourced Language the Content Devin Hoesen; Dessi Puji Lestari; Dwi Hendratmo Widyantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7984

Abstract

Training speech recognizer with under-resourced language data still proves difficult. Indonesian language is considered under-resourced because the lack of a standard speech corpus, text corpus, and dictionary. In this research, the efficacy of augmenting limited Indonesian speech training data with highly-resourced-language training data, such as English, to train Indonesian speech recognizer was analyzed. The training was performed in form of shared-hidden-layer deep-neural-network (SHL-DNN) training. An SHL-DNN has language-independent hidden layers and can be pre-trained and trained using multilingual training data without any difference with a monolingual deep neural network. The SHL-DNN using Indonesian and English speech training data proved effective for decreasing word error rate (WER) in decoding Indonesian dictated-speech by achieving 3.82% absolute decrease compared to a monolingual Indonesian hidden Markov model using Gaussian mixture model emission (GMM-HMM). The case was confirmed when the SHL-DNN was also employed to decode Indonesian spontaneous-speech by achieving 4.19% absolute WER decrease.
The Strategies for Quorum Satisfaction in Host-to-Host Meeting Scheduling Negotiation Rani Megasari; Kuspriyanto Kuspriyanto; Emir Mauludi Husni; Dwi Hendratmo Widyantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4521

Abstract

This paper proposes two strategies for handling conflict schedule of two meetings which invite the same member of personnel at the same time through host-to-host negotiation scheme. The strategy is to let the member attend the other meeting under the condition that the group decision regarding the schedule is not changed and meeting quorum is fulfilled, namely release strategy. Other strategy is to substitute the absent personnel in order to keep the number of attendees above the quorum, namely substitute strategy. This paper adapts a mechanism design approach, namely Clarke Tax Mechanism, to satisfy incentive compatibility and individual rationality principal in meeting scheduling. By using a release strategy and substitute strategy, colliding meetings can still be held according to the schedule without the need for rescheduling. This paper shows the simulation result of using the strategies within some scenarios. It demonstrates that the number of meeting failures can be reduced with negotiation.        
TRANSFORMING RHETORICAL DOCUMENT PROFILE INTO TAILORED SUMMARY OF SCIENTIFIC PAPER Masayu Leylia Khodra; Mohammad Dimas; Dwi Hendratmo Widyantoro; E. Aminudin Aziz; Bambang Riyanto Trilaksono
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Since abstract of scientific paper is author biased, readers’ required information may not be included in the abstract. Tailored summary may help them to get a summary based on their information needs. This research is the first one that implements tailored summary system for scientific paper. Tailored summary applies information extraction that transforms a scientific paper into Rhetorical Document Profile, a structured representation of paper content based on rhetorical scheme of fifteen slots. This research adapted building plan that used rhetorical scheme of seven slots. We also implement tailored summary system. After generating initial summary, surface repair is conducted to improve summary readability. Each sentence in initial summary is combined with template phrase based on syntax-tree combination method. There are five groups of template phrases provided in surface repair. We construct evaluation standards by asking five human raters. The best method for sentence selection subsystem that uses Maximal Marginal Importance-Multi Sentence is employing TF.IDF weighting system with precision/recall of 0.61. The surface repair subsystem has acceptance of 0.91.
Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning Fariska Zakhralativa Ruskanda; Dwi Hendratmo Widyantoro; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.4

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.
Optimalisasi Rekomendasi Rute Pada Perencanaan Perjalanan Wisata: Studi Pustaka: Optimization Route Recommendation-Based Tourist Trip Design Problem: A Literature Study Ramdani, Ahmad Luky; Widyantoro, Dwi Hendratmo; Munir, Rinaldi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1213

Abstract

Tourist trip design problems (TTDP) merupakan permasalahan yang berkaitan dengan bidang pariwisata. TTDP berkaitan dengan perencanaan pengguna dalam melakukan perjalanan wisata berdasarkan pada tempat wisata yang menarik. Dalam sistem rekomendasi, TTDP merupakan permasalahan yang menarik. Hal ini karena tidak hanya digunakan untuk menemukan tempat wisata yang sesuai dengan pengguna, tetapi juga untuk menggabungkan tempat wisata ke dalam rute perjalanan yang praktis dengan mempertimbangkan batasan. Pada artikel ini bertujuan menyajikan penelitian sebelumnya yang berkaitan dengan proses optimasi rekomendasi perjalanan dan bagaimana permasalahan tersebut dimodelkan menggunakan pendekatan yang berbeda untuk mencari solusi yang optimal. Selain itu peluang penelitian yang dapat dilakukan untuk meningkatkan performa rekomendasi. Berdasarkan synthetic literatur review (SLR) dalam penelitian ini, didapatkan peluang penelitian yang dapat dilakukan untuk mendapatkan rekomendasi rute perjalanan yang optimal seperti kombinasi algoritma metaheuristic atau algoritma bio-inspired. Selain itu pada personalisasi pengguna terkait tempat wisata, terdapat peluang mengimplementasikan algorime deep learning seperti LTSM, Transformer, Bert sebagai nilai tempat wisata dari sisi pengguna
Co-Authors Ayu Purwarianti Bambang Riyanto Trilaksono Dessi Puji Lestari Devin Hoesen E. Aminudin Aziz Emir Mauludi Husni KUSPRIYANTO Masayu Leylia Khodra Mohammad Dimas Ramdani, Ahmad Luky Rani Megasari Rinaldi Munir Ruskanda, Fariska Zakhralativa