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All Journal CommIT (Communication & Information Technology) ComTech: Computer, Mathematics and Engineering Applications Indonesian Journal of Electrical Engineering and Computer Science
Iman Herwidiana Kartowisastro
Bina Nusantara University
Computer Science & IT Engineering Mathematics

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The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval Budi Yulianto; Widodo Budiharto; Iman Herwidiana Kartowisastro
CommIT (Communication and Information Technology) Journal Vol. 11 No. 1 (2017): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i1.2108

Abstract

Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size.
Pengukuran Jarak Berbasiskan Stereo Vision Iman Herwidiana Kartowisastro
ComTech: Computer, Mathematics and Engineering Applications Vol. 1 No. 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2559

Abstract

Measuring distance from an object can be conducted in a variety of ways, including by making use of distance measuring sensors such as ultrasonic sensors, or using the approach based vision system. This last mode has advantages in terms of flexibility, namely a monitored object has relatively no restrictions characteristic of the materials used at the same time it also has its own difficulties associated with object orientation and state of the room where the object is located. To overcome this problem, so this study examines the possibility of using stereo vision to measure the distance to an object. The system was developed starting from image extraction, information extraction characteristics of the objects contained in the image and visual distance measurement process with 2 separate cameras placed in a distance of 70 cm. The measurement object can be in the range of 50 cm - 130 cm with a percentage error of 5:53%. Lighting conditions (homogeneity and intensity) has a great influence on the accuracy of the measurement results. 
A hybrid CNN-autoencoder-SVM/XGBoost model for polyphonic orchestral instrument classification Kelvin Wyeth; Iman Herwidiana Kartowisastro
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp114-126

Abstract

Polyphonic orchestral recordings pose significant challenges in music information retrieval (MIR) due to their overlapping frequency ranges and timbral similarities among instrument families, which complicate multi-label instrument classification. Prior studies have explored the integration of convolutional neural networks (CNN)-based feature extraction with classical machine learning (ML) classifiers, often on monophonic or simpler datasets like IRMAS. But the integration of deep learning (DL) feature extraction, Autoencoder (AE)-based dimensionality reduction, and ML classifiers for polyphonic orchestral instrument recognition remains underexplored. This study proposes a hybrid framework utilizing a pre-trained Inception V3 CNN for feature extraction from mel-spectrograms, followed by an optional 50% dimensionality reduction via AE, and finally, classification with support vector machines (SVM) or extreme gradient boosting (XGBoost). Experiments were run on two polyphonic datasets, OpenMIC-2018 and Orchset. The results demonstrate that non-AE configurations generally outperform AE variants. These results extend prior studies such using polyphonic datasets. The results highlight the practical value of hybrid CNN-ML pipelines and the trade-offs of feature compression in MIR.
Co-Authors Kelvin Wyeth Widodo Budiharto Yulianto, Budi