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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CommIT (Communication & Information Technology) Indonesian Journal of Electrical Engineering and Computer Science Engineering, Mathematics and Computer Science Journal (EMACS)
Tjeng Wawan Cenggoro
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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 1 
Deep Learning for Crowd Counting: A Survey Tjeng Wawan Cenggoro
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 1 No. 1 (2019): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v1i1.5794

Abstract

The growth of deep learning for crowd counting is immense in the recent years. This results in numerous deep learning model developed with huge multifariousness. This paper aims to capture a big picture of existing deep learning models for crowd counting. Hence, the development of novel models for future works can be accelerated.
Observing Pre-Trained Convolutional Neural Network (CNN) Layers as Feature Extractor for Detecting Bias in Image Classification Data Amadea Claire Isabel Ardison; Mikhaya Josheba Rumondang Hutagalung; Reynaldi Chernando; Tjeng Wawan Cenggoro
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8144

Abstract

Detecting bias in data is crucial since it can pose serious problems when developing an AI algorithm. The research aims to propose a novel study design to detect bias in image classification data by using pretrained Convolutional Neural Network (CNN) layers as a feature extractor. There are three datasets used in the research with varying degrees of complexity, those are low, medium, and high complexity. There are Modified National Institute of Standards and Technology (MNIST) Digits, batik collections (Parang, Megamendung, and Kawung), and Canadian Institute for Advanced Research (CIFAR-10) datasets. Then, the researchers make a baseline workflow and substitute a step-in feature extraction with a convolution using the first pre-trained CNN layer and each of its kernels. Then, the researchers evaluate the effect of the experiments using accuracy. By observing the effect of the individual kernel, the research can better make sense of what happens inside a CNN layer. The research finds that color in the image is an essential factor when working with CNN. Furthermore, the proposed study design can detect bias in image classification data where it is related to the color of the image. Detecting this bias early is important in helping developers to improve AI algorithms.
Fast Ant Colony Optimization for Clustering Abba Suganda Girsang; Tjeng Wawan Cenggoro; Ko-Wei Huang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp78-86

Abstract

Data clustering is popular data analysis approaches, which used to organizing data into sensible clusters based on similarity measure, where data within a cluster are similar to each other but dissimilar to that of another cluster. In the recently, the cluster problem has been proven as NP-hard problem, thus, it can be solved with meta-heuristic algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO), respectively. This paper proposes an algorithm called Fast Ant Colony Optimization for Clustering (FACOC) to reduce the computation time of Ant Colony Optimization (ACO) in clustering problem. FACOC is developed by the motivation that a redundant computation is occurred in ACO for clustering. This redundant computation can be cut in order to reduce the computation time of ACO for clustering. The proposed FACOC algorithm was verified on 5 well-known benchmarks. Experimental result shows that by cutting this redundant computation, the computation time can be reduced about 28% while only suffering a small quality degradation.
Energy-efficient deep learning model for fruit freshness detection Febrian Valentino; Tjeng Wawan Cenggoro; Gregorius Natanael Elwirehardja; Bens Pardamean
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1386-1395

Abstract

Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.
Co-Authors Abba Suganda Girsang, Abba Suganda Amadea Claire Isabel Ardison Bens Pardamean Febrian Valentino Gregorius Natanael Elwirehardja Ko-Wei Huang Mikhaya Josheba Rumondang Hutagalung Reynaldi Chernando