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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
 1 
Search results for , issue "Vol 16, No 4 (2022): October" : 10 Documents clear
Automatic Detection of Helmets on Motorcyclists Using Faster - RCNN Aliyyah Nur Azhari; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.68245

Abstract

Motorcycles have been a popular choice for a go-to daily means of transportation due to its lower price, making it affordable for high to low-class citizens. Helmets are required for every motorcycle owner so that the rider’s head is protected from accidents. However, not many people follow the rules and tend to not wear helmets and plenty of them underestimate the usage of helmets. For this, it is necessary to implement a system that can detect which rider wears the helmet or not by applying deep learning techniques. This paper aims to implement one of the deep learning techniques, which is Faster R – CNN to detect the helmets and the motorcyclists. After training 400 images using different learning rates, the mean average precision (mAP) achieved the highest with 87% using the learning rate of 0.0001
The usefulness of an Augmented Reality-based Interactive 3D Furniture Catalog as a Tool to Aid Furniture Store Sales Operations Ismail Ismail; Evan Syaputra; Benny Dwika Leonanda; Nur Iksan; Azmi Shawkat Abdulbaqie; Mohd Razimi Husin; Hishamuddin Ahmad; Ismail Yusuf Panessai
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.69570

Abstract

The global crisis, that has resulted from the outbreak of Covid-19, influences all aspects of daily life. Due to the people's poor purchasing power, several major stores, such as Furniture Store-XYZ, were forced to close several branches. To counter this, it will be required to adopt unique initiatives that will assist attract visitors and enhance sales while still adhering to the established health protocols. AR-Furniture is the ideal technology to solve this problem. AR-Furniture is an Augmented Reality-based technology that enables a 3D furniture catalog to present a complete picture of a piece of furniture in a virtual form that appears natural and identical to the original. The MDLC development process used in the AR-Furniture Mobile App. According to the study's findings, 100% of respondents agree that AR-Furniture helps to sell and to buy process be done effectively and productively and gives the users innovative ideas. 70% of respondents strongly agree that AR-Furniture makes it easier for users to reach their goals and that AR-Furniture allows users to do whatever they want. 100% of respondents strongly believe that AR-Furniture is helpful and that shoppers can save time while picking the right furniture. Furthermore, AR-Furniture makes it simple for consumers to select preferred furniture without engaging with shopkeeper workers.
Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding Muhammad Haris Maulana; Masayu Leylia Khodra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.72981

Abstract

 Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data.  Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance.
Traditional Music Regional Classification using Convolutional Neural Network (CNN) Raymond Luis; Nur Rokhman
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.73910

Abstract

Traditional Indonesian music is an Indonesian cultural heritage that is often forgotten by modern society. Many people do not know which area the traditional music came from. This is a problem because of the large amount of traditional music that loses its identity. Deep Learning technology can be a solution to this traditional music classification problem. The topic of traditional music classification was chosen because there has been no research using this topic before.This research will classify traditional music based on the area of origin using data from Youtube with the extraction method of the Mel-Frequency Cepstral Coefficients (MFCC) feature and the Convolutional Neural Network (CNN) classification model. There are 7 provinces that will be used as classification labels, namely Riau, Papua, Special Capital District of Jakarta, Special Region of Yogyakarta , North Sumatra, West Java, and South Sulawesi.The classification system produced in this study produced good classification accuracy with a value of 74.03%.
Topic Modeling on Online News.Portal Using Latent Dirichlet Allocation (LDA) Mohammad Rezza Fahlevvi; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.74383

Abstract

The amount of News displayed on online news portals. Often does not indicate the topic being discussed, but the News can be read and analyzed. You can find the main issues and trends in the News being discussed. It would be best if you had a quick and efficient way to find trending topics in the News. One of the methods that can be used to solve this problem is topic modeling. Theme modeling is necessary to allow users to easily and quickly understand modern themes' development. One of the algorithms in topic modeling is the Latent Dirichlet Allocation (LDA). This research stage begins with data collection, preprocessing, n-gram formation, dictionary representation, weighting, topic model validation, topic model formation, and topic modeling results.            Based on the results of the topic evaluation, the. The best value of topic modeling using coherence was related to the number of passes. The number of topics produced 20 keys, five cases with a 0.53 coherence value. It can be said to be relatively stable based on the standard coherence value.
Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia Nur Fadilah; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76396

Abstract

Essay is one of the assessments to find out the abilities of students in depth.  UKARA is an automatic essay scoring development that combines NLP and machine learning.  This study uses the datasets provided for the UKARA challenge which consists of 2 types, datasets A and B. The dataset provided is still small for the model creation  process so that it is one of the causes of the resulting model is not optimal. This research focuses on the process of adding or augmenting data using EDA (Easy Data Augmentation Techniques). There are four methods applied, namely Synonym Replacement (SR), Random Insertion (RI), Random Swab (RS), and Random Deletion (RD).  The data is used for model creation by using the BiLSTM method. Performa model evaluated using confusion matrix with nilai accyouracy, precision, recall dan f-measure.The results showed that the dataset A without augmentation using k-fold cross validation produced the highest accuracy value with a value of 85.07%. While the results in data B show EDA insert with k-fold cross validation of 72.78%.
Real-Time Face Recognition Civil Servant Presence System Using DNN Algorithm Yogi Angga Putra; Imelda Imelda
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.77026

Abstract

Facial recognition has become a growing topic among Computer Vision researchers because it can solve real-life problems, including during the COVID-19 pandemic. The pandemic is why the Indonesian government has imposed social restrictions and physical contact in public places. Before the pandemic, most touch-based attendance systems used fingerprints or Radio Frequency Identification (RFID) cards. The solution proposed in this study is to identify real-time facial recognition of the Civil Service presence system using a Deep Neural Network. The goal is to minimize physical contact. The research stages include data collection, augmentation and preprocessing, CNN modeling and training, model evaluation, converting to OpenCV DNN, implementation of transfer learning, and identification of test data. This research contributes to testing variations in distance and position so it can recognize a person's face even when wearing a mask and glasses. This DNN model produces a validation accuracy value of 99.48% and a validation loss of 0.0273 with a data training process of 10 times. Tests for variations in distance, position, use of masks, and glasses on MTCNN detection provide an average accuracy for each trial of 100%, 96%, and 100%, respectively. Therefore, the average accuracy of the Haar Cascades detection test is 100%, 85%, and 100%.
Naive Bayes Method and C4.5 in Classification of Birth Data Asep Afandi; Noviana Noviana; Deti Nurdianah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.78198

Abstract

Data on the birth and productive age of a mother to get pregnant in Lampung is still high. to find out the comparison of the productive age of pregnant women and whether they have met the minimum and maximum requirements for a mother to become pregnant, and the criteria for babies born. Where the results of data processing will be used as a source of data for counseling mothers, especially for residents of Banjar Kertahayu village. The data processing requires a special method so that the results become a benchmark for a decision later, such as Data Mining. The method used for data processing used is Naive Bayes and C4.5 Algorithm. The data used is birth data in 2017-2021, the source of data from the Banjar Village Midwife-Central Lampung Regency. Research Results Method C 4.5 Middle age has a dominant age category value of 0.3324138. where the highest value is in 2017, and accuracy is 100 percent from the 2017-2021 data. The baby weight criterion using the Naïve Bayes Class Method has a dominant Middle-aged category value of 0.09675, the highest value in 2017, The results of accuracy for 5 years have accuracy of 92.84% based on 2017-2021 birth data
Forecasting Indonesian Oil, Non-Oil and Gas Import Export with Fuzzy Time Series Syalam Ali Wira Dinata; Ayuning Arum Purbosari; Primadina Hasanah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.78399

Abstract

 Indonesia is active in export and import activities. Some of the commodities traded are oil and gas, as well as food and other industrial materials. Export and import activities play a role in determining the stability of the country's economy seen from its trade balance. According to the Central Statistics Agency, Indonesia experienced a deficit of USD 864 million due to a decline in exports at the beginning of 2020. Based on the state of the trade balance, the government needs to make policies in order to maintain the stability of the Indonesian economy. The right decision-making must be supported by accurate information, therefore, through this research, the value of Indonesia's exports and imports will be forecasted in the oil and gas and non-oil and gas sectors for the next period using the Fuzzy Time Series (FTS). FTS was chosen as the forecasting method because it is able to predict free real time data with arbitrary patterns. The data used is data on the value of exports and imports of oil and gas and non-oil and gas sectors for 2011-2020. To overcome the problem of stationary data variance and reduce the error value, a Box Cox transformation will be applied. The research stages include data transformation with Box Cox, forming universe and linguistic sets, determining interval length, fuzzification, forming FLR and FLR, defuzzification and forecasting. The final forecast results estimate that exports and imports in the oil and gas sector in 2021 will decline, while for the non-oil and gas sector will fluctuate and increase from the previous year. Forecasting with Box Cox transform data is more accurate with MAPE 19.56% and RMSE 121.52 compared to forecasting with original data with MAPE 74.89% and RMSE 132.09.
Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion I Komang Arya Ganda Wiguna; Putu Sugiartawan; I Gede Iwan Sudipa; I Putu Yudi Pratama
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.78664

Abstract

 Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy.

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