IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Articles
476 Documents
<![CDATA[Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space ]]>
<![CDATA[Bob Subhan Riza]]>;
<![CDATA[Jufriadif Na’am]]>;
<![CDATA[Sumijan Sumijan]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.74531
<![CDATA[Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.]]>
<![CDATA[Risk Assessment for Logistics Applications in Cloud Migration ]]>
<![CDATA[Maniah Maniah]]>;
<![CDATA[Benfano Soewito]]>;
<![CDATA[Ford Lumban Gaol]]>;
<![CDATA[Edi Abdurachman]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.74567
<![CDATA[The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration.]]>
<![CDATA[Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network ]]>
<![CDATA[Dzikri Rahadian Fudholi]]>;
<![CDATA[Muhammad Auzan]]>;
<![CDATA[Novia Arum Sari]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.75697
<![CDATA[ Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems.]]>
<![CDATA[Internet of Things (IoT) Arduino-Based Classroom Monitoring Utilizes Temperature Sensors And CO2 Sensors ]]>
<![CDATA[Tri Suratno]]>;
<![CDATA[Edi Saputra]]>;
<![CDATA[Zainil Abidin]]>;
<![CDATA[Daniel Arsa]]>;
<![CDATA[Norman Syarief]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.76241
<![CDATA[Comfort room temperature is determined by indoor air quality, such as temperature and CO2 gas. This study aims to determine the comfort of a class by reviewing the number of students, CO2 gas, and temperature in an Arduino-based classroom using an automatic IoT system with a Completely Randomized Design (CRD) method. Research proves that there is a significant effect between the number of students on the concentration of CO2, but it does not directly affect the air temperature in the room. The lecture hall is still relatively safe but not ideal and requires a temperature reduction of -7oC.]]>
<![CDATA[Financial Distress Prediction with Stacking Ensemble Learning ]]>
<![CDATA[Muhammad Fadhlil Hadi]]>;
<![CDATA[De-Ron Liang]]>;
<![CDATA[Tri Kuntoro Priyambodo]]>;
<![CDATA[Azhari SN]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.76575
<![CDATA[Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.]]>
<![CDATA[GDSS Development of Bali Tourism Destinations With AHP and Borda Algorithms Based on Tri Hita Karana ]]>
<![CDATA[Putu Sugiartawan]]>;
<![CDATA[I Gede Iwan Sudipa]]>;
<![CDATA[I Komang Arya Ganda Wiguna]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.76605
<![CDATA[Development of Bali tourist destinations using the concept of local wisdom Tri Hita Karana (THK). THK is a concept that contains the philosophy of community life in Bali which means three causes of welfare. This concept is needed to realize tourism, culture and nature. In determining a decision to develop an object in a tourist destination using the THK concept, knowledge from several stakeholders is needed. To combine decisions from several stakeholders is needed. GDSS is a computer-based system that can support the Bali Provincial Government Tourism Office and several components involved in THK to take a decision in developing an object in a tourist destination. To determine the decision of each individual used the AHP model. The AHP model is a model that can solve complex multi-criteria problems into a hierarchy. This AHP model will produce alternative individual decisions from the results of parameter weight processing for each individual. Based on the final result of the GDSS, the development of Bali tourism destinations based on THK is in the form of ranking of the six parameters used (Promotion of tourist destinations, Improvement of facilities, Human Resources, Synergy, Environmental preservation, Setting of holy places). The alternative that has the highest value is used as a reference in developing a THK-based tourist destination,]]>
<![CDATA[Automatic Detection of Helmets on Motorcyclists Using Faster - RCNN ]]>
<![CDATA[Aliyyah Nur Azhari]]>;
<![CDATA[Wahyono Wahyono]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.68245
<![CDATA[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]]>
<![CDATA[The usefulness of an Augmented Reality-based Interactive 3D Furniture Catalog as a Tool to Aid Furniture Store Sales Operations ]]>
<![CDATA[Ismail Ismail]]>;
<![CDATA[Evan Syaputra]]>;
<![CDATA[Benny Dwika Leonanda]]>;
<![CDATA[Nur Iksan]]>;
<![CDATA[Azmi Shawkat Abdulbaqie]]>;
<![CDATA[Mohd Razimi Husin]]>;
<![CDATA[Hishamuddin Ahmad]]>;
<![CDATA[Ismail Yusuf Panessai]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.69570
<![CDATA[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.]]>
<![CDATA[Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding ]]>
<![CDATA[Muhammad Haris Maulana]]>;
<![CDATA[Masayu Leylia Khodra]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.72981
<![CDATA[ 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.]]>
<![CDATA[Traditional Music Regional Classification using Convolutional Neural Network (CNN) ]]>
<![CDATA[Raymond Luis]]>;
<![CDATA[Nur Rokhman]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.73910
<![CDATA[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%.]]>
