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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Muhammad Juan Al Qausar Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Navigating the complex legal and regulatory landscape requires a sophisticated platform that is not only comprehensive but also user-friendly and enables seamless analysis and document comparison in the legal realm. To address this challenging requirement, this research is dedicated to the user experience (UX) design of state-of the-art cloud-native mobile applications specifically designed for legal and regulatory harmonization. The methodology used is based on design thinking principles and takes advantage of the benefits of cloud computing. These include, but are not limited to, scalability, reliability, and security. The result is a solution that is not only powerful but also cost-effective. The core of this application follows a user-centered design philosophy and involves potential users closely in the design process. A comprehensive evaluation is performed using the System Usability Scale (SUS) methodology to measure the effectiveness and usability of the application. The results of this comprehensive approach are convincing. The application has an impressive 95.5% usage rate, indicating strong user engagement. Furthermore, user satisfaction ratings are particularly high and transformative impact of this application in a complex environment of legal harmonization. Through its innovative design, seamless functionality, and user-centric approach, this application has proven to be not only a technical solution, but also a catalyst for positive change in the legal and regulatory harmonization process.

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Drones computer Vision using Deep Learning to Support Fishing Management in Indonesia 3

Drones Computer Vision using Deep Learning to Support Fishin...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Triany, Natassya Afdalena Faisal Sulistiawan, Jaka Yojana, Albert Cahya Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

It is necessary to study technical factors such as bait used, oceanographic conditions of fishing areas and skipjack tuna trade patterns (Katsuwonus pelamis) as well as other factors in Sulawesi Fisheries. Supporting data to study these technical factors need to be obtained so that it can be improved throughout the year as a skipjack fishing standard with weights taken by the skipjack weights. Therefore, drones and underwater drones with cameras with high-resolution video and images to be fed and processed by computer vision. These drones are operated via Huhate ships which are then used to test efficient technical factors as expected, can be used in determining the size, shape, and colour as well as the analysis of skipjack fish that are in the Fish Aggregating Devices (FADs or Rumpon) in order to manage the management of fisheries managed in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Drones

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Kus Andriadi Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large.

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer science and Information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification

Downstream Task Performance of Pre-Trained Model For Outdoor...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Theresia Herlina Rochadiani Yaya Heryadi Ilvico Sonata Lukas Wawan Suparta BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Fuzzy water flow algorithm (FWFA) based ecological decision support model (ecoDSM) for flood control operating system

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AIP Conference Proceedings 2023年第1期2896卷

作者： Ditdit Nugeraha Utama Rachmat Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Intiland Development Jakarta Indonesia 10220

Flood disaster happens periodically in Jakarta. One opportunity that can be maximized to mitigate it is control management of the Manggarai water gate (MWG). The MWG itself is placed in the Ciliwung river in one district of the capital Jakarta. It has a vital role to minimize the impact of flood disasters in Jakarta. The study is a further version of previous research indeveloping a water-flow-like algorithm (WFA) based decision support model (DSM) to mitigate the impact of flood disasters. Theconstructed DSM is practically functioned to control the operation of opening/closing the MWG. The DSM was constructed by combining the previous WFA model with the concept of fuzzy logic (FL). The constructed model is called Fuzzy-WFA (FWFA) ecological DSM (ecoDSM); as it considers several aspects of ecology, e.g. river condition, weather situation, and flood itself. By building the FWFA-ecoDSM, the value of uncertainty can be converted to become crisp logic that is easier to understand. At the end of this research, the FWFA-ecoDSM was implemented in more detail compared with the previous one by producing the decimal numbers with 3 until 6 numbers behind the point; exclusively, in predicting the water level status, where the status used todecide the opening/closing level decision for opening or closing the MWG.

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A Review of Implementation and Obstacles in Predictive Machine Learning Model at Educational Institutions 2

A Review of Implementation and Obstacles in Predictive Machi...

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2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Ranggadara, Indra Sari, Yunita Sartika Dwiasnati, Saruni Prihandi, Ifan Sfenrianto Computer Science Department Mercu Buana University Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Machine learning is a system that can learn by itself by using training data and testing data testing. In a variety of machine learning research has various obstacles in implementation especially in education institution, in its application, many things need to considered for more information to enrich research in the area of machine learning by way of comparing various obstacles in implementation in similar research. So that in this study is to find similarities and anticipations made in the initial stages of pre-processing to visualisation, as well as the contribution that can give in this study explains the best alternative for researching following the experience of several researchers in conducting machine learning research. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Machine learning

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computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries 3

Computer Vision and Fuzzy Logic for Sustainable Indonesian F...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Saputra, Adi Sakinah, Putri Purwoko, Hery Zulardi, Indra Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier

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Procedia computer science 2023年 227卷 482-489页

作者： Albert Verasius Dian Sano Adriel Anderson Stefanus Evaristus Didik Madyatmadja Hendro Nindito Agung Purnomo Corinthias P.M. Sianipar Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Department of Global Ecology Graduate School of Global Environmental Studies (GSGES) Division of Environmental Science and Technology Graduate School of Agriculture (GSA) Kyoto University Kyoto 606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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