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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

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Performance of news clustering using ant colony optimization

Performance of news clustering using ant colony optimization

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Jenson, T. Girsang, A.S. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Humans are basically curious creatures. We always look for all the latest information through books or news. News usually contains tags or categories to make it easier to find similar news. It can be done manually by a human or with a machine to make it easier. Data mining techniques such as clustering and classification can be used to help the categorization process. Clustering is a technique that can be used to grouped news based on their similarity. There is some algorithm that can be used to clustering data. One of them is Ant Colony Optimization (ACO). ACO is a metaheuristic algorithm that mimics how ants bring food to the colony. ACO also can be used to solve the clustering problem. In this paper, we will use the ACO version for clustering to cluster news and find the performance of the said method. Then we compare the result with ground truth. © Published under licence by IOP Publishing Ltd.

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Hotel content-based recommendation system

Hotel content-based recommendation system

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International Conference on science and Technology Applications 2019, ICoSTA 2019

作者： Wahyudi, Kristian Latupapua, Johanes Chandra, Ritchie Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Content based recommendation system tries to recommend items similar to those a given user has likely in the past, whereas systems designed according to the collaborative recommendation paradigm identify users whose preferences are similar to those of the given user and recommend items they have liked. The proposed recommendation system is discussing about Data finitihotel recommendations for a place across U.S. The selected process for the recommendation is calculating the rating of hotel categories on the city. By calculating hotel categories from several city by combining two features namely categories and taken from the city of the selected hotelcategory as well. The result of process will be recommended hotel with highest ratingto the user. © Published under licence by IOP Publishing Ltd.

关键词： Hotels

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Collaborative Recommendation System in Users of Anime Films 4

Collaborative Recommendation System in Users of Anime Films

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Girsang, A.S. Al Faruq, B. Herlianto, H.R. Simbolon, S. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The recommendation system is one method to know the preference consumer by showing the potential object. This recommendation also helps the consumer gets the preference object. One of the popular objects in the recommendation is anime film. In this case, we conduct research to recommend anime films based on ratings of previously watched films. Collaborative filtering is a technique that consists of calculating similarities, predictions, and recommendations. This study is taken from dataset Kaggle which consists of 73,516 users and 12,294 anime. A user's history will be matched with the whole user's history with alternating least squares (ALS) method. The anime will be recommended based on that results. This method is expected to help millions of users find the desired anime. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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Classification of Alzheimer’s Disease using Random Oversampling and Albumentations on Convolutional Neural Network

Classification of Alzheimer’s Disease using Random Oversamp...

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International Conference on Informatics and Computing (ICIC)

作者： Calvin Kamardi I Kadek Perry Bagus Laksana Maria Susan Anggreainy Nora Fitriawati Michiko Amaya Yonatan Tesalonika Abigail Eikwine Mangkang Vianny Pengesa Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

Alzheimer’s Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition.

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Performance of Information Technology Infrastructure Prediction using Machine Learning

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Procedia computer science 2021年 179卷 515-523页

作者： Ignatius Rahardjo Heruwidagdo Suharjito Novita Hanafiah Yanto Setiawan Computer Science Department Binus Graduate Program-Master in Computer Science Bina Nusantara University Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performance. In this study we use a machine learning approach to make predictions about the performance of information technology infrastructure. The experiment took data from several servers in a company to be tested. The performance measure of resources used in this study are CPU Performance, Disk performance, Memory capacity, and Network performance. Several algorithms and machine learning methods are tested, such as Linear Regression, kNN, SVR, Decision Tree and Random Forest, to find the best model fit for the servers. The comparison result shows that Linear regression and kNN perform well in predicting the network performance in those three servers.

关键词： log performance machine learning kNN IT infrastructure performance prediction

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Implementation of naive bayes classifier algorithm in classification of civil servants

Implementation of naive bayes classifier algorithm in classi...

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International Conference on science and Technology Applications 2019, ICoSTA 2019

作者： Achmad, Rifki Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

As the executor of the transfer of civil servants, BKD (Badan Kepegawaian Daerah) is required for professionals in carrying out their duties in mutation positions for civil servants. This study implements a mutation system for civil servants by using Naive Bayes Classifier Algorithm. This study is implemented in Secretariat District Regency of Pelalawan, Riau Indonesia, This system uses some attributes such as age, total work period, education background and implemented on a web based application. This approach is an approximation to Bayes theorem which is the principle of statistical opportunity to combine previous knowledge with new knowledge. This system is very effective classification algorithm (get the right results) and efficient which can be a basic reason in mutation civil servants. © Published under licence by IOP Publishing Ltd.

关键词： Classifiers

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia 2

Lockdown strategy worth lives: The SEIRD modelling in COVID-...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Nurlaila, I. Hidayat, A.A. Pardamean, B. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program 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

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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