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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Harvy, Ikrar Girsang, Abba Suganda Saragih, Melva Hermayanty Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods. © 2022 IEEE.

关键词： classification news convolution neural network deep learning journalist Social media

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Fostering Digital Innovation in Indonesia: The Role of STEM-AI Skills in Overcoming Talent Deficits and Gender Stereotypes 4

Fostering Digital Innovation in Indonesia: The Role of STEM-...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Halim, Erwin Zahra, Amalia Maryani Wulandhari, Lili Ayu Fitrianah, Devi Puspita, Virienia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Communication Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

This research explores the optimization of digital talent in advanced industries, particularly in the context of rapid digital transformation. Despite the increasing importance of digital talent for gaining a competitive edge, several challenges persist. In Indonesia, for example, there is a significant shortage of digitally skilled workers, with the World Bank reporting a gap of 9 million skilled individuals over the past 15 years. Additionally, artificial intelligence (AI) is expected to impact 9.5 million jobs, further exacerbating the demand for digital skills. Furthermore, the employability of graduates remains low, largely due to inadequate training, poor language proficiency, and limited cultural sensitivity. Many companies face difficulties in finding qualified individuals, quickly upskilling their workforce, and fostering an innovative organizational culture. This study seeks to evaluate the factors influencing digital talent performance in the context of these challenges. It examines seven key variables: digital readiness, digital technology adoption, relevance of AI, digital skills, individual performance, problem-solving ability, and overall digital talent performance. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data was collected from 378 participants, primarily students with STEM backgrounds, in the Jabodetabek area of Indonesia in November 2024. The findings reveal that all hypotheses are significant, with results indicating that user satisfaction and the perceived value of digital talent in Indonesia have a notable impact on continued usage intentions in digital roles. © 2024 IEEE.

关键词： digital readiness digital talent performances digital technology individual performance STEM-AI

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White rice stem borer pest detection system using image-based convolution neural network 9th

White rice stem borer pest detection system using image-base...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Saufi, Akhmad Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation-the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur. © 2024 The Authors.

关键词： Convolutional neural networks

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Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online during the Covid-19 Pandemic

Analysis of Factors Influencing Decisions to Purchase Airlin...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Lie, Yulius Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions. © 2023 IEEE.

关键词： Sales

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Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset

Single and Multi-Documents Text Summarization Technologies f...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Sukmandhani, Arief Agus Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Bina Nusantara University Binus Online Learning Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665468763

The summarization technologies have been increasingly used in recent decades. Those technologies are a very important part of emerging topics in computer science and engineering, such that Natural Language Processing (NLP). Several methods have been used for analysis to get good summary results. There are two types of document summaries: single document summaries and multi-document summaries. Single document summaries aim to extract information from a single document to get new and relevant summary information, while multi-document summaries extract information from multiple documents. This study focuses on the activities of the semantic literature in previous studies to obtain the basis of the widely used base methods and data sets used in this study. Data was collected from Scopus publication sources from 2019 until 2022 Q2 for analysis. Researchers use guidelines from the semantic literature method by using the basis of Kitchenham and Charters as a reference in its design. In this study, there were forty-eight articles obtained from the filtering results of several criteria used, including exclusion and quality assessment. © 2022 IEEE.

关键词： Semantics

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science 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 Program Bina Nusantara University Jakarta11480 Indonesia

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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Indonesian Agricultural-crops Classification Using Transfer Learning Model 8

Indonesian Agricultural-crops Classification Using Transfer ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNetB0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. © 2023 The Authors. Published by Elsevier B.V.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry

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Procedia computer science 2024年 245卷 480-489页

作者： Muhammad Asrol Napthaleni Ditdit Nugeraha Utama Suharjito Christian Harito Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul 05006 South Korea

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content.

关键词： Clustering Fuzzy Hybrid model Inference System Prediction Sugarcane

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