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2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011

作者： Budiwati, Sari Dewi Haryatno, Joko Dharma, Eddy Muntina Information System Study Program Telkom Polytechnic Indonesia Electrical Engineering Department Institute Technology of Telkom Indonesia

ISBN: (纸本)9781457707520

Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters. This is the reason why we need pattern recognition. Handwriting recognition has been the object of intensive research during the last decades, including Japanese characters recognition. In this research, we build an application to recognize Kanas character handwriting. Pattern recognition on Kana is started based on Optical Character Recognition (OCR) consist of scan image, preprocessing, feature extraction and post-processing. Classification process will be done in post-processing using neural network back propagation. The processing of input images, involves training and testing image. Training image uses 3.956 characters done in three phases of training. Testing image uses 552 characters of some images inputted in training phase and image which has not been used before. The test result achieve maximum accuracy of recognition system at 91.88% from train image and 31.03% from test image. © 2011 IEEE.

关键词： Backpropagation

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The Investigation of Student Engagement as Mediator in ICT Readiness and Experience on E-Learning Effectiveness in Post-Pandemic Recovery 9

The Investigation of Student Engagement as Mediator in ICT R...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Sofia Amriza, Rona Nisa Saintika, Yudha Firmansyah, Aruga Yudish Information System Study Program Faculty of Informatics Institut Teknonogi Telkom Purwokerto Purwokerto Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

E-learning is an asynchronous or synchronous communication mode to confirm knowledge through electronic media. The implementation of e-learning in Indonesia has many challenges, including the low independent learning culture, which creates the perception that the absence of direct interaction between lecturers and students and e -learning does not always enhance students' abilities to study independently. The effectiveness of e-learning needs to assess to assist e-learning providers with product customization so that e-learning matches the needs and expectations of users. This study analyzes the effect of information and communication technology (ICT) readiness and experience on the effectiveness of e-learning with student engagement as a mediator. This research used Structural Equation Modeling Partial Least Square (SEM-PLS) to test the hypothesis. The results of this study are the mediation of emotional engagement on readiness, cognitive engagement on information seeking, cognitive engagement on socialization and behavior engagement on readiness and socialization have a significant effect on e-learning effectivity. © 2022 IEEE.

关键词： E-learning

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Deep Learning-Based Disaster Sentiment Analysis of Unbalanced Twitter Data: The Case of Indonesia Earthquake 11

Deep Learning-Based Disaster Sentiment Analysis of Unbalance...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Amriza, Rona Nisa Sofia Wijayanto, Sena Institut Teknonogi Telkom Purwokerto Information System Study Program Faculty of Informatics Purwokerto Indonesia

ISBN: (纸本)9798350305968

The unbalanced dataset dramatically impacted the effectiveness of classification techniques, often leading to classifiers that were biased towards the majority class. Researchers had proposed deep learning along with random undersampling and oversampling to address this issue. This study conducted a comparative analysis between undersampling and oversampling methods for handling imbalanced class data. In addition, a deep learning approach was employed, specifically utilizing bidirectional LSTM and attention mechanisms. The method was assessed using a confusion matrix. The findings of this study demonstrated that oversampling surpassed undersampling, achieving an impressive F1 score of 79.87%. © 2023 IEEE.

关键词： Sentiment analysis

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The Understanding of Customer Satisfaction on A Fintech Application Using A Machine Learning Approach 12

The Understanding of Customer Satisfaction on A Fintech Appl...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Amriza, Rona Nisa Sofia Nurhakim, Fajrin Januarita, Dwi Institut Teknonogi Telkom Purwokerto Information System Study Program Faculty of Informatics Purwokerto Indonesia

ISBN: (纸本)9798350341102

Fintech companies should prioritize assessing customer satisfaction and focus on specific market segments to attract and retain customers effectively. This study investigates customer satisfaction levels within the *** application, utilizing the PIECES framework and machine learning techniques. Among various models, the Random Forest classification emerges as the superior performer. With an 80% training data and 20% testing data split, the model achieves impressive metrics: accuracy of 92.7%, F1 score of 91.9%, precision of 91.8%, and recall of 92.7%. Detailed analysis using the Confusion Matrix reveals that 134 instances labeled as 'Satisfied' were correctly classified, five instances labeled as 'Uncertain' were accurately categorized, and one instance labeled as 'Very Satisfied' was correctly identified. In conclusion, machine learning plays a significant role in analyzing and classifying customer satisfaction levels, with Random Forest outperforming other machine learning models. © 2023 IEEE.

关键词： Customer satisfaction

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The Smart Mobility Insight of Bus Rapid Transit (BRT) Trans Jateng Purwokerto-Purbalingga Ridership 8

The Smart Mobility Insight of Bus Rapid Transit (BRT) Trans ...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Kusumawardani, Dwi Mustika Saintika, Yudha Romadlon, Fauzan Information System Study Program Faculty of Informatics Institut Teknologi Telkom Purwokerto Purwokerto Indonesia Industrial Engineering Study Program Faculty of Industrial Engineering and Design Institut Teknologi Telkom Purwokerto Purwokerto Indonesia

ISBN: (纸本)9781665416979

The rapid development of technology presents a new paradigm, one of which is in the transportation sector. The concept of smart mobility is one of the components in the realization of a smart city, which is closely related to the transportation sector. The Purwokerto-Purbalingga Bus Rapid Transit (BRT) is the government's effort to encourage the transportation sector towards smart mobility. It requires three crucial categories: Accessibility, sustainability, and communication and information technology. This study proves and explains that these three categories can be fulfilled by the Purwokerto-Purbalingga Bus Rapid Transit, especially from women's perception, indicated by an average accessibility score of 4.04, sustainability of 4.22, and information and Communication Technology of 3.80. Efforts that can be taken by the government and Bus Rapid Transit managers include monitoring and evaluating bus stops and bus arrival frequencies as well as developing mobile-based applications that provide real-time information related to bus rapid transit. © 2021 IEEE.

关键词： Sustainable development

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Unraveling Game Addiction Behavior Among Digital Native Students: Leveraging Machine Learning for Insights 13

Unraveling Game Addiction Behavior Among Digital Native Stud...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Amriza, Rona Nisa Sofia Fadhilla, Cut Alna Telkom University Department of Information System Purwokerto Indonesia Universitas Samudra Informatics Study Program Faculty of Engineering Aceh Indonesia

ISBN: (纸本)9798350368086

The allure of gaming often leads to excessive engagement, which can result in addictive and problematic behaviors, negatively impacting academic performance and school achievements. While gaming becomes increasingly prevalent, concerns about game addiction have grown, particularly among students. The excessive and obsessive usage of video games that characterizes game addiction can have detrimental effects like academic deterioration, social isolation, and mental health problems. This study seeks to elucidate the complex patterns of game addiction among digital-native students by utilizing advanced machine learning techniques. We employed Random Forest, SVM, Decision Tree, and Naïve Bayes algorithms to classify game addiction data from students. To address the class imbalance, we applied Borderline-SMOTE. Our results demonstrate that decision tree and random forest outperformed other machine learning approaches, achieving an accuracy of 95% in classifying game addiction. These findings contribute to the expanding body of knowledge on game addiction. Furthermore, in order to provide real-time monitoring and intervention for students who are at risk of developing a game addiction, future research may investigate incorporating these findings into educational platforms or applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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Japanese character (Kana) pattern recognition application using neural network

Japanese character (Kana) pattern recognition application us...

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International Conference on Electrical Engineering and Informatics, ICEEI

作者： Sari Dewi Budiwati Joko Haryatno Eddy Muntina Dharma Information System Study Program - Telkom Polytechnic Electrical Engineering Department Institute Technology of Telkom

Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters. This is the reason why we need pattern recognition. Handwriting recognition has been the object of intensive research during the last decades, including Japanese characters recognition. In this research, we build an application to recognize Kana's character handwriting. Pattern recognition on Kana is started based on Optical Character Recognition (OCR) consist of scan image, preprocessing, feature extraction and post-processing. Classification process will be done in post-processing using neural network back propagation. The processing of input images, involves training and testing image. Training image uses 3.956 characters done in three phases of training. Testing image uses 552 characters of some images inputted in training phase and image which has not been used before. The test result achieve maximum accuracy of recognition system at 91.88% from train image and 31.03% from test image.

关键词： Training Testing Neurons Character recognition Biological neural networks Feature extraction Writing

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Analysis and Design of Decision Support system Dashboard for Predicting Student Graduation Time

Analysis and Design of Decision Support System Dashboard for...

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Satrio Wibowo Rachmadita Andreswari Muhammad Azani Hasibuan Information System Study Program Telkom University Bandung Indonesia

ISBN: (纸本)9781538684030

information systems is one of the existing study program at telkom University that has produced many graduates since it was established in 2008. However, not all graduates produced successfully completed the study period during the four years of normal study. The percentage of graduates on time has some decline between the target and the achievement of the study program. From academic year 2014/2015 to 2016/2017 decrease annually about 1% every year, which is it becomes problems for the credibility and existence of study program and also for academic planners who may have an impact on accreditation assessment process of the study program when it is audited. One of the efforts that can be done by the study program to increase the students on time graduation rate is by making decision support system dashboard that giving early warning to the lecturer or the head of the study program if there are students who are predicted not to graduate on time. By using the C4.5 algorithm to perform the data analysis by looking at the causes of student's graduation time and pureshare methodology to perform dashboard development method. The result of this study is a prototype of decision support system dashboard, because there are lack of analysis in decision making and the dashboard only showing information and temporary prediction. The data model that used on this research is labeling data that has been processed using C4.5 algorithm and data that has been through data cleansing process using Pentaho Data Integration. This prototype is expected to be used as a reference base to support academic planners in order to make this application run with real time data.

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Deep Learning-Based Disaster Sentiment Analysis of Unbalanced Twitter Data: The Case of Indonesia Earthquake

Deep Learning-Based Disaster Sentiment Analysis of Unbalance...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Rona Nisa Sofia Amriza Sena Wijayanto Information System Study Program Faculty of Informatics Institut Teknonogi Telkom Purwokerto Purwokerto Indonesia

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