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Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting

Impact of Filter-Based Feature Selection on Prediction Model...

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International Conference on informatics and Computing (ICIC)

作者： Sri Handani Widiastuti Purwanto Adi Wibowo Ratih Wirapuspita Wisnuwardani Informatics Engineering Study Program Sekolah Tinggi Teknologi Bontang Indonesia Doctoral Program of Information System School of Postgraduate Studies Diponegoro Univercity Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro Univercity Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro Univercity Semarang Indonesia Faculty of Public Health Mulawarman Univercity Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study investigates the impact of feature selection results using the filter method on the performance of predictive models for the nutritional status of children aged 0-23 months. This study aimed to understand how correlation-based feature selection methods affect the efficiency and accuracy of predictive models. The dataset comes from the Basic Health Research survey, which consists of 84 variables and around 130,000 data, including anthropometric, family, socioeconomic, and environmental information. In this study, various filter-based feature selection methods were applied to evaluate and identify features that have significant relationships with children’s nutritional status, both linearly and non-linearly. The mutual information method was used to identify complex relationships between features and target variables, while the Pearson Correlation Coefficient and information gain were used to assess the relevance of numerical and categorical features. Of the 84 variables in the dataset, 37 features were selected after the selection process. The model performance evaluation was carried out using the Random Forest (RF) and Gradient Boosting (GB) algorithms, by comparing the results between the dataset that had been selected and without feature selection. The RF model without feature selection produces accuracy: 0,84600, precision: 0,83958, recall: 0,84600, F1-score: 0,78962, and a computation time of 9 seconds. The GB model without feature selection produces accuracy: 0,86330, precision: 0,84722, recall: 0,86330, F1-score: 0,84925, and a computation time of 2 minutes 50 seconds. After feature selection and cross-validation, the RF model shows accuracy: 0,85701, precision: 0,83599, recall: 0,85701, and F1 score: 0,83484. The GB model shows accuracy at 0,86124, precision at 0,84226, recall at 0,86130, and F1 score at 0,84120.

关键词： Radio frequency Pediatrics Accuracy Correlation Computational modeling Predictive models Aging Feature extraction Prediction algorithms Numerical models

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Deep Learning for Cyber Security: Deephoax Detection 2

Deep Learning for Cyber Security: Deephoax Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Yakub, Sy. Yuliani Wowor, Rivo Juicer Agustriawan, David Irmawati Kristiyanti, Dinar Ajeng Winarno, P.M. Hassan, Rosilah Mohamad, Roshayu Razzaq, Randy Zahran Universitas Multimedia Nusantara Department of Informatics Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Uin Sultan Maulana Hasanuddin Department of Informatics Banten Indonesia

ISBN: (纸本)9798331506995

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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Use of an Expert System to Diagnose and Provide Solutions for Pests and Diseases in Hydroponic Mustard Plants Using Certainty Factor and Forward Chaining Methods

Use of an Expert System to Diagnose and Provide Solutions fo...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Mahesa Bayu Baihaqi Aris Puji Widodo Ferry Jie Master of Information System Study Program Diponegoro University Central Java Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Central Java Indonesia School of Business and Law Edith Cowan University Australia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Mustard greens are a vegetable that is very popular with people, especially hydroponic mustard greens, which appear fresher. However, like other vegetables, mustard greens grown hydroponically are also susceptible to pests and diseases, which can cause the vegetables to lose their freshness and may even result in crop failure. This condition can cause losses for mustard greens farmers in running their businesses. This research proposes an information system that represents the expertise of hydroponic mustard greens farmers in diagnosing pests and diseases in mustard plants as well as solutions to overcome them. This is to make it easier for novice growers and anyone interested in learning about diseases or pests that impact hydroponic mustard greens. The expert system developed is web-based and functions as a user consultation medium in relation to mustard pests and diseases using the forward chaining and certainy factor method to explain the decision tree procedure for mustard plant disease symptoms. With this expert system, farmers do not need to look for people who can explain the problems they face. We found that there were 10 types of pests and diseases and 29 symptoms in the hydroponic mustard plants reviewed in this research.

关键词： Plant diseases Plants (biology) Hydroponics Expert systems Information technology Faces Diseases Business Information systems Farming

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Ade Cahyo Priyo Utomo Christy Atika Sari Eko Hari Rachmawanto Heru Pramono Hadi Salama A. Mostafa Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%.

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Analysis on vocational high school teacher competency gaps: Implication for VHS teacher training needs 2

Analysis on vocational high school teacher competency gaps: ...

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2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Wahyuni, D.S. Agustini, K. Sindu, I.G.P. Sugihartini, N. Informatics Engineering Education Study Program Universitas Pendidikan Ganesha81116 Indonesia

This study proposed a novel approach that integrates qualitative and quantitative methods to identify the competency gaps of VHS teachers in Vocational High Schools (VHSs). It comprised four research steps, namely (1) determining the ideal teacher competencies through Fuzzy Delphi technique, (2) arranging the competency priorities by using the analytic hierarchy process, (3) conducting VHS teacher performance evaluation by using a 360-degree rater, and (4) the competency gaps analysis using the IPA matrix. The sample members involved in this study were 7 experts from the industry, academia, and government, and 50 VHS teachers from several regencies in Bali Province, Indonesia. This study suggested that the gaps analysis between the existing VHS teacher competencies and the ideal ones include (1) the pedagogical competency - the low level of factual knowledge mastery in vocational knowledge and skills;(2) mastery in the application of contents;(3) mastery of content knowledge related to vocational teaching subjects;(4) networking and collaboration between the relevant industry and government;(5) continuous professional development;and (6) entrepreneurship. These findings imply that these needs of competencies should be the focus of teacher training programs for VHS teachers. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Personnel training

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern

Online Retail Pattern Quality Improvement: From Frequent Seq...

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

作者： Ridowati Gunawan Informatics Study Program Sanata Dharma University Yogyakarta Indonesia

ISBN: (纸本)9781665401524

There has been a change in people's shopping behavior, especially during the Covid-19 pandemic, from what traditionally requires direct face-to-face meetings between sellers and buyers, to virtual face-to-face through various shopping media. All activities carried out by customers, click streams performed, items purchased, the number of items including the price will be recorded in a log. Activity records in the log are very useful to be able to find out the pattern of activity sequences from customers, especially the order of items purchased by customers. However, the management certainly needs more knowledge, not just the order of goods that are often purchased by customers. Does the order of items purchased also provide maximum profit? There have been many methods to get frequent sequential patterns from customer activities, but getting a pattern that chooses more quality by adding utility value needs to be considered. In this research, the method used to obtain frequent sequential patterns is using PrefixSpan (Prefix-projected Sequential PAtterN) and the method used to obtain a high-utility sequential pattern is the USpan (Utility Sequential PAtterN) method. USpan is applied to the BMS (Blue Martini DataSet) dataset, which is the dataset used in KDD (Knowledge Discovery in Databases) CUP 2000 which consists of clickstream data from an e-commerce. The experimental results show that the frequent sequential pattern will always appear in the high-utility sequential pattern but not vice versa. It is certain that a high-utility pattern must be sequential, but a sequential pattern is not necessarily a high-utility sequential. From the results of the high-utility sequential pattern, it can be used as input to provide recommendations to customers to carry out the shopping process on items that can provide greater profits. The conclusion of the research conducted is that the high-utility sequential pattern mining can produce a higher quality pattern than just getting a fr

关键词： Seminars COVID-19 Pandemics Databases Media Knowledge discovery Electronic commerce

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Scholarship Recipients Prediction Model using k-Nearest Neighbor Algorithm and Synthetic Minority Over-sampling Technique

Scholarship Recipients Prediction Model using k-Nearest Neig...

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IEEE International Conference on System Engineering and Technology

作者： Dede Kurniadi Fitri Nuraeni Nia Abania Leni Fitriani Asri Mulyani Yoga Handoko Agustin Department of Computer Science Institut Teknologi Garut Garut Indonesia Study Program of Informatics Institut Teknologi Garut Garut Indonesia

ISBN: (纸本)9798350319842

This research aims to develop a prediction model for scholarship recipients using the k-Nearest Neighbor (KNN) and Synthetic Minority Over Sampling Technique (SMOTE) algorithms. The method in model development uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which consists of phases of business understanding, data understanding, data processing, modeling, evaluation, and deployment. The dataset used is the Indonesian Smart College Card (KIP-K) Scholarship data in the real world obtained at one of the universities in Indonesia. The evaluation of the model uses a confusion matrix to calculate the values of accuracy, recall, precision, F1-Score, and AUC. The research results show seven attributes that influence the selection process for prospective scholarship recipients, namely non-academic achievement scores, selection test scores, survey scores, interview scores, economic condition scores, academic achievement, and choice of study programs. While the results of model testing produce the best model performance at a value of k=3 with 90.12% accuracy, 85.57% precision, 96.51% recall, 90.71% F1-score, and 0.957 AUC, which are included in the Excellent Classification category. Based on the test results, the model can be applied to a prediction system for prospective scholarship recipients at higher education institutions to make it more targeted.

关键词： Economics Scholarships Education Predictive models Prediction algorithms Systems engineering and theory Data models

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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