Prediction of heart disease using random forest algorithm, support vector machine, and neural network Didik Setiyadi, Henderi Henderi, Anrie Suryaningrat, Rulin Swastika, Saludin Saludin, Muhamad Malik Mutoffar, Imam Yunianto Telkomnika Telecommunication Computing Electronics and Control, 2025 The heart is a vital organ responsible for pumping blood throughout the human body. Machine learning has become an increasingly important tool in medical forecasting, improving diagnostic accuracy and reducing human errors. This study focuses on detecting heart disease using machine learning algorithms. It aims to compare the performance of three key algorithms random forest (RF), support vector machine (SVM), and neural networks (NN), in predicting heart disease. Using a patient dataset with both nominal and numeric attributes, record mining techniques were applied through Orange software. The target classes indicated the absence (0) or presence (1) of heart disorders. The evaluation was based on the prediction accuracy of each algorithm. Results show that SVM achieved the highest accuracy, with a rate of 85%, outperforming RF and NN. The findings suggest that the SVM algorithm is a reliable tool for heart disease prediction, helping reduce diagnostic errors and improve medical decision-making.
Performance Comparison of Naive Bayes and Logistic Regression for Sentiment Analysis of YouTube Comments on Indonesia's Education System Asro, Agus Sulaiman, Henderi, Sudaryono International Conference on Awareness Science and Technology Icast, 2025 This study evaluates the performance of Naive Bayes and Logistic Regression algorithms in sentiment analysis of YouTube comments on Indonesia’s education system. Using the CRISP-DM framework, the research applies preprocessing, TF-IDF feature extraction, and integrates Grid Search with the Synthetic Minority Oversampling Technique (SMOTE) to optimize classification results. A dataset of 8,730 comments collected between January and June 2025 was analyzed. Logistic Regression, enhanced with Grid Search and SMOTE, achieved the highest accuracy of 93.02%, while Naive Bayes reached 89.49%. The sentiment distribution shows a dominance of negative opinions (56.5%), followed by positive (30%) and neutral (13.5%). These results confirm the advantage of combining resampling and parameter tuning in improving sentiment classification performance. The findings provide valuable insights for policymakers, demonstrating how social media data can serve as an evaluative tool for education policy. Future work may expand to other platforms and employ advanced models such as deep learning for broader analysis.
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi Henderi Journal of Applied Data Sciences, 2025 This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.
Artificial Intelligence Analysis of Cultural Narratives Shaping Emotional Responses to Infertility Ade Kemala Jaya, Usman Ependi, Antonius Ary Setyawan, Aman Jaiswal, Henderi, Galih Putra Cesna, Ridwan Kurniaji Proceeding 2025 4th International Conference on Creative Communication and Innovative Technology Empowering Transformative Mature Leadership Harnessing Technological Advancement for Global Sustainability Iccit 2025, 2025 Infertility is a global health concern often linked to emotional distress shaped by cultural narratives. While medical aspects are well studied, the cultural emotional dimension remains less explored, especially using computational methods. This study examines how cultural norms influence emotional responses to infertility. AI-driven Natural Language Processing (NLP) techniques, such as sentiment analysis, thematic coding, and topic modelling, were applied to qualitative data from interviews, social media, and cultural texts across Southeast Asia, Sub-Saharan Africa, Western Europe, and North America. Findings show clear cultural patterns: collectivist societies report higher shame and stigma, while individualist cultures exhibit greater resilience but more isolation. Religious contexts often rely on spiritual guidance, whereas medicalized societies prioritize fertility treatments. These results underscore the strong influence of cultural norms on emotional expression, social support, and healthcare access. By combining computational analysis with sociocultural perspectives, this study offers actionable insights for designing culturally sensitive interventions. The approach demonstrates the value of integrating AI and cultural analysis in addressing infertility’s emotional and social impacts.
Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments Deshinta Arrova Dewi Journal of Applied Data Sciences, 2024 The prevalence of streaming data across various sectors poses significant challenges for real-time anomaly detection due to its volume, velocity, and variability. Traditional data processing methods often need to be improved for such dynamic environments, necessitating robust, scalable, and efficient real-time analysis systems. This study compares two advanced machine learning approaches—LSTM autoencoders and Matrix Profile algorithms—to identify the most effective method for anomaly detection in streaming environments using the NYC taxi dataset. Existing literature on anomaly detection in streaming data highlights various methodologies, including statistical tests, window-based techniques, and machine learning models. Traditional methods like the Generalized ESD test have been adapted for streaming data but often require a full historical dataset to function effectively. In contrast, machine learning approaches, particularly those using LSTM networks, are noted for their ability to learn complex patterns and dependencies, offering promising results in real-time applications. In a comparative analysis, LSTM autoencoders significantly outperformed other methods, achieving an F1-score of 0.22 for anomaly detection, notably higher than other techniques. This model demonstrated superior capability in capturing temporal dependencies and complex data patterns, making it highly effective for the dynamic and varied data in the NYC taxi dataset. The LSTM autoencoder's advanced pattern recognition and anomaly detection capabilities confirm its suitability for complex, high-velocity streaming data environments. Future research should explore the integration of LSTM autoencoders with other machine-learning techniques to enhance further the accuracy, scalability, and efficiency of anomaly detection systems. This study advances our understanding of scalable machine-learning approaches and underscores the critical importance of selecting appropriate models based on the specific characteristics and challenges of the data involved.
An Extensive Exploration into the Multifaceted Sentiments Expressed by Users of the myIM3 Mobile Application, Unveiling Complex Emotional Landscapes and Insights B Herawan Hayadi Journal of Applied Data Sciences, 2024 This study investigates user sentiment towards the myIM3 application, an application used for telecommunication service management in Indonesia. Using text analysis and machine learning methods, we analyzed user reviews to identify dominant sentiment patterns and evaluate different classification models. Word cloud analysis, sentiment distribution, and donut plots were utilized to gain deeper insights into user preferences and issues. Results indicate that the majority of user reviews are neutral (52.2%), with 37% positive reviews and 33.4% negative reviews. Users consistently pay attention to aspects such as internet connection (Neutral: 92%, Positive: 95%, Negative: 87%) and pricing (Neutral: 92%, Positive: 92%, Negative: 93%) in their reviews. Evaluation of classification models like Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest shows that the SVM model performs the best with an accuracy of 93%, high precision (Negative: 93%, Neutral: 92%, Positive: 95%), recall (Negative: 93%, Neutral: 95%, Positive: 91%), and F1-score (Negative: 93%, Neutral: 94%, Positive: 93%). These findings can serve as a basis for service improvement and better product development in the future, while also affirming the capability of text analysis and machine learning techniques in providing valuable insights for telecommunication service providers.
Performance Comparison of Naive Bayes and Logistic Regression for Sentiment Analysis of YouTube Comments on Indonesia’s Education System Asro, A Sulaiman, Henderi, Sudaryono 2025 13th International Conference on Awareness Science and Technology (iCAST) , 2025 2025
Segmentation and Profiling of Electric Vehicle Market Using Clustering Analysis: A Case Study with Implications for Digital Marketing in the EV Sector Henderi, AU Zailani, NM Tuah, A Abas Journal of Digital Market and Digital Currency 2 (3), 323-342 , 2025 2025 Citations: 1
Comparative Study of Traditional and Modern Models in Time Series Forecasting for Inflation Prediction Henderi, S Sofiana International Journal for Applied Information Managemen 5 (3), 155-167 , 2025 2025 Citations: 5
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach TB Kurniawan, DA Dewi, Henderi, MS Hasibuan, MZ Zakaria, AAB Ismail Journal of Applied Data Sciences 6 (3), 1756-1768 , 2025 2025 Citations: 1
Incorporate Transformer-Based Models for Anomaly Detection DA Dewi, HKR Singh, J Periasamy, TB Kurniawan, Henderi, ... Journal of Applied Data Sciences 6 (3), 2046-2055 , 2025 2025 Citations: 4
Peran routing protokol dalam meningkatkan kinerja cloud computing M Saputra, D Jonathan, Henderi Penerbit Underline , 2025 2025
Unlock big data & machine learning : menggunakan 14 algoritma unggulan untuk bisnis dan industri 5.0 A Martono, Henderi, Padeli CV Bintang Semesta Media , 2025 2025
Business Intelligence: Data Analysis, Data Warehouse, Data Mining, Dashboard E Rahwanto, Henderi, U Rahardja, Hamdani Lingkas Edukasi Indonesia , 2025 2025
Model Sistem Pendukung Keputusan Dosen Berprestasi di Bidang Tri Dharma Menggunakan Metode Simple Attribute Rating Technique RF Nugraha, Henderi, Sudaryono ICIT (Innovative Creative and Information Technology) Journal 11 (1), 105-121 , 2025 2025
Prediction of heart disease using random forest algorithm, support vector machine and neural network D Setiyadi, Henderi, A Suryaningrat, R Swastika, Saludin, MM Mutoffar, ... TELKOMNIKA Telecommunication Computing Electronics and Control 23 (1), 129-137 , 2025 2025 Citations: 9
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi, Asro, A Sulaiman, TB Kurniawan, DA Dewi, M AlQudah Journal of Applied Data Sciences 6 (1), 189-200 , 2025 2025 Citations: 15
Kemajuan Ekonomi Digital dan Perannya dalam Membentuk Dinamika Perdagangan Internasional Modern H Henderi, KI Mustofa, N Lutfiani, AN Savitri ADI Bisnis Digital Interdisiplin Jurnal 5 (2), 17-24 , 2024 2024 Citations: 5
The Role of RegTech in Automating Compliance and Risk Management MD Firiza, Henderi, N Lutfiani, ARA Zahra, U Rahardja 2024 12th International Conference on Cyber and IT Service Management (CITSM) , 2024 2024 Citations: 19
The Role of User Behavior Patterns in Enhancing Fraud Detection in Online Banking: A Bibliometric Analysis P Silvia, Q Aini, EA Nabila, Henderi, H Nusantoro 2024 2nd International Conference on Technology Innovation and Its … , 2024 2024 Citations: 4
Optimalisasi Database 3.0 untuk Verifikasi Data Pelatihan Pelaut RF Nugraha, Henderi, Sudaryono JTERA (Jurnal Teknologi Rekayasa) 9 (2), 101-112 , 2024 2024
Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments DA Dewi, HKR Singh, J Periasamy, TB Kurniawan, Henderi, ... Journal of Applied Data Sciences 5 (4), 1949-1962 , 2024 2024 Citations: 3
Advanced Anomaly Detection in ECG Signals Through Convolutional Autoencoders H Henderi, M Misinem, H Hamdani, MZ Zakaria, SB Kasim Indonesian Journal of E-learning and Multimedia (IJOEM) 3 (3), 114-125 , 2024 2024 Citations: 1
Sistem Informasi Pelayanan Pengaduan Satuan Ploisi Pamong Praja Berbasis Webiste M Jahiri, H Henderi, ABB Ladjamudin Jurnal Informasi dan Komputer 12 (02), 56-63 , 2024 2024 Citations: 21
Optimization of Davies-Bouldin Index with k-medoids algorithm H Henderi, L Fitriana, I Iskandar, R Astuti, MI Arifandy, BH Hayadi, ... AIP Conference Proceedings 3065 (1), 030002 , 2024 2024 Citations: 9
Combination of particle swarm optimization and back-propagation algorithm H Henderi, R Ramli, EP Cynthia, S Sarbaini, F Muttakin, N Nazaruddin, ... AIP Conference Proceedings 3065 (1), 030003 , 2024 2024
MOST CITED SCHOLAR PUBLICATIONS
Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer Henderi, T Wahyuningsih, E Rahwanto International Journal of Informatics and Information System 4 (1), 13-20 , 2021 2021 Citations: 569
Evaluasi Penerapan SIMRS Menggunakan Metode HOT-Fit di RSUD dr. Soedirman Kebumen PD Abda’u, WW Winarno, H Henderi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi … , 2018 2018 Citations: 125
Blockchain in Indonesia University: A Design Viewboard of Digital Technology Education A Dudhat, NPL Santoso, Henderi, S Santoso, R Setiawati Aptisi Transactions on Technopreneurship (ATT) 3 (1), 68-80 , 2021 2021 Citations: 64
UML Powered Design System Using Visual Paradigm Henderi, R Untung, R Efana CV. Literasi Nusantara Abadi , 2021 2021 Citations: 58
Algorithm Automatic Full Time Equivalent, Case study of health service PA Sunarya, F Andriyani, Henderi, U Rahardja International Journal of Advanced Trends in Computer Science and Engineering … , 2019 2019 Citations: 48
Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice's Coefficient T Wahyuningsih, H Henderi, W Winarno Journal of Applied Data Sciences 2 (2) , 2021 2021 Citations: 45
A proposed gamification framework for smart attendance system using rule base Henderi, Q Aini, NPL Santoso, A Faturahman, U Rahardja Journal of Advanced Research in Dynamical and Control Systems 12 (02), 1827 … , 2020 2020 Citations: 44
Penerapan E-Learning Sebagai Media Pembelajaran Berbasis Aplikasi Android Menggunakan Metode Research and Development M Jahiri, IID Yusuf, Henderi Technomedia Journal (TMJ) 8 (2), 261-275 , 2023 2023 Citations: 43
Evaluation of maturity level of the electronic based government system in the department of industry and commerce of Banjar Regency MRY Saputra, WW Winarno, H Henderi, S Shaddiq Journal of Robotics and Control (JRC) 1 (5), 156-161 , 2020 2020 Citations: 43
Decision support system untuk penilaian kinerja guru dengan metode profile matching A Suhartanto, K Kusrini, H Henderi Jurnal Komputer Terapan 2 (2), 149-158 , 2016 2016 Citations: 39
Perencanaan strategis sistem informasi untuk meningkatkan keunggulan kompetitif sekolah islam terpadu IS Widiati, E Utami, H Henderi Creative Information Technology Journal 2 (4), 329-340 , 2015 2015 Citations: 34
Design and Development of Interactive Media in Vocational High Schools Using the MultimediaDevelopment Life Cycle Method Based on Android IID Yusuf, M Jahiri, Henderi, ABB Ladjamudin JINAV: Journal of Information and Visualization 5 (1), 134-145 , 2024 2024 Citations: 31
Analisis dan perancangan sistem informasi kepegawaian menggunakan unified modeling language (UML) DE Profesi, Henderi Jurnal Sistem Informasi dan Teknologi Informasi 7 (1), 22-33 , 2018 2018 Citations: 30
Dengue classification method using support vector machines and cross-validation techniques H Hamdani, HR Hatta, N Puspitasari, A Septiarini, H Henderi IAES International Journal of Artificial Intelligence (IJ-AI) 11 (3) , 2022 2022 Citations: 29
Rancangan sistem informasi pendaftaran siswa baru berbasis web pada SMK Putra Rifara A Maghfiroh, Henderi, G Maulani Jurnal Ilmiah MATRIK 22 (1), 1-7 , 2020 2020 Citations: 28
Desain aplikasi e-learning sebagai media pembelajaran artificial informatics Henderi, Maimunah, R Andrian Journal CCIT 4 , 2011 2011 Citations: 27
Perancangan Sistem E-Ticket Pelaporan Incident Berbasis Web Pada PT. AEROFOOD Indonesia I Muntasir, G Pramono, E Nurninawati, S Santoso, Henderi JATI (Jurnal Mahasiswa Teknik Informatika) 7 (2), 1070-1075 , 2023 2023 Citations: 24
Blockchain Family Deed Certificate for Privacy and Data Security PA Sunarya, Henderi, Sulistiawati, A Khoirunisa, P Nursaputri 2020 Fifth International Conference on Informatics and Computing (ICIC) 2020 , 2020 2020 Citations: 24
Penerapan algoritma K-Nearest Neighbour dalam menentukan pembinaan Koperasi Kabupaten Kotawaringin Timur YA Setianto, K Kusrini, H Henderi Creative Information Technology Journal 5 (3), 232-241 , 2019 2019 Citations: 24
Metode Fuzzy dan AHP Dalam Penerapan Sistem Pendukung Keputusan N Norhikmah, R Rumini, H Henderi SEMNASTEKNOMEDIA ONLINE 1 (1), 09-31 , 2013 2013 Citations: 23
