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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Infotech Journal Jurnal Inspiration Info-Teknik KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization ILKOM Jurnal Ilmiah Jurnal Penelitian Enjiniring Fakultas Teknik Unhas JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Batara Wisnu : Indonesian Journal of Community Services Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul)
Nurul Fathanah Mustamin
Information Technology, Lambung Mangkurat University, Banjarmasin
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Ensemble Techniques Based Risk Classification for Maternal Health During Pregnancy Mustamin, Nurul Fathanah; Buang, Ariyani; Aziz, Firman; Nur, Nur Hamdani
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2005.190-197

Abstract

This research focuses on the critical aspect of maternal health during pregnancy, emphasizing the need for early detection and intervention to address potential risks to both mothers and infants. Leveraging various classification methods, including Naïve Bayes, decision trees, and ensemble learning techniques, the study investigates the prediction of childbirth potential and pregnancy risks. The research begins with data collection, followed by preprocessing to clean and prepare the data, including handling missing values and normalization. Next, cross-validation is performed to ensure model robustness. Five ensemble techniques are used for risk classification: Ensemble Boosted Trees, which enhances the performance of decision trees; Ensemble Bagged Trees, which combines predictions from decision trees trained on different subsets of data; Ensemble Subspace Discriminant, which applies discriminant analysis on random subspaces; Ensemble Subspace KNN, which uses K-Nearest Neighbors (KNN) within random subspaces; and Ensemble RUS Boosted Trees. Key variables such as maternal age, height, Hb levels, blood pressure, and previous pregnancy history are considered in these analyses. Additionally, the study introduces Ensemble Learning based on Classification Trees, revealing significant improvements in accuracy compared to cost-sensitive learning approaches. The comparison of methods, including Naïve Bayes and K-Nearest Neighbor, provides insights into their respective performances, with ensemble techniques demonstrating their potential. The proposed ensemble learning techniques, namely Ensemble Boosted Trees, Ensemble Bagging Trees, Ensemble Subspace Discriminant, Ensemble Subspace KNN, and Ensemble RUS Boosted Trees, are systematically evaluated in classifying pregnancy risks based on a comprehensive dataset of 1014 records. The results showcase Ensemble Bagging Trees as a standout performer, with an accuracy of 85.6%, indicating robust generalization and effectiveness in clinical risk assessment compared to traditional methods such as Decision Tree (61.54% accuracy), K-Nearest Neighbor (74.48%), Ensemble Learning based on Cost-Sensitive Learning (73%), Ensemble Learning based on Classification Tree (76%), Gaussian Naïve Bayes (82.6%), Multinomial Naïve Bayes (84.8%), and Bernoulli Naïve Bayes (84.8%). Ensemble Bagging Trees achieved the highest accuracy proving to be more effective than the other methods. However, the study emphasizes the need for continuous refinement and adaptation of ensemble methods, considering both accuracy and interpretability, for successful deployment in healthcare decision-making. These findings contribute valuable insights into optimizing pregnancy risk classification models, paving the way for improved maternal and infant healthcare outcomes.
DESAIN DAN PENGEMBANGAN CUSTOM DEVICE SEBAGAI SISTEM KONTROL GAME BEPAY Zulkarnain, Andry Fajar; Mustamin, Nurul Fathanah; Maulida, Muti'a
INFO-TEKNIK Vol 21, No 2 (2020): INFOTEKNIK VOL. 21 NO. 2 DESEMBER 2020
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/infotek.v21i2.10174

Abstract

BeatME is made by utilizing digital technology and network technology to make it easier for humans to channel their creativity in the field of music. With this convenience, it is expected that people who have always wanted to play music but are constrained by ability, knowledge, tools, cost, time, and location, can realize their desires and produce works. The BeatME game control system is a tool made by using Arduino as a game controller named. Based on the results of tests that have been done, it can be concluded that the device can send and receive data for communication with the Bepay game. And also equipped with a vibrating sensor and infrared to add the impression of drawing in its use. It is expected that with this test can be played by beginners and who are already accustomed to the game Bepay to become an interesting entertainment.
Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm Aziz, Firman; Mustamin, Nurul Fathanah; Rijal, Muhammad; Tanniewa, Adam M
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3079

Abstract

This study aims to enhance the performance of Human Activity Recognition (HAR) systems by implementing the Stacked Sparse Autoencoder (SSAE) algorithm combined with Support Vector Machine (SVM). The objective is to enhance the classification accuracy of human activities using sensor data. The materials for this study include a dataset collected from wearable devices equipped with accelerometers and gyroscopes. These devices generate time-series data representing a range of activities, such as walking, running, sitting, and standing. The raw data were preprocessed through normalization and segmented into fixed time windows to ensure uniformity and reliability for analysis. The methods utilized involve employing SSAE for automated feature extraction. The SSAE algorithm extracts hierarchical and abstract features from sensor data, enabling the model to learn complex patterns that traditional methods might overlook. The extracted features are then input into the SVM classifier to perform activity classification. SSAE was trained using unsupervised learning techniques, followed by supervised fine-tuning with labeled datasets. The results demonstrate that the SSAE-SVM model achieves superior performance compared to traditional SVM. The SSAE-SVM achieved 89% accuracy, 87% precision, 89% sensitivity, and 88% F1 score, significantly outperforming the traditional SVM’s 37% accuracy, 75% precision, 37% sensitivity, and 36% F1 score. These findings underscore the potential of SSAE in enhancing HAR systems by effectively extracting features from sensor data. Future research should focus on the real-time implementation of SSAE, leveraging diverse sensor modalities, and exploring its applicability in broader fields, such as predictive maintenance and personalized health monitoring.
Quality Analysis of the Digital Library Website of Universitas Lambung Mangkurat with Webqual 4.0 Method and User Experience Questionnaire (UEQ) Nurul Huda; Andreyan Rizky Baskara; Nurul Fathanah Mustamin; Yuslena Sari
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i1.464

Abstract

The current digital literature resources are widely provided by various libraries, especially those associated with higher education institutions. One such example of a digital library is the Lambung Mangkurat University Digital Library (Digilib ULM). Pre-evaluation results indicate that around 50% of the issues are related to the quality of the website, such as its appearance and functionality. Therefore, this research examines the quality of the ULM Digital Library to identify indicators that match user preferences and require improvement. The Webqual 4.0 method and User Experience Questionnaire are uti- lised for this purpose. The research is quantitative with a descriptive approach. Data is collected through a questionnaire, with 100 respondents sampled from the entire population, consisting of ULM students. Webqual 4.0 comprises three dimensions: usability, information quality, and service interaction. Meanwhile, UEQ consists of six aspects: attractiveness, dependability, efficiency, perspicuity, stimulation, and novelty. The research findings from Webqual 4.0 indicate a service interaction score of 2.75, information quality of 2.68, and usability of 2.45. The usability aspect falls into the low category or does not meet user expectations, while the other aspects fall into the moderate category. The UEQ results show an attractiveness score of 1.130, efficiency of 0.648, and novelty of 0.673, all scoring below the average compared to benchmarks, while other aspects score above average. Based on these results, it is evident that the Digilib ULM website does not meet user expectations and can be considered suboptimal, requiring improvement and enhancement.
Co-Authors Andreyan Rizky Baskara Buang, Ariyani Fajar Hanifa Nuridi Radam Fajar Zulkarnain, Andry Fathiah Nuraisyah Radam Firman Aziz Firmansyah Firmansyah Husnul Khatimi Indrabayu Indrabayu Intan Sari Areni Iphan Fitrian Radam Ishak, Pertiwi Maulida, Muti'a Muhammad Afief Ma’Ruf Muhammad Alkaff Mutia Maulida Nandang Eko Yulianto Nur Hamdani Nur Nurul Huda Rafif Taufiqurrahman Reza Karimi Rijal, Muhammad Tanniewa, Adam M Wijaya, Eka Setya Yuslena Sari, Yuslena