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Naive Bayes for Thesis Labeling Nurhayati, Fitria; Khusna, Arfiani Nur; Saputra, Dimas Chaerul Ekty
Mobile and Forensics Vol 3, No 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v3i1.3763

The thesis preparation in the Department of Informatics Universitas Ahmad Dahlan is divided into two areas of interest, namely Intelligent Systems and Software and Data Engineering. Existing thesis title data is only used as an archive and has never been processed or classified to determine the trend of thesis topics based on student interest each year. The stages include data collection, the data is divided into two parts (training data and test data), manual labeling of training data, text preprocessing, and classification using Naive Bayes. The results show the trend of thesis title taking from 2013 to 2018 shows the thesis trend in the field of Intelligent Systems and Software. Accuracy testing uses Confusion Matrix and K-Fold Cross Validation with a k value is 10, has a value of 94.60%, precision of 97.30%, and a recall of 85.70%.

Naive Bayes for Thesis Labeling Nurhayati, Fitria; Khusna, Arfiani Nur; Saputra, Dimas Chaerul Ekty
Mobile and Forensics Vol. 3 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v3i1.3763

The thesis preparation in the Department of Informatics Universitas Ahmad Dahlan is divided into two areas of interest, namely Intelligent Systems and Software and Data Engineering. Existing thesis title data is only used as an archive and has never been processed or classified to determine the trend of thesis topics based on student interest each year. The stages include data collection, the data is divided into two parts (training data and test data), manual labeling of training data, text preprocessing, and classification using Naive Bayes. The results show the trend of thesis title taking from 2013 to 2018 shows the thesis trend in the field of Intelligent Systems and Software. Accuracy testing uses Confusion Matrix and K-Fold Cross Validation with a k value is 10, has a value of 94.60%, a precision of 97.30%, and a recall of 85.70%.

Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification Autism Spectrum Disorder Dimas Chaerul Ekty Saputra; Yusuf Maulana; Thinzar Aung Win; Raksmey Phann; Wahyu Caesarendra
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26094

Autism spectrum disorder (ASD) is a developmental disability resulting from neurological disparities. People with ASD frequently struggle with communication and social interaction, as well as limited or repetitive interests or behaviors. People with ASD may also have unique learning, movement, and attention styles. ASD sufferers can be interpreted as 1 in every 100 individuals in the globe having ASD. Abilities and requirements of autistic individuals vary and may change over time. Some autistic individuals are able to live independently, while others have severe disabilities and require lifelong care and support. Autism frequently interferes with educational and employment opportunities. Additionally, the demands placed on families providing care and assistance can be substantial. Important determinants of the quality of life for persons with autism are the attitudes of the community and the level of support provided by local and national authorities. Autism is frequently not diagnosed until adolescence, despite the fact that autistic traits are detectable in early infancy. This study will discuss the identification of Autism Spectrum Disorders using Magnetic Resonance Imaging (MRI). MRI images of ASD patients and MRI images of patients without ASD were compared. By employing multiple machine learning and deep learning techniques, such as random forests, support vector machines, and convolutional neural networks, the random forest method achieves the utmost accuracy with 100% using confusion matrix. Therefore, this technique is able to optimally identify ASD through MRI.

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification Saputra, Dimas Chaerul Ekty; Ma'arif, Alfian; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20898

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.

Implementation of Named Entity Recognition with a Developing Question Answering System: A Case Study in the Merapi Volcano Museum Khusna, Arfiani Nur; Putri, Okhy Kharisma; Saputra, Dimas Chaerul Ekty
Journal of Intelligent Computing & Health Informatics Vol 3, No 1 (2022): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i1.9205

Merapi volcano museum is a place to get some information about active mountain activities, the general public can access the website page at mgm.slemankab.go.id. Indeed, visitors are given easy access, but the information provided by the website is not fully complete, causing visitors to feel dissatisfied. Based on the results of a questionnaire from 40 respondents, it was found that 50.55% of website visitors did not get the information they wanted. Therefore, in this research, we built a Question Answering System (QAS) using the Named Entity Recognition (NER) method that has been implemented into Telegram. To improve the performance of the QAS system, testing and analysis has been carried out with a "white box" approach. The results show that the QAS system has 3 regions and 3 independent paths, with path 1 being 1-2-3-4-11, path 2 being 1-2-3-4-5-6-7-8-11, and path 3 being 1-2-3-4-5-6-7-9-10-11. Based on the results of this study, all three paths can produce the correct answer.

A Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia Sasmita, Novi Reandy; Khairul, Moh; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Dahlawy, Arriz; Apriliansyah, Feby; Muliadi, Muliadi; Saputra, Dimas Chaerul Ekty; Noviandy, Teuku Rizky; Watsiq Maula, Ahmad
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.130

The clustering, one of statistical analysis, can be used for understanding population patterns and as a basis for more targeted policy making. In this ecological study, we explored the population dynamics across 23 districts/cities in Aceh Province. The study used the Aceh Population Development Profile Year 2022 data, focusing on the total population, in-migrants, out-migrants, fertility, and maternal mortality as variables. The study employed descriptive statistics to ascertain the data distribution, followed by the Shapiro-Wilk test to evaluate normality, which is crucial for selecting the appropriate statistical methods. The Spearman test was used to determine correlations between the total population and the variable as indicators. Probabilistic Fuzzy C-Means (PFCM) method is used for clustering. To optimize clustering, the silhouette coefficient was calculated using the Euclidean Distance and the elbow method, with the results analyzed using R-4.3.2 software. This study's design and methods aim to provide a nuanced understanding of demographic patterns for targeted policy-making and regional development in Aceh, Indonesia. Based on the data normality test results, only fertility (p-value = 0.45), while the other variables are not normally distributed. Spearman test was used, and the results showed that only in-migrants (p-value = 1.78 x 10-6) and out-migrants (p-value = 2.30 x 10-6) correlated to the Aceh Province population. Using the population variable and the two variables associated with it, it was found that 4 is the best optimum number of clusters, where clusters 1, 2, 3, and 4 consist of three districts/city, nine districts/city, four districts/city and seven districts/city respectively.

Revolutionizing Anemia Classification with Multilayer Extremely Randomized Tree Learning Machine for Unprecedented Accuracy Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Futri, Irianna; Win, Thinzar Aung; Sunat, Khamron; Ratnaningsih, Tri
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1379

Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.

HNIHA: Hybrid Nature-Inspired Imbalance Handling Algorithm to Addressing Imbalanced Datasets for Improved Classification: In Case of Anemia Identification Saputra, Dimas Chaerul Ekty; Ratnaningsih, Tri; Futri, Irianna; Muryadi, Elvaro Islami; Phann, Raksmey; Tun, Su Sandi Hla; Caibigan, Ritchie Natuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11306

This study presents a comprehensive evaluation of three ensemble models designed to handle imbalanced datasets. Each model incorporates the hybrid nature-inspired imbalance handling algorithm (HNIHA) with matthews correlation coefficient and synthetic minority oversampling technique in conjunction with different base classifiers: support vector machine, random forest, and LightGBM. Our focus is to address the challenges posed by imbalanced datasets, emphasizing the balance between sensitivity and specificity. The HNIHA algorithm-guided support vector machine ensemble demonstrated superior performance, achieving an impressive matthews correlation coefficient of 0.8739, showcasing its robustness in balancing true positives and true negatives. The f1-score, precision, and recall metrics further validated its accuracy, precision, and sensitivity, attaining values of 0.9767, 0.9545, and 1.0, respectively. The ensemble demonstrated its ability to minimize prediction errors by minimizing the mean squared error and root mean squared error to 0.0384 and 0.1961, respectively. The HNIHA-guided random forest ensemble and HNIHA-guided LightGBM ensemble also exhibited strong performances.

Bibliometric Analysis of Explainable AI in Advance Care Planning: Insights, Collaborative Trends, and Future Prospects Futri, Irianna; Muryadi, Elvaro Islami; Saputra, Dimas Chaerul Ekty
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.11641

The increasing complexity of healthcare systems has led to a growing need for Advance Care Planning (ACP) to ensure personalized care for patients. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance ACP by providing transparent and interpretable decision-making processes. However, the current landscape of XAI in ACP remains unclear, necessitating a comprehensive bibliometric analysis. This study employed a systematic review of existing literature on XAI in ACP, using a bibliometric approach to analyze publication trends, collaboration patterns, and research themes. One hundred sixty articles were selected from prominent databases, and their metadata were extracted and analyzed using Biblioshiny, the analysis revealed a significant growth in ACP XAI-related publications, focusing on deep learning and natural language processing techniques. The top contributing authors and institutions were identified, and their collaborative networks were visualized. The results also highlighted the prominent themes of patient-centered care, decision support systems, and healthcare analytics. The study's findings have implications for developing more effective XAI-based ACP systems. This bibliometric analysis provides valuable insights into the current state of XAI in ACP, highlighting the need for further research and collaboration to address the complex challenges in healthcare. The study's outcomes can inform policymakers, researchers, and practitioners in developing more effective ACP systems that leverage the potential of XAI.

An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Phann, Raksmey; Futri, Irianna; Lismawati, Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.

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