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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Vygotsky: Jurnal Pendidikan Matematika dan Matematika Jurnal Aplikasi Sains Data
Prismahardi Aji Riyantoko
Okayama University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics

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Comparative Analysis of Hierarchical Clustering and K-Medoids for Clustering Cases of Childhood Respiratory Diseases in Lamongan Regency Adelia Yuandhika; Nezalfa Sabrina; Cahya Eka Melati; Dwi Arman Prasetya; Prismahardi Aji Riyantoko
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.37

Abstract

Abstract— Respiratory diseases affecting children remain a significant health issue in Indonesia, including in Lamongan Regency. The region faces challenges related to pediatric respiratory illnesses, particularly Childhood Tuberculosis, Pneumonia in toddlers, and Cough in toddlers, which impact children's quality of life and development. Therefore, understanding the spatial distribution and correlation patterns among these diseases is essential to support more targeted health intervention planning. This study analyzes the distribution patterns of pediatric respiratory diseases in Lamongan Regency and clusters regions based on similarities in the number of cases using an unsupervised learning approach. The method employed is Hierarchical Clustering with four distance calculation techniques: single, complete, average, and ward linkage and K-Medoids with two distance calculation techniques: euclidean and manhattan distance. The data, sourced from the Lamongan District Health Office, include four numerical variables related to respiratory diseases, aggregated by sub-districts. Data normalization was carried out using standardization, and cluster quality was evaluated using three internal metrics: Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The analysis results indicate that the optimal number of clusters is three. Among all methods tested, the Hierarchical Clustering with ward linkage method yielded the best performance, with a Silhouette Score of 0.5447, a DBI of 0.5884, and a CHI of 20.3018. These results demonstrate that the ward linkage method is the most effective in clustering regions based on the characteristics of pediatric respiratory disease cases and can be used for mapping priority health intervention areas in Lamongan Regency.
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) I Gede Susrama Mas Diyasa; Hajjar Ayu Cahyani Kuswardhani; Mohammad Idhom; Prismahardi Aji Riyantoko; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.
Portfolio Optimization across Various Types of Variance-Covariance Matrices Using The Sequential Quadratic Programming (SQP) Lutfi Mardianto; Mira Mustika; Eristia Arfi; Ramanda Ditra Kristian; Nayla Nurul Aisyah; Prismahardi Aji Riyantoko
Vygotsky: Jurnal Pendidikan Matematika dan Matematika Vol. 8 No. 2 (2026): Vygotsky: Jurnal Pendidikan Matematika dan Matematika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/voj.v8i2.1427

Abstract

This study analyzes stocks provided in LQ45 index and compares the performance of ten stocks with highest expected returns between Danantara and non-Danantara groups. The data are used to estimate parameters for optimization. The Markowitz model is employed to construct portfolios based on risk and expected return, requiring the derivation of a variance-covariance matrix. This matrix is compared with those generated by the Single Index Model (SIM) and Constant Correlation Model to evaluate differences among approaches. The nonlinear optimization problem is solved using Sequential Quadratic Programming (SQP). At an expected return of 0.2597%, all matrices allocate weights to a single stock, while setting it to zero changes portfolio proportions without altering the Sharpe ratio.
Application of Convolutional Neural Network (CNN) for Web-Based Translation of Indonesian Text into Sign Language Diajeng Prameswari; Larasati; Muhammad Naswan Izzudin Akmal; Prismahardi Aji Riyantoko; Dwi Arman Prasetya
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v1i2.12

Abstract

Communication for the deaf and hard of hearing is often hindered by the limited number of sign language interpreters. This research aims to develop a web-based text-to-text sign language translation system using Convolutional Neural Networks (CNN) to bridge this communication gap. The system is built with the ASL Alphabet dataset containing 87,000 images from 29 classes (A-Z, SPACE, DELETE, NOTHING). The CNN model was designed with three convolutional layers and trained for 15 epochs using 80% of the data, while 20% of the data was used for testing. The user interface was developed using Streamlit for ease of use. Training results showed a training accuracy of 98.96% and a validation accuracy of 98.61% at the 15th epoch. Model evaluation yielded an overall accuracy of 98%, with high precision, recall, and F1-score values for most classes. This research demonstrates the significant potential of CNN in developing automatic sign language translators, which is expected to improve information accessibility and inclusivity for the deaf community.
Co-Authors Adelia Yuandhika Cahya Eka Melati Deshinta Arrova Dewi Diajeng Prameswari Dwi Arman Prasetya Dwi Arman Prasetya Eristia Arfi Hajjar Ayu Cahyani Kuswardhani I Gede Susrama Mas Diyasa Larasati Mardianto, Lutfi Mira Mustika Mohammad Idhom Muhammad Naswan Izzudin Akmal Nayla Nurul Aisyah Nezalfa Sabrina Ramanda Ditra Kristian