Article Per Year (5 Year)
p-Index From 2021 - 2026
1.829
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics ELINVO (Electronics, Informatics, and Vocational Education) JURNAL PENGABDIAN KEPADA MASYARAKAT Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Mantik Business Review and Case Studies EXPLORER Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Humasak T. A. Simanjuntak
Institut Teknologi Del
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Spatial Semantic Analysis and Origin-Destination Prediction Based on Extensive GPS Trajectory in Jakarta Humasak Simanjuntak; Agnes Hutauruk; Haryati Situmorang; Yoshua Silitonga
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5388

Abstract

The rapid growth of mobility data from GPS trajectories offers unprecedented opportunities to gain deep insights into human mobility behavior, with significant implications for urban planning, traffic management, public transportation optimization, emergency response, and smart city development. However, a key challenge lies in transforming raw GPS trajectory data, consisting of sequences of coordinates and timestamps, into meaningful, context-rich information that can support analysis and decision making. This study proposes a semi-supervised framework to enhance the contextual and semantic understanding of journeys, using Grab Jakarta GPS trajectory data as a case study. The framework involves extracting origin-destination pairs, augmenting the data with temporal (day, time) and spatial (postal code, land use) contexts through public datasets, assigning cluster labels to characterize groups of journeys, analyzing mobility patterns, and ultimately predicting trip destinations. Origin-destination clustering, performed using the DBSCAN algorithm, identified five meaningful clusters, achieving the highest silhouette score of 0.56 with epsilon = 7.0 and min_samples = 5. Subsequently, a regression-based prediction model was developed, employing nine algorithms, including three deep learning approaches. The LSTM model demonstrated the best performance, yielding a mean squared error of 0.0053 and a coefficient of determination (R²) of 86.20% in predicting trip destinations. These findings highlight the potential of integrating spatial-temporal enrichment and machine learning to derive actionable insights from GPS trajectory data.
The Multimodal Medical Clustering of Chest X-Ray Images and Clinical Reports Using Deep Visual and Semantic Text Features Humasak Simanjuntak; Lenni Hutapea; Jonathan Simorangkir; Yolanda Saragih; Jaden Panggabean
EXPLORER Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2915

Abstract

The limited availability of labelled medical data is a real obstacle in building a supervised learning-based Radiology analysis system. This study offers an alternative approach, unlabeled multimodal clustering, that simultaneously combines clinical information sources: chest X-ray images and physician clinical reports. Visual features are extracted from 1,000 images using a pretrained ResNet152, and text features are constructed using a Dual TF-IDF Vectorizer that processed the findings and conclusions columns separately with explicit weightings (0.4 and 0.6), then enriched with 10 related topics from the Latent Dirichlet Allocation (LDA) model. These two modalities are combined through a weighted late fusion strategy after being normalized by L2, with a visual weight of 0.5 and a text weight of 0.5, respectively. Before clustering, dimensionality reduction is performed using UMAP with 50 components and a cosine metric. Determining the optimal K value using the Elbow and Silhouette Score methods in the range of K=2 to K=15 showed K=8 as the best choice with a Silhouette Score value of 0.7524. The clustering results reveal eight clinical groups reflecting diverse diagnostic patterns, ranging from normal to abnormal findings, such as cardiomegaly and pneumonia, that require clinical attention. This approach has the potential to serve as the basis for a medical data exploration system that relies on no label annotations.
Co-Authors Adesty Saragih Agnes Hutauruk Arie Satia Dharma Arlinta Christy Barus Arlinta Christy Barus Banjarnahor, Wiwin Barus, Arlinta Claudia, Marwani Erisha Sitorus Febriani Gultom Gonzales, Helen Grace B. Goretti Situmorang Grase Panjaitan Hansel Septiyan Pasaribu Haryati Situmorang Jaden Panggabean Jonathan Simorangkir Lenni Hutapea Lowiska Simanjuntak Lubis, Anju Ucok Lumbanraja, Amelia Magdalena Panjaitan Manurung, Joshua Koko Sarasi Manurung, Ruth Christine Margaretha, Patricia Dian Monica Butarbutar Nancy Panjaitan Nicholas Hutabarat Noramti Mardianti Manurung Panjaitan, Magdalena Panjaitan, Sintong Purba, Susi Rajagukguk, Nico Regita Ricton Samuel Sitorus Robert Aritonang Rosni Lumbantoruan Samosir, Gabriel Samuel Indra Gunawan Situmeang Siahaan, Lamsihar Silaban, Prince Ephraim Prabowo Sintong Panjaitan Sitorus, Erisha Sormin, Venny Handayani Wesly Mailander Siagian Wiwin Sry Adinda Banjarnahor Yolanda Saragih Yoshua Silitonga