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Analysis of Patient Satisfaction Toward the Implementation of the Bed Management Application at Langsa General Hospital: A Case Study of Bed Management System Deployment JB, Salwa Nur; Fachrurazy, Fachrurazy; Nadita, Lola Astri; Hidayati, Sri
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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The digital transformation of healthcare has become a strategic imperative for improving hospital efficiency, transparency, and patient-centered service quality. This study examines the impact of the Implementation of the Bed Management Application on Patient Satisfaction at Langsa General Hospital, integrating theoretical perspectives from the Technology Acceptance Model (TAM), the DeLone and McLean Information System Success Model (ISSM), and the SERVQUAL framework. Using a quantitative explanatory–predictive approach, the research employs both statistical regression analysis (SPSS 26.0) and algorithmic predictive modeling (Python Decision Tree Classifier) to measure and predict the relationship between system implementation and patient satisfaction. Data were collected from 120 inpatients who experienced the digital bed allocation process, using validated indicators that capture ease of use, reliability, accuracy, service speed, and transparency. The results of the regression analysis reveal that the implementation of the Bed Management Application has a positive and statistically significant effect on patient satisfaction (B = 0.687, β = 0.682, p < 0.001), with a coefficient of determination (R² = 0.465), indicating that 46.5% of the variance in satisfaction can be explained by system implementation effectiveness. Complementary algorithmic analysis using the Decision Tree Classifier achieved a prediction accuracy of 50%, identifying a key threshold at X_mean = 4.1, above which patients were predominantly classified into the High Satisfaction category. The findings confirm that technological quality, perceived usefulness, and information transparency significantly influence patient satisfaction, validating the theoretical constructs of TAM and ISSM. Furthermore, the integration of inferential and predictive analyses offers both theoretical validation and operational insight, illustrating that robust digital system implementation enhances patient experience, efficiency, and service reliability. This research contributes to advancing hybrid analytical approaches in health informatics, supporting data-driven decision-making and the national Smart Hospital Initiative to optimize patient-centered digital healthcare delivery in Indonesia.

Adaptive Brain-Computer Interface Based on CNN-RNN for Medical Rehabilitation and Smart Device Control Rakha Dwi Prayoga
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Brain-Computer Interfaces (BCIs) based on motor imagery (MI) offer a direct communication pathway for assistive technologies and neurorehabilitation. A significant challenge lies in the inherent non-stationarity and inter-subject variability of Electroencephalography (EEG) signals, which limits the performance and adaptability of conventional systems. This paper proposes a novel adaptive BCI framework that leverages a hybrid Convolutional and Recurrent Neural Network (CNN-RNN) to dynamically learn spatio-temporal features from raw, multi-channel EEG data. This study aims to develop a lightweight and stable model for accurate MI classification. The model was designed for efficiency, utilizing a streamlined architecture with merely 41,860 parameters, and was rigorously evaluated on the public BCI Competition IV 2a dataset for four-class MI classification across nine subjects. The results demonstrate a robust validation accuracy of 62.17%, significantly surpassing the chance-level baseline of 25%. Crucially, the model exhibited exceptional stability, converging rapidly and maintaining consistent performance without overfitting, while also showcasing efficient computational properties. This study confirms the viability of lightweight, adaptive deep learning models in creating more reliable and practical BCIs, establishing a foundational step towards their application in clinical rehabilitation and smart device control.

Utilization of CRISPR and AI-Based Biotechnology for Early Detection and Therapy Development of Genetic Diseases Dili, Muhammad Assya
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Spinal Muscular Atrophy (SMA) remains a critical genetic disease requiring early detection, yet conventional methods like PCR and genetic sequencing suffer from high costs, extended processing times, and limited accuracy in detecting minor mutations. This study addresses these challenges by developing an innovative integrated system that combines CRISPR-Cas biotechnology with artificial intelligence to revolutionize genetic disease detection. The research employs CRISPR system remodeling to optimize guide RNA design targeting SMN1 and SMN2 genes, integrated with a hybrid deep learning model combining Convolutional Neural Network and XGBoost for intelligent mutation prediction. Unlike traditional approaches, this system achieves detection accuracy exceeding 96.5% while significantly reducing processing time through automated AI-driven interpretation of CRISPR signals. The integration enables real-time analysis of complex genetic patterns, minimizes false detection rates, and generates precision-based therapy recommendations tailored to individual mutation profiles. This breakthrough offers substantial advantages over existing methods by providing faster, more accurate, and cost-effective genetic screening suitable for neonatal programs, particularly in resource-limited settings. The system demonstrates strong potential for clinical implementation, supporting early intervention strategies that can dramatically improve patient outcomes. By bridging molecular biology and computational intelligence, this research contributes a transformative framework for genetic disease detection that is scalable, efficient, and clinically applicable, paving the way for personalized medicine approaches in managing hereditary disorders.

MediaPipe-LSTM: Multi-Task Pose Recognition for Safety and Creative Quality Control Divian Nathaniel, Raymond
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Spinal Muscular Atrophy (SMA) remains a critical genetic disease requiring early detection, yet conventional methods like PCR and genetic sequencing suffer from high costs, extended processing times, and limited accuracy in detecting minor mutations. This study addresses these challenges by developing an innovative integrated system that combines CRISPR-Cas biotechnology with artificial intelligence to revolutionize genetic disease detection. The research employs CRISPR system remodeling to optimize guide RNA design targeting SMN1 and SMN2 genes, integrated with a hybrid deep learning model combining Convolutional Neural Network and XGBoost for intelligent mutation prediction. Unlike traditional approaches, this system achieves detection accuracy exceeding 96.5% while significantly reducing processing time through automated AI-driven interpretation of CRISPR signals. The integration enables real-time analysis of complex genetic patterns, minimizes false detection rates, and generates precision-based therapy recommendations tailored to individual mutation profiles. This breakthrough offers substantial advantages over existing methods by providing faster, more accurate, and cost-effective genetic screening suitable for neonatal programs, particularly in resource-limited settings. The system demonstrates strong potential for clinical implementation, supporting early intervention strategies that can dramatically improve patient outcomes. By bridging molecular biology and computational intelligence, this research contributes a transformative framework for genetic disease detection that is scalable, efficient, and clinically applicable, paving the way for personalized medicine approaches in managing hereditary disorders.

Optimizing Sustainable Aquaculture via Internet of Things and Machine Learning Raffi Darrell Firmansyah
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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This research aims to design and build an integrated system utilizing the Internet of Things (IoT) and Machine Learning (ML) for the optimization of sustainable aquaculture. The primary objective is to address key aquaculture challenges, including unstable water quality, feed inefficiency, and slow disease detection. The research design involves a real-time monitoring system using IoT sensors (pH, temperature, and dissolved oxygen) connected to an ESP32 microcontroller. The methodology consists of data collection from these sensors, which is then analyzed using machine learning algorithms: Linear Regression to predict water quality and a Decision Tree to classify fish health. The main outcomes show the system successfully monitors water quality in real-time. The Linear Regression model achieved a low Mean Squared Error (MSE) of 0.042 for predictions, and the Decision Tree model achieved a 93.7% accuracy in classifying fish health conditions. The conclusion is that this system is proven to be an effective decision support tool for enhancing the productivity and sustainability of aquaculture.

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Jamaluddin

Contact Email
hengkitamando26@gmail.com

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+6281397181985

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publication.aptikomsumut@gmail.com

Editorial Address
Jl. Alumni No.3, Padang Bulan, Kec. Medan Baru, Kota Medan, Sumatera Utara 20155

Location
Kota medan,

Sumatera utara

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Jamaluddin

Contact Email hengkitamando26@gmail.com

Phone +6281397181985

Journal Mail Official publication.aptikomsumut@gmail.com

Editorial Address Jl. Alumni No.3, Padang Bulan, Kec. Medan Baru, Kota Medan, Sumatera Utara 20155

Location Kota medan, Sumatera utara INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Jamaluddin

Contact Email
hengkitamando26@gmail.com

Phone
+6281397181985

Journal Mail Official
publication.aptikomsumut@gmail.com

Editorial Address
Jl. Alumni No.3, Padang Bulan, Kec. Medan Baru, Kota Medan, Sumatera Utara 20155

Location
Kota medan,

Sumatera utara

INDONESIA