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Dwiza Riana
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INDONESIA
Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 7 Documents
 1 
Search results for , issue "volume 4 no. 2, june 2026" : 7 Documents clear
Classification of Corn Leaf Disease Detection Using ResNet50 and Inception V3 I Indarti; Dewi Laraswati; F Frieyadie
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.90

Abstract

Corn is an important food crop in Indonesia, requiring accurate classification methods to support agricultural productivity. To evaluate and compare ResNet50 and Inception V3 models with augmentation techniques for corn image classification. Deep learning classification using CNN architectures with rotation, shifting, and flipping augmentation. 3,560 corn images (2,500 training, 700 validation, and 360 testing). ResNet50 achieved 93.05% accuracy, while Inception V3 achieved the highest performance with 94.02% accuracy, 93.04% precision, 93.00% recall, and 93.02% F1-score. Image augmentation significantly improved classification performance, and Inception V3 was identified as the most effective model for corn image classification.
Computational Pharmacology Approach to Identify Antidiarrheal Candidates from Eleusine indica L.: Molecular Docking Simulation and Drug-likeness Prediction Targeting 5ZHP Receptor Faisal Akhmal Muslikh; Rizki Rahmadi Pratama; Syahputra Wibowo; Yanu Andhiarto; Burhan Ma’arif; Maximus M. Taek
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.142

Abstract

Diarrhea remains a significant global health concern with high morbidity and mortality rates, particularly in developing countries. The use of synthetic antidiarrheal drugs is associated with various adverse effects, necessitating the exploration of safer therapeutic alternatives derived from natural sources. This study aimed to evaluate the potential of secondary metabolite compounds from Eleusine indica as antidiarrheal candidates through an in silico approach, employing drug-likeness analysis and molecular docking simulation against the muscarinic acetylcholine M3 receptor (PDB ID: 5ZHP). Drug-likeness analysis was performed using the SwissADME web tool based on Lipinski's Rule of Five. Molecular docking simulation was conducted using Molegro Virtual Docker, with the root mean square deviation (RMSD) value employed as a validation parameter. The results revealed that the majority of the compounds satisfied Lipinski's criteria, indicating their potential as oral drug candidates. Docking method validation yielded an RMSD value of 0.777437 Å, confirming the validity and reliability of the docking procedure. Docking results suggested that compound M15 (andrographolide) and compound M13 [4-(1-hydroxyethyl)-2,6-bis(3-methylbut-2-en-1-yl)phenol] exhibited the lowest Rerank Scores of −116.686 and −114.300, respectively, which were notably lower than that of the positive control loperamide (−41.9287), suggesting potentially stronger binding affinity and more favorable interaction with the target receptor. However, these findings are predictive in nature and require further validation through experimental biological studies and molecular dynamics simulations to confirm actual binding stability and pharmacological effectiveness.
Accuracy of Clinical Coding in Cesarean Section Cases Using ICD-10 and ICD-9-CM Gama Bagus Kuntoadi; Indah Kristina; Hudiyati Agustini; Rumondang Christin
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.146

Abstract

Accurate clinical coding in cesarean section cases is essential for morbidity reporting, clinical data quality, and the validity of INA-CBG reimbursement claims under the national health insurance system. Obstetric cases are inherently complex, involving multiple diagnosis and procedure codes that must conform to ICD-10 and ICD-9-CM standards. Objective: This study aimed to analyze the accuracy of ICD-10 diagnosis coding and ICD-9-CM procedure coding across multiple obstetric coding components in cesarean section cases at a hospital in Jakarta, Indonesia. A descriptive quantitative study with a retrospective design was conducted on 95 medical records of obstetric and gynecological patients who underwent cesarean section in 2025, selected through total sampling. Coding accuracy was determined by comparing hospital-assigned codes against standard codes re-verified by researchers based on WHO ICD-10 Volume 2 and ICD-9-CM guidelines. Data were analyzed using frequency, percentage, mean, median, and standard deviation, following a Shapiro-Wilk normality test. The overall accuracy rate for ICD-10 diagnosis codes was 83.9% (266/317 codes) and for ICD-9-CM procedure codes was 91.5% (86/94 codes). Accuracy varied substantially across coding components: secondary diagnosis codes for cesarean delivery and delivery outcome codes each reached 90.5%, while codes for concomitant diagnoses reached only 34.7%. For procedures, cesarean section codes (74.x) achieved 85.3% accuracy, whereas non-cesarean section procedure codes showed accuracy of only 5.3%, predominantly due to undercoding of additional operative procedures documented in surgical reports. Coding accuracy for obstetric cesarean section cases was generally good to very good in aggregate; however, substantial gaps remain in the coding of concomitant diagnoses and additional procedures. This study contributes a component-level evaluation across diagnosis, delivery outcome, and procedure coding, offering a more granular assessment of coding accuracy than prior single-metric studies.
The Sociodemographic Factors and Accessibility in Utilizing the Maternal and Child Health Handbook for Monitoring Under-Five Children in North Sumatera Syafrina Ulfah; Fithri Handayani Lubis; Kiki Rismadi; Diza Fathamira Hamzah
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.149

Abstract

Utilization of the Maternal and Child Health (MCH) Handbook plays a crucial role in monitoring the health and development of children under five years of age; however, its uptake remains suboptimal. According to the 2023 Indonesian Health Survey (SKI), MCH Handbook utilization for under-five growth monitoring in North Sumatra Province was only 67.9%, below the national average of 74.4%. This study aimed to analyze the influence of sociodemographic factors and healthcare accessibility on MCH Handbook utilization for under-five child monitoring in North Sumatra Province. A cross-sectional design was employed using secondary data from the 2023 SKI. The study sample comprised 4,164 under-five children who owned or had previously owned an MCH Handbook in North Sumatra. Data were analyzed through univariate frequency distribution, bivariate chi-square testing, and multivariate multiple logistic regression using the backward stepwise (likelihood ratio) method. The multivariate analysis demonstrated that health insurance ownership (AOR = 1.389; 95% CI = 1.215–1.587), maternal education (AOR = 1.326; 95% CI = 1.107–1.588), maternal age (AOR = 1.163; 95% CI = 1.009–1.339), and maternal employment (AOR = 1.161; 95% CI = 1.007–1.338) were significant positive predictors of MCH Handbook utilization, while longer travel time to health facilities (AOR = 0.690; 95% CI = 0.503–0.946) was inversely associated. Area classification was not statistically significant. These findings underscore the need for sustained health education targeting mothers, maintenance of universal health insurance coverage, and strengthening of community-based health services particularly posyandu to optimize MCH Handbook utilization in North Sumatra.
Determinants of Hospital Length of Stay Among Pulmonary Tuberculosis Inpatients at A Secondary General Hospital in Indonesia: A Comparative Analysis Balqis Nurmauli Damanik; Hely Hely
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.150

Abstract

Tuberculosis (TB) remains a major public health challenge in Indonesia, which has the second-highest TB burden globally. Hospital length of stay (LOS) is an important indicator of inpatient care efficiency and influences reimbursement under the Indonesia Case-Based Groups (INA-CBGs) payment system. However, evidence regarding determinants of LOS in Indonesian secondary hospitals is still limited. This retrospective comparative study analyzed secondary medical-record data from a secondary general hospital in Medan, North Sumatra, collected between January and August 2025. Among 238 identified records, 215 patients with a primary diagnosis of pulmonary TB (ICD-10: A15.0–A16.9) met the inclusion criteria. LOS was calculated from admission and discharge dates. As LOS was not normally distributed (Shapiro–Wilk, p < 0.001), the Kruskal–Wallis and Mann–Whitney U tests with Dunn post-hoc correction were applied (α = 0.05). The median LOS was 3 days (IQR 2–4; range 0–13). Most patients were younger than 65 years (75.3%), while 51.2% had no documented comorbidity. Comorbidity burden was the only factor significantly associated with LOS (H = 18.74, p < 0.001; η² = 0.082). Patients with two or more comorbidities had significantly longer hospitalization than those without comorbidities (mean 4.18 vs. 2.82 days; p < 0.001) and those with one comorbidity (p = 0.013). No significant differences were found according to age group or insurance status. These findings indicate that comorbidity burden contributes to prolonged hospitalization among pulmonary TB inpatients and support integrating multimorbidity management into TB care. Larger multicenter prospective studies are needed to validate these findings.
Machine Learning-Based Outcome Prediction in Isolated Ventricular Septal Defects Nurdan Erol; Çiğdem Erol; Ilkim Ecem Emre
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.151

Abstract

Ventricular Septal Defect (VSD) is one of the most common congenital heart defects. Predicting whether isolated VSD will close spontaneously, require surgical intervention, or remain unclosed is essential for optimizing patient management and avoiding unnecessary treatment. This study aimed to develop and evaluate machine learning (ML) models for predicting VSD outcomes using maternal and neonatal clinical characteristics. A retrospective dataset of 382 patients with isolated VSD was analyzed and categorized into spontaneous closure, surgical closure, and non-closure outcomes. Data preprocessing included duplicate removal and listwise deletion of records with missing values. To address class imbalance, random undersampling and oversampling were applied exclusively to the training set (80%), while the independent test set (20%) remained unchanged. Five ML algorithms-Decision Tree, Random Forest, K-Nearest Neighbor, Naive Bayes, and XGBoost-were evaluated using accuracy, macro-average area under the receiver operating characteristic curve (AUC), and class-specific F1-scores. XGBoost achieved the best overall performance with an accuracy of 65.8% and a macro-average AUC of 0.81, demonstrating balanced classification across all outcome groups. Although Decision Tree and Random Forest produced the highest F1-score (92.3%) for the minority surgical closure class, their overall multiclass performance was inferior to XGBoost. Sampling strategies had minimal impact on overall predictive performance, although ensemble-based methods showed greater robustness to class imbalance. These findings suggest that ML, particularly XGBoost, provides a promising approach for early risk stratification of isolated VSD, supporting personalized clinical decision-making and improving identification of patients requiring surgical intervention.
Mobile Web App Development for Diabetic Foot Screening Using Inlow’s 60-Second Screen with Automated Risk Classification Suhendri; Wildan Zhilal Manafi; Bayu Reviyadi; Sri Rahayu; Iin Karmila Septiani; Mita Nurmala
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.152

Abstract

Diabetic foot complications constitute a major contributor to preventable lower-extremity amputation, yet primary care screening remains inconsistent due to the absence of integrated digital tools implementing validated clinical protocols. This study presents the design, implementation, and system-centric evaluation of Podiatrix, a mobile web application that operationalizes Inlow's 60-Second Diabetic Foot Screen through an automated, condition-based clinical workflow. Unlike existing tools that address isolated screening criteria, Podiatrix implements all seven Inlow criteria within a unified five-step wizard and applies a deterministic hierarchical classification engine that directly mirrors the original Inlow protocol logic rather than relying on fixed score thresholds. The system was evaluated using three complementary methods: black-box testing across 50 simulated clinical scenarios, Nielsen's heuristic usability evaluation conducted by three independent evaluators, and performance load testing using Apache JMeter under concurrent user conditions. Results demonstrated 100% classification accuracy (50/50 scenarios) matching manual Inlow protocol interpretation, an average heuristic severity score of 1.15 out of 4 indicating high usability, and a mean response time of 820 ms with less than 1% error rate under 100 concurrent users. These findings confirm that Podiatrix provides a computationally robust, highly usable, and scalable digital infrastructure that lays the groundwork for future prospective clinical trials in primary care and community health settings.

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