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All Journal ILKOM Jurnal Ilmiah JSAI (Journal Scientific and Applied Informatics)
Muliati Badaruddin
Universitas Ichsan Gorontalo Utara
Computer Science & IT

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Optimizing Healthcare Services at Suwawa Health Center through Android-based Information System Muliati Badaruddin; m salim; Santawali
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 1 (2024): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i1.5995

Abstract

Healthcare services are the implementation of efforts to maintain optimal health levels, both at the individual and community levels. Improving the quality of healthcare services in Community Health Centers (Puskesmas) has become increasingly important, especially since people today are more discerning in choosing the healthcare services they receive. The research objective is to design and implement an information system that facilitates service transformation at Puskesmas Suwawa. This system aims to enhance the accessibility of healthcare services by allowing the public to undergo health check-ups, report complaints, and consult with general practitioners without the need to physically visit the Puskesmas. The research method used is the research and development method, which aims to create a specific product and test its effectiveness. The system was tested using Whitebox testing, where the registration web module was converted into a flowchart, and Cyclomatic Complexity (CC) was calculated, as well as Blackbox testing. The test results showed that Cyclomatic Complexity (CC) was 6, and the V(G) test result was 6. Based on these results, it can be concluded that the Android-based healthcare service information system at Puskesmas Suwawa, Bone Bolango Regency, can improve the effectiveness and efficiency of providing healthcare services to the public
Analysis of a Hybrid DNN–BiLSTM Framework for Longitudinal Prediction of Lung Disease Recurrence Using Clinical Data Olha Musa; Zainudin Sidik; Ifriandi Labolo; Muliati Badaruddin; Abdul Malik I. Buna
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3222.404-419

Abstract

Predicting lung disease recurrence from longitudinal clinical data remains challenging because irregular temporal patterns and heterogeneous patient characteristics reduce the effectiveness of conventional deep learning models. This study analyzes a hybrid Deep Neural Network (DNN)–Bidirectional Long Short-Term Memory (BiLSTM) framework for the longitudinal prediction of lung disease recurrence using clinical data collected between 2021 and 2024 from a referral hospital in Gorontalo. The dataset includes demographic information, laboratory examination results, clinical diagnoses, and longitudinal medical records. Lung disease recurrence is defined as the reappearance or worsening of the disease during longitudinal clinical follow-up after the initial diagnosis or treatment. The proposed framework combines DNN to learn complex nonlinear relationships among multivariate clinical features and BiLSTM to capture temporal dependencies across sequential patient observations. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and Root Mean Square Error (RMSE), and compared with standalone DNN and BiLSTM models. Experimental results demonstrate that the proposed hybrid framework consistently outperformed the individual models, achieving an improvement of approximately 7–10% across the evaluation metrics while providing more stable longitudinal prediction performance. Furthermore, multivariate analysis identified dominant clinical variables associated with lung disease recurrence, improving the interpretability of prediction results for clinical decision-making. These findings indicate that the proposed framework provides an effective computational approach for longitudinal clinical prediction and supports the development of intelligent clinical decision support systems for recurrence risk assessment
Co-Authors Abdul Malik I. Buna Ifriandi Labolo M Salim Olha Musa Santawali Zainudin Sidik