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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health, Engineering and Technology
Rini Halila Nasution
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Diagnosis Gangguan Pernapasan Pada Anak (Balita) Menggunakan Metode Forward Chaining dan Naïve Bayes Ilka Zufria; Rini Halila Nasution; Tanjung, Siti Maya Sari
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6915

Abstract

Gangguan sistem pernapasan sering terjadi pada manusia dan dapat menular kapanpun dari manusia ke manusia. Ganguan pernapasan sering sekali terjadi dibandingkan dengan gangguan atau infeksi sistem organ tubuh lainnya. Bermula dari flu biasa dengan gejala relatif ringan sampai berat, batuk, demam, sakit tenggorokan dan sesak napas. Penyakit ini sangat bahaya, khususnya jika menjangkit pada balita. Untuk menyelesaikan masalah yang ada peneliti berupaya membangun sistem pakar untuk membantu masyarakat mengenali lebih dini penyakit pernapasan seperti selesma (common cold), flu, bronkitis, pneumonia, asma, alergi (Rhinitis), sinusitis, tuberkulosis, bronkopneumonia. Berdasarkan gejala-gejala yang dimasukkan ke dalam sistem nantinya, sistem akan menggunakan metode forward chaining dan naïve bayes sebagai mesin inferensinya yang akan menghasilkan diagnosis penyakit. Metode forward chaining akan digabungkan bersama metode naïve bayes untuk menemukan nilai maksimal dari setiap penyakit. Berdasarkan 9 jenis data penyakit dengan 30 jenis data gejala pada penyakit sistem pernapasan, sistem memiliki akurasi sebesar 87,6%. Dengan hasil ini peneliti berharap dapat membantu masyarakat sebagai media konsultasi awal mendiagnosis penyakit pernapasan.
Disease Clusterization Based on Patient Age and Disease Type Using K-Means Clustering Jalaluddin Mahally Hasibuan; Hendra Cipta; Rini Halila Nasution
International Journal of Health Engineering and Technology Vol. 4 No. 5 (2026): IJHESS JANUARY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v4i5.521

Abstract

This study aims to classify disease types based on patient age using the K-Means Clustering method in order to identify disease distribution patterns at Malahayati Islamic Hospital, Medan. The data used in this research consists of medical record data of patients from October to December 2024, including variables such as age, type of disease, gender, and area of residence. The research stages include data cleaning, data transformation of age and disease attributes into numerical values, and clustering analysis using the K-Means algorithm implemented through RapidMiner software. The clustering results produced three main clusters, representing high, moderate, and low disease prevalence levels. Diseases with the highest prevalence cluster include pregnancy-related cases, pneumonia, acute respiratory infections (ISPA), chronic obstructive pulmonary disease (COPD), and gastroenteritis (GEA), which are predominantly found in adult and elderly age groups. The results indicate that patient age significantly influences disease distribution patterns. This study demonstrates that K-Means Clustering is effective in identifying age-based disease patterns and can serve as a decision-support tool for healthcare planning, resource allocation, and disease prevention strategies in hospital management.  
Co-Authors Hendra Cipta Ilka Zufria Jalaluddin Mahally Hasibuan Tanjung, Siti Maya Sari