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Betrisandi, Betrisandi
Universitas Pohuwato
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Transportation Other

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Classification of Pneumonia Severity in Children Using the Fuzzy K-Nearest Neighbor Method Based on Patient Clinical Data Rahmat Thaib; Betrisandi Betrisandi
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.34624

Abstract

Pneumonia is one of the most deadly acute respiratory infections in children, especially in the toddler age group. Indonesia ranks eighth among 15 countries with the highest pneumonia mortality rate, namely 22,000 toddler deaths per year. Pneumonia can be caused by various microorganisms such as viruses, fungi, and bacteria. The occurrence of pneumonia is characterized by symptoms of cough and/or difficulty breathing such as rapid breathing and lower chest wall indrawing. The diagnosis of pneumonia is generally based on a combination of clinical symptoms such as fever, cough, rapid breathing, and physical examination results such as physical or radiological, however, the diagnostic process often encounters obstacles, such as limited trained medical personnel, limited diagnostic tools and subjectivity in assessing symptoms, especially in children who are not yet able to communicate their complaints clearly. This study aims to classify pneumonia based on symptoms and severity, namely severe pneumonia and mild pneumonia in children to assist medical personnel in making more accurate and efficient decisions. The results of this study indicate that the Fuzzy K-Nearest Neighbor method with k=3 and m=2 produces an accuracy of 62.67%, precision of 65.91%, recall of 69.05%, F1-Score of 67.44%, and a deviation of ±8.00% in classifying pneumonia in children.
Comparison of NBC and KNN in Classifying Stunting in Children in Rural Areas Betrisandi Betrisandi; Rahmat Thaib
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34488

Abstract

Stunting is one of the chronic nutritional problems that remains a serious concern in Indonesia. Children who experience stunting not only experience physical growth retardation, but also cognitive development disorders that have the potential to reduce intelligence, academic achievement, and productivity in adulthood. The problem in this study is the high prevalence of stunting in children in rural areas. The purpose of this study is to analyse the performance of the Naïve Bayes Classifier (NBC) and K-Nearest Neighbour (KNN) and compare the performance of the two methods to determine the most optimal method for classifying stunting status in children in accordance with the Research Master Plan with a focus on engineering and technology for improving ICT content and the research topic of big data technology development. The research methods used included data collection through observation and interviews. Data processing and analysis were carried out by comparing the NBC and KNN methods in classifying child stunting. The results of this study indicate that the NBC method has higher accuracy, namely 95.24% and an F1-score of 97%, compared to the KNN method, which has an accuracy of 76.19% and an F1-score of 86%. Therefore, the KNN method is more optimal for use in classifying stunting in children.
Penerapan Case Based Reasoning Untuk Penyakit Tanaman Semangka Betrisandi Betrisandi
JURNAL TECNOSCIENZA Vol. 3 No. 2 (2019): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/p5jz0164

Abstract

Semangka (Citrullus Vulgaris Schard) tumbuh merambat dan banyak memiliki kandungan air. Proses pembudidayaan tanaman semangka juga tidak terlepas dengan persoalan penyakit. Terbatasnya pengetahuan dan kurangnya pemahaman mengidentifikasi penyakit tanaman semangka sering mengakibatkan pertumbuhan tanaman semangka kurang maksimal, sehingga hasil panen pun kurang memuaskan bahkan bisa mengakibatkan gagal panen. Adapun tujuan dari penelitian ini untuk mengidentifikasi penyakit pada tanaman semangka menggunakan Case Based Reasoning dengan similarity sebagai metode pengukuran similaritas. Proses identifikasi dengan cara memasukkan gejala-gejala yang terjadi ke dalam sistem, kemudian proses perhitungan nilai similaritas antara kasus baru dengan dengan basis kasus. Sistem dibangun dengan 30 gejala untuk 15 penyakit. Masing-masing gejala mempunyai nilai yang berbeda di mana nilai bobot yang digunakan ditentukan oleh pakar. Kata kunci: Case Based Reasoning, Similarity, Semangka
Co-Authors Abdul Yunus Labolo Anas Anas Annahl Riadi Bahrin Bahrin Bahrin Bahrin Drajana, Ivo Colanus Rally Ivo Colanus Rally Drajana Maryam Diange Maryam H Dumako Maryam Hasan Rahmat Thaib Rahmat Thaib, Rahmat Thaib Sudirman S. Panna Sulaehani, Ruhmi Warid Yunus