Article Per Year (5 Year)
p-Index From 2020 - 2025
2.258
P-Index
This Author published in this journals
All Journal Jurnal Teknik Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jambura Journal of Electrical and Electronics Engineering Jurnal Tecnoscienza
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

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jambura Journal of Electrical and Electronics Engineering

 1 
Application of Naïve Bayes Classifier Method for Classification of Acute Respiratory Infection (ARI) Rahmat Thaib, Rahmat Thaib; Betrisandi, Betrisandi
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
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.v7i2.31851

Abstract

ARI is a global problem that affects millions of individuals every year, including in Pohuwato District. Symptoms include runny nose, sore throat, cough, and shortness of breath which are generally caused by bacterial or viral infections. Types of ARI can be classified into mild, moderate, and severe. The problem in this study is the lack of public knowledge related to ARI management and the high incidence of ARI disease.   This study aims to classify ARI diseases based on patient age and length of hospitalisation in the hope that it can help medical personnel at the puskesmas in providing fast handling and appropriate treatment to patients with ARI. Data collection techniques were carried out by direct survey to the Marisa Health Centre, Pohuwato Regency, Gorontalo Province. Data analysis is done by data mining which emphasises the classification of ARI diseases with the Naïve Bayes Classifier method. The results of this study showed high accuracy of 96.61% for mild prediction, 95.60% for moderate prediction, and 100% for severe prediction. So the Naïve Bayes Classifier method is able to produce correct predictions in identifying ARI cases.ISPA adalah salah satu masalah global yang mempengaruhi jutaan individu setiap tahunnya, termasuk di Kabupaten Pohuwato. Gejala yang ditimbulkan bervariasi seperti pilek, sakit tenggorokan, batuk, dan sesak napas yang umumnya disebabkan oleh infeksi bakteri atau virus. Jenis ISPA dapat diklasifikasikan menjadi ringan, sedang, dan berat. Permasalahan dalam penelitian ini adalah kurangnya pengetahuan masyarakat terkait penanganan ISPA dan tingginya angka kejadian penyakit ISPA.   Penelitian ini bertujuan untuk mengklasifikasikan penyakit ISPA berdasarkan usia pasien dan lama rawat inap dengan harapan dapat membantu para tenaga medis di puskesmas dalam memberikan penanganan yang cepat serta pengobatan yang tepat kepada para penderita ISPA. Teknik pengumpulan data dilakukan dengan survei langsung ke Puskesmas Marisa,  Kabupaten Pohuwato, Provinsi Gorontalo. Analisis data dilakukan dengan data mining yang menekankan pada klasifikasi penyakit ISPA dengan metode Naïve Bayes Classifier. Hasil dari penelitian ini menunjukkan akurasi yang tinggi yaitu 96.61% untuk prediksi ringan, 95,60% untuk prediksi sedang, dan 100% untuk prediksi berat. Jadi metode Naïve Bayes Classifier mampu menghasilkan prediksi yang tepat dalam mengidentifikasi kasus ISPA.
Application of the K-Medoids Clustering Algorithm to Determine the Nutritional Status of Toddlers Betrisandi, Betrisandi; Hasan, Maryam; Bahrin, Bahrin
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 1 (2025): Januari - Juni 2025
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.v7i1.27523

Abstract

Stunting is a chronic malnutrition problem due to a lack of nutritional intake in the body for a long time, as a result of which there is a growth disorder in children, namely the child's height becomes shorter or dwarfed than the standard age. The problem of malnutrition in toddler is a major concern in various countries. Based on the data from the Ministry of Health of the Republic of Indonesia in 2018, 30.8% of Indonesian toddlers were malnourished. Malnutrition in toddlers is very influential on physical and mental growth such as intellectual intelligence, speaking, walking, learning, immune system and low body immune system. The purpose of thisstudy is to classify the nutritional status of toddler into five cluster namely cluster 0 poor, cluster 1 undernourished, cluster 2 good nutrition, cluster 3 over nutrition and cluster 4 obesity by using the K-Medoids Algorithm method. K-Medoids Algorithmis one of the Algorithm used in data and mining. K-Medoids Algorithm is relatively fast and simple, making it easier to find Medoids in a group (cluster). Based on the data on toddlers there are 74 toddlers, the results of clustering are obtained from grouping, cluster 0 is 13 toddlers with bad nutrition, cluster 1 is undernutrition 15 toddlers, cluster 2 is good nutrition 21 toddlers, cluster 3 is over nutrition 23 toddlers and cluster 4 is obesity totaling 2 toddlers.Stunting merupakan masalah kurang gizi kronis karena kurangnya asupan gizi pada tubuh dalam waktu yang cukup lama akibatnya terjadi gangguan pertumbuhan pada anak-anak yakni tinggi badan anak menjadi lebih pendek atau kerdil dari standar usianya. Masalah kurang gizi padakelompok balita menjadi perhatian utama diberbagai negara. Berdasarkan data Kemenkes RI tahun 2018 sebanyak 30,8 % balita Indonesia mengalami gizi buruk. Gizi kurang pada balita sangat berpengaruh pada pertumbuhan fisik maupun mental seperti kecerdasan intelektual, berbicara, berjalan, belajar, daya tahan tubuh serta sistem imun tubuh rendah. Adapun tujuan dari penelitian ini adalah untuk mengelompokkan status gizi balita ke dalam 5 cluster yaitu cluster 0 gizi buruk, cluster 1 gizi kurang, cluster 2 gizi baik, cluster 3 gizi lebih dan cluster 4 obesitas dengan menggunakan metode Algoritma K-Medoids. Algoritma K-Medoids merupakan salah satu algoritma yang digunakan dalam data mining. Algoritma K-medoids relatif cepat dan sederhana sehingga memudahkan menemukan medoids dalam sebuah kelompok (cluster). Berdasarkan data balita yang ada sejumlah 74 balita maka didapatkan hasil clustering dari pengelompokkan dengan cluster 0 yaitu gizi Buruk berjumlah 13 balita, cluster 1 yaitu gizi kurang berjumlah 15 balita, cluster 2 yaitu gizi baik sejumlah 21 balita, cluster 3 yaitu gizi lebih berjumlah 23 balita dan cluster 4 obesitas berjumlah 2 balita. 
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 Sudirman S. Panna Sulaehani, Ruhmi Warid Yunus