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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Engineering, Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal Of Artificial Intelligence And Software Engineering Science: Indonesian Journal of Science INOVTEK Polbeng - Seri Informatika
Mukti Qamal
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Classification For Determining Nutritional Status of Toddlers Using Random Forest Method at Tanah Pasir Primary Health Centre, North Aceh Sofyan Iryad, Indana; Qamal, Mukti; Razi, Ar
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10855

Abstract

The nutritional status of toddlers is a fundamental factor in supporting their growth and development, particularly during the golden period of 0–5 years of age. Malnutrition in toddlers can have detrimental effects on physical growth, cognitive development, and immune function. In Indonesia, child malnutrition remains a significant public health challenge, particularly in rural areas, necessitating improved nutritional surveillance systems at primary health centers. The manual assessment of nutritional status at community health centers (Puskesmas) often poses challenges in promptly identifying toddlers with undernutrition or severe malnutrition. This study aims to develop a toddler nutritional status classification system based on the Random Forest method to assist healthcare workers in determining nutritional status quickly and accurately. This study utilized a dataset of 2,612 toddler anthropometric records collected from Tanah Pasir Community Health Center, North Aceh, between November 2024 and January 2025. The dataset was split into training (2,090 records, 80%) and testing (522 records, 20%) sets using stratified random sampling. Key variables included age (0-60 months), body weight (kg), and body height (cm). Nutritional status categories were determined based on WHO Child Growth Standards using the weight-for-age (W/A), height-for-age (H/A), and weight-for-height (W/H) indices. The Random Forest method was chosen due to its ability to construct multiple decision trees through ensemble learning, resulting in more accurate predictions and better resistance to overfitting. The model was implemented with 100 trees and evaluated using standard classification metrics. The experimental results demonstrated that the system achieved strong classification performance, with an accuracy of 93%, precision of 95%, recall of 98%, and an F1-score of 96%. The high recall value is particularly significant in healthcare applications, ensuring minimal false negatives in detecting malnourished toddlers. The developed system facilitates healthcare workers in efficiently and systematically monitoring toddlers' nutritional status with consistent classification standards. Therefore, this system is expected to serve as a decision-support tool to improve community nutritional status at the community health center level, enabling early intervention for at-risk children.
Classification of Smoking Addiction Levels Among Universitas Malikussaleh Students Using the C4.5 Algorithm Mundirawati, Cut; Qamal, Mukti; Rosnita, Lidya
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13809

Abstract

This study addresses the subjective determination of smoking addiction levels among students at Malikussaleh University by implementing the C4.5 algorithm. Using a data mining approach based on entropy and gain ratio, the research objectively classifies addiction levels. Data was gathered from 300 respondents, divided into 240 training and 60 testing samples, covering attributes such as cigarettes per day, smoking duration, and the first cigarette after waking. Analysis reveals that cigarettes per day yielded the highest gain ratio (0.2717), serving as the decision tree's root. The classification identified 95 students with mild, 148 moderate, 55 severe, and 2 very severe addiction. Model evaluation via a confusion matrix showed 80% accuracy, 64.5% precision, 56.8% recall, and a 58.9% F1-score. The C4.5 algorithm proved effective in building an interpretative model using IF–THEN rules. These findings provide a solid foundation for university health policies, prevention programs, and early identification of high-addiction risks among students.
Comparison of Single Exponential Smoothing and Double Exponential Smoothing Methods for Gold Price Prediction mardhatillah, mardhatillah; bustami, bustami; suwanda, rizki; safwandi, safwandi; qamal, mukti
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.8597

Abstract

Emas diakui secara global sebagai safe haven asset dengan nilai yang relative stabil, meskipun harganya tetap mengalami fluktasi akibat pengaruh faktor ekonomi  seperti kondisi global, inflasi, serta keseimbangan permintaan dan penawaran. Oleh karena itu, peramalan harga emas yang akurat menjadi penting dalam mendukung pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan kinerja metode Single Exponential Smoothing dan Double Exponential Smoothing dalam meramalkan harga emas. Data yang digunakan berupa data deret waktu bulanan harga emas periode januari 2022 – 2024 yang diperoleh dari beberapa took emas. Sistem peramalan dikembangkan berbasis web menggunakan bahasa pemograman PHP. Evaluasi akurasi dilakukan menggunakan metode Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa kedua metode mampu memberikan prediksi yang cukup baik, namun metode SES menghasilkan nilai MAPE yang lebih rendah dibandingkan DES. Penelitian ini diharapkan dapat menjadi referensi bagi pelaku usaha emas dalam menentukan strategi investasi yang tepat  
Co-Authors Alzaky, Muammar Ananda Faridhatul Ulva Andriani, Dela Aris Munandar Burhanuddin Burhanuddin Bustami Bustami Eva Darnila Fadhliani, Fadhliani Fadlisyah Fadlisyah Fajriana, Fajriana Fikriah Nst, Nahdah Hamdhana, Defry Hamdhana, Defry Hasan, Safwandi Lidya Rosnita LRosnita, Lidya Mardhatillah, Mardhatillah Mardhatillah, Mona Martin Martin Marzian, Fadel Melodi, Balqis Muhammad Naufal Mundirawati, Cut Muthmainnah Muthmainnah Nurhasanah Nurhasanah Pranidana, Abdi Mulia Pratomo, Rio Rahmadani Sari, Putri Dwi Razi, Ar Risawandi, Risawandi Rizki Suwanda Rizky Putra Fhonna Safwandi Safwandi, Safwandi Saptari, Mochamad Ari Sofyan Iryad, Indana Tri Wulandari Yulisda, Desvina