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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Engineering, Science and Information Technology Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 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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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Few-Shot Learning for Classifying Genuine and Bot Comments on YouTube Using Transformer Models Fikriah Nst, Nahdah; Hamdhana, Defry; Qamal, Mukti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10023

Abstract

This study aims to develop a comment classification system on the YouTube platform to distinguish between real accounts and bot accounts, addressing the challenge of limited labeled data through a few-shot learning approach. The issue of bot accounts masquerading as real users in comment sections is becoming increasingly prevalent and has the potential to spread spam, misinformation, and influence public opinion. In this study, a Transformer-based model, DistilBERT, is used, which is known for its efficiency in understanding natural language context. The model is trained in a few-shot scenario (N5 to N50) using a very limited amount of training data. Testing results show that the model maintains high and stable performance even with minimal data (N5), achieving an F1-score above 0.90. In addition, this system is implemented into a web application using Flask to enable direct and interactive comment detection. The main contribution of this research is the proof that the combination of few-shot learning and the DistilBERT model can provide a practical and efficient solution for classifying YouTube bot account comments even with limited data conditions, as well as providing a replicable approach for similar problems on other digital platforms.
Smart Valve Irrigation System Using Fuzzy Logic for Mustard Pranidana, Abdi Mulia; Qamal, Mukti; Risawandi, Risawandi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10024

Abstract

This study presents the design and implementation of a smart irrigation system using Mamdani fuzzy logic integrated with IoT-based environmental sensors. The system utilizes an ESP32 microcontroller, DHT22 temperature sensor, capacitive soil moisture sensor, and a solenoid valve to perform adaptive irrigation based on real-time environmental conditions. The fuzzy logic engine processes sensor inputs and determines the irrigation intensity through centroid-based defuzzification. A web-based dashboard was developed using PHP and JavaScript to monitor temperature, soil moisture, and irrigation status in real time. The system was tested on mustard greens (Brassica juncea L.) for 12 hours, resulting in a 35% water usage reduction compared to manual watering methods while maintaining optimal soil moisture. This approach demonstrates a promising solution for sustainable and efficient smart agriculture.
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.
Co-Authors Abdul Hadi Alzaky, Muammar Ananda Faridhatul Ulva Andriani, Dela Aris Munandar Burhanuddin Burhanuddin Eva Darnila Fadhliani, Fadhliani Fadlisyah Fadlisyah Fajriana, Fajriana Fikriah Nst, Nahdah Hamdhana, Defry Hamdhana, Defry Hasan, Safwandi Lidya Rosnita LRosnita, Lidya Maito, Rizki Minta Mardhatillah, Mona Martin Martin Maryana Maryana Marzian, Fadel Melodi, Balqis Muhammad Naufal Mukarramah Mukarramah, Mukarramah Muthmainnah Muthmainnah Nurdin Nurdin Nurhasanah Nurhasanah Pranidana, Abdi Mulia Pratomo, Rio Rahmadani Sari, Putri Dwi Razi, Ar Risawandi, Risawandi Rizky Putra Fhonna Sahputra, Ilham Saptari, Mochamad Ari Sofyan Iryad, Indana Tri Wulandari Yesy Afrillia Yulisda, Desvina