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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 Algoritme Jurnal Mahasiswa Teknik Informatika TECHSI - Jurnal 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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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.
Pendampingan Petani Milinieal dalam Penggunaan ASSA: Sistem Jalur Distribusi di dalam e-Marketplace berbasis Smart Farming untuk Ketahanan Pangan di Kecamatan Lhoksukon Aceh Utara Ulva, Ananda Faridhatul; Fadhliani, Fadhliani; Nurhasanah, Nurhasanah; Andriani, Dela; Qamal, Mukti
Jurnal SOLMA Vol. 13 No. 2 (2024)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v13i2.15171

Abstract

Background: Petani mileneal dikenal sebagai petani yang berusia 18-55 tahun, di Kabupaten Aceh Utara Petani Mileneal khususnya di Desa Lhoksukon sangat meningkat pesat di tahun 2023. Para petani mileneal mengalami kesulitan dalam hal pemasaran dan hal pemantauan pertanian mereka secara jarak jauh dan realtime contohnya dalah pemantauan tata kelola air, dan penyiraman tanaman. Tujuan kegiatan ini adalah dapat meningkatkan minat para petani dalam pertanian guna menjadikan para petani mileneal maju dan mandiri dan tercapainya ketahanan pangan di Aceh Utara. Metode: Mitra yang menjadi kegiatan ini adalah 100 petani milleneal di Desa Lhoksukon serta Pemerintah Daerah Aceh Utara. Observasi, wawancara dan kuesioner dilakukan untuk pengumpulan data. Workshop, pelatihan, penyuluhan, dan pedampingan kepada petani dilakukan selama 4 bulan. Hasil: Adanya efesiensi jalur distribusi serta pengurangan biaya, peningkatan penjualan produk sebesar 30% dalam tiga bulan pertama penggunaan aplikasi ASSA, biaya produksi berkurang hingga 10%, pengiriman produk menjadi lebih cepat, sehingga produk masih segar saat sampai ke konsumen. Kesimpulan: Kegiatan ini berhasil meningkatkan keterampilan petani dibidang pemasaran digital, memperluas akses pasar dan penjualan produk, optimalisasi dalam jalur distribusi, dimana hal ini dapat berkontriusi signifikan terhadap ketahanan pangan di Aceh Utara.
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.
IMPLENTASI DATA MINING UNTUK REKOMENDASI PAKET MENU MAKANAN DENGAN MENGGUNAKAN ALGORITMA APRIORI Qamal, Mukti; Syah, Fadli; Parapat, Az Zura Izmi
TECHSI - Jurnal Teknik Informatika Vol. 14 No. 1 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i1.6747

Abstract

Saat ini dunia bisnis sedang sangat berkembang salah satunya bisnis Restoran. Sebuah restoran memiliki banyak menu dari makanan, minuman dan cemilan. Dimana setiap harinya restoran akan melakukan transaksi dari menu tersebut sehingga data transaksi penjualan menjadi menumpuk. Namun data transaksi yang ada ini belum dimanfaatkan dengan baik. Salah satu pemanfaatan yang dapat dilakukan dari data transaski yaitu melakukan pengolahan data, dimana menu yang dipesan oleh pelanggan yang berbeda-beda dapat dilakukan analisis untuk menentukan paket menu yang dapat menjadi rekomendasi kepada para pelanggan. Salah satu teknologi yang dapat digunakan untuk mewujudkannya adalah data mining. Algoritma apriori termasuk jenis aturan asosiasi pada data mining yang dapat digunakan untuk menentukan pola kombinasi antar itemset. Pada penelitian ini digunakan 1000 data transaksi penjualan. Dimana nilai support dan confidence yang dimasukkan yaitu 30 %. Setelah mengetahui pola pembelian konsumen, kita bisa mengatur strategi penjualan seperti membuat paket menu berupa makanan dan minuman. Hasil yang diperoleh dari perhitungan algoritma apriori yaitu, pola kombinasi menu makanan dan minuman yang paling sering dipesan adalah Nasi Putih Ayam Penyet dan Air Mineral dengan nilai confidence tertinggi yaitu 41,69%
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 Abdul Hadi 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 Maito, Rizki Minta Mardhatillah, Mardhatillah Mardhatillah, Mona Martin Martin Maryana Maryana Marzian, Fadel Melodi, Balqis Muhammad Naufal Mukarramah Mukarramah, Mukarramah Mundirawati, Cut Muthmainnah Muthmainnah Nurdin Nurdin Nurhasanah Nurhasanah Parapat, Az Zura Izmi Pranidana, Abdi Mulia Pratomo, Rio Rahmadani Sari, Putri Dwi Razi, Ar Risawandi, Risawandi Rizki Suwanda Rizky Putra Fhonna Safwandi Safwandi, Safwandi Sahputra, Ilham Saptari, Mochamad Ari Sofyan Iryad, Indana syah, Fadli Tri Wulandari Yesy Afrillia Yulisda, Desvina