Garuda - Garba Rujukan Digital

Syukron, Ananda Irya Shakila

Unknown Affiliation

Author-ID : 9613132

Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Pengembangan Sistem Deteksi Malware Berbasis Web Menggunakan Algoritma Random Forest dengan Notifikasi Realtime Musyaafa, Muhammad Naufal; Kiswanto, Dedy; Syukron, Ananda Irya Shakila; Amanah, Fadilla
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.9896

Abstrak - Perkembangan teknologi digital yang pesat telah meningkatkan risiko serangan malware yang semakin kompleks dan sulit dideteksi dengan metode konvensional berbasis tanda tangan (signature-based). Penelitian ini bertujuan untuk mengembangkan sistem deteksi malware berbasis web menggunakan algoritma Random Forest dengan fitur notifikasi realtime untuk meningkatkan efektivitas dan kecepatan respon terhadap ancaman keamanan. Dataset yang digunakan berasal dari platform Kaggle, terdiri atas sekitar 34.000 file Portable Executable (PE) Windows dengan 55 fitur yang diekstraksi secara statis dari PE Header dan daftar impor DLL. Model machine learning dibangun menggunakan Pipeline dari Scikit-learn yang terdiri dari StandardScaler, CountVectorizer, dan RandomForestClassifier. Hasil evaluasi menunjukkan bahwa model mencapai tingkat akurasi sebesar 99,65% dengan nilai precision, recall, dan F1-score sempurna (1.00) pada kedua kelas, menunjukkan kemampuan model yang sangat andal dan seimbang dalam mendeteksi file berbahaya maupun aman. Model yang telah divalidasi kemudian diimplementasikan ke dalam aplikasi web berbasis Django dengan antarmuka dinamis, yang memberikan peringatan instan kepada pengguna secara asinkronus menggunakan teknologi JavaScript (Fetch API). Penelitian ini berhasil menghasilkan sistem deteksi malware yang praktis, adaptif, dan responsif, yang dapat meningkatkan kesadaran serta keamanan digital pengguna terhadap ancaman siber yang terus berkembang.Kata kunci : Deteksi Malware; Machine Learning; Random Forest; Django; Notifikasi Realtime; Abstract - The rapid development of digital technology has increased the risk of malware attacks that are increasingly complex and difficult to detect using conventional signature-based methods. This study aims to develop a web-based malware detection system using the Random Forest algorithm with real-time notification features to improve the effectiveness and speed of response to security threats. The dataset used was sourced from the Kaggle platform, consisting of approximately 34,000 Windows Portable Executable (PE) files with 55 features extracted statically from the PE Header and DLL import list. The machine learning model was built using a Pipeline from Scikit-learn consisting of StandardScaler, CountVectorizer, and RandomForestClassifier. The evaluation results show that the model achieved an accuracy rate of 99.65% with perfect precision, recall, and F1-score (1.00) in both classes, demonstrating the model's highly reliable and balanced ability to detect both malicious and safe files. The validated model was then implemented into a Django-based web application with a dynamic, which provides instant alerts to users asynchronously using JavaScript technology (Fetch API).This research has successfully produced a practical, adaptive, and responsive malware detection system that can increase user awareness and digital security against ever-evolving cyber threats. Keywords: Malware Detection; Machine Learning; Random Forest; Django; Real-Time Notifications;

Sistem Monitoring Kualitas Udara dan Peringatan Dini Berbasis IoT dengan Prediksi Polusi Menggunakan Random Forest Regression Syukron, Ananda Irya Shakila; Kiswanto, Dedy; Hafiz, Alvin; Harahap, Salsa Nabila
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10243

Abstrak - Pencemaran udara merupakan ancaman serius terhadap kesehatan masyarakat dan kualitas lingkungan, sehingga diperlukan sistem pemantauan yang responsif, andal, dan proaktif. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem monitoring kualitas udara dan peringatan dini berbasis Internet of Things (IoT) dengan integrasi machine learning untuk prediksi tingkat polusi secara real-time. Metode penelitian mencakup pengembangan perangkat keras berbasis ESP32 yang terhubung dengan sensor DHT11 (suhu dan kelembaban) serta MQ-135 (konsentrasi gas), pengiriman data ke server VPS melalui protokol HTTP, pembuatan dashboard web untuk visualisasi dan notifikasi, serta pembangunan model prediksi menggunakan Random Forest Regression. Dataset dikumpulkan secara real-time dengan total 38.733 entri, kemudian diproses melalui preprocessing (pembersihan, imputasi missing value, normalisasi Min-Max) dan dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan bahwa model memiliki performa sangat baik dengan R² Score 0,8829 (latih) dan 0,8852 (uji), MAE 2,98, serta RMSE 5,13 pada data uji mengonfirmasi akurasi tinggi dan minimnya risiko overfitting. Analisis feature importance mengungkapkan bahwa konsentrasi gas (PPM) merupakan variabel paling dominan dalam prediksi AQI (skor 0,8497). Sistem peringatan dini juga terbukti efektif saat AQI melebihi ambang batas (misalnya ≥151), indikator LED berubah merah, alarm suara aktif berulang, dan insiden secara otomatis tercatat dalam log digital untuk manajemen respons yang terukur. Secara keseluruhan, integrasi IoT dan Random Forest Regression menghasilkan sistem monitoring yang tidak hanya informatif dan andal, tetapi juga proaktif dalam mitigasi risiko kesehatan akibat polusi udara.Kata kunci : IoT; Random Forest Regression; Kualitas Udara; Sistem Peringatan Dini; Abstract - Air pollution poses a serious threat to public health and environmental quality, requiring a responsive, reliable, and proactive monitoring system. This study aims to design and implement an Internet of Things (IoT)-based air quality monitoring and early warning system with machine learning integration for real-time pollution level prediction. The research methods included developing ESP32-based hardware connected to DHT11 (temperature and humidity) and MQ-135 (gas concentration) sensors, sending data to a VPS server via the HTTP protocol, creating a web dashboard for visualization and notification, and building a prediction model using Random Forest Regression. The dataset was collected in real-time with a total of 38,733 entries, then processed through preprocessing (cleaning, missing value imputation, Min-Max normalization) and divided with a ratio of 80:20 for training and testing. The evaluation results show that the model performs very well with an R² Score of 0.8829 (training) and 0.8852 (testing), MAE of 2.98, and RMSE of 5.13 on the test data, confirming high accuracy and minimal risk of overfitting. Feature importance analysis revealed that gas concentration (PPM) was the most dominant variable in AQI prediction (score 0.8497). The early warning system also proved effective when the AQI exceeded the threshold (e.g., ≥151), with the LED indicator turning red, the audible alarm sounding repeatedly, and the incident being automatically recorded in a digital log for measurable response management. Overall, the integration of IoT and Random Forest Regression resulted in a monitoring system that is not only informative and reliable but also proactive in mitigating health risks due to air pollution.Keywords: IoT; Random Forest Regression; Air Quality Monitoring; Early Warning System;

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