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All Journal The Indonesian Journal of Business Administration Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Techne : Jurnal Ilmiah Elektroteknika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) ProBisnis : Jurnal Manajemen Jurnal Minfo Polgan (JMP) Community: Jurnal Pengabdian Pada Masyarakat
Adhitya Nugraha
Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Optimasi Logistic Regression untuk Deteksi Serangan DoS pada Keamanan IoT Primadya, Nauval Dwi; Nugraha, Adhitya; Luthfiarta, Ardytha; Fahrezi, Sahrul Yudha
Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.1065

Abstract

Keamanan perangkat Internet of Things (IoT) merupakan prioritas utama karena potensi risiko kerusakan perangkat dan kebocoran data yang dapat berdampak serius. Perangkat IoT telah membawa manfaat signifikan ke berbagai sektor, seperti kesehatan, transportasi, dan industri, namun tingkat serangan terhadapnya terus meningkat. Dalam mengatasi tantangan ini, pendekatan machine learning digunakan dengan memanfaatkan dataset CIC IOT ATTACKS 2023 dari University of New Brunswick. Untuk menghasilkan data yang berkualitas, dilakukan random undersampling untuk mengatasi ketidakseimbangan data, dan seleksi fitur menggunakan Recursive Feature Elimination untuk mendapatkan fitur terbaik. Pemilihan Logistic Regression sebagai algoritma pemodelan dipilih dengan pertimbangan yang matang. Logistic Regression dipilih karena kemampuannya memberikan interpretasi yang jelas terhadap kontribusi relatif setiap fitur terhadap prediksi keamanan perangkat IoT. Selain itu, model ini efisien secara komputasional, mengatasi ketidakseimbangan data, dan tahan terhadap overfitting, yang semuanya merupakan faktor krusial dalam konteks keamanan IoT. Hasil penelitian menunjukkan bahwa penggunaan Logistic Regression bersamaan dengan seleksi fitur memberikan tingkat akurasi tertinggi mencapai 97%, dengan waktu pemrosesan yang efisien sekitar 11 detik. Dari hasil ini, dapat disimpulkan bahwa kombinasi teknik random undersampling dan seleksi fitur menggunakan Recursive Feature Elimination secara positif memengaruhi akurasi pada model Logistic Regression, menjadikannya pilihan yang sesuai untuk meningkatkan keamanan perangkat IoT.
Accelerating Classification For Iot Attack Detection Using Decision Tree Model With Gini Impurity Tree-Based Feature Selection Technique Dzaki, Muhammad Hafizh; Nugraha, Adhitya; Luthfiarta, Ardytha; Riyanto, Azizu Ahmad Rozaki; Novandian, Yohanes Deny
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.3817

Abstract

The Internet of Things (IoT) continues to expand rapidly, with the number of connected devices expected to reach billions in the near future. However, it makes IoT devices prime target for cyber-attack. Therefore, an effective Intrusion Detection System (IDS) is required to detect these attacks swiftly and accurately. This study aims to build a machine learning-based IDS to effectively detect attack on IoT network using the CIC IoT 2023 dataset. The dataset contains over 46 million data rows with 48 features, covering 33 attack types and 1 benign class. To address the dataset's complexity and enhance processing efficiency, feature selection technique was applied. Six feature selection techniques from three categories – Filter-based, Wrapper-based, and Hybrid methods – were evaluated to produce the best feature subset. Each subset was tested using a Decision Tree algorithm. Then, the model performance calculated based on accuracy, computational time, as well as macro-precision, -recall, and -F1-score. The results demonstrate that the three best feature selection from each category – Mutual Information, Genetic Algorithm, and Gini Impurity Tree-based – improved training time by average different 55 seconds from 148 seconds, which speed up by 63.06% without sacrificing accuracy. The Gini Impurity Tree-based algorithm proved to be the most efficient, producing the smallest feature subset, which is 10 features, faster processing times, which is 40 seconds, and shallower tree’s depth, which is 64 level from 73 level. In conclusion, feature selection not only enhances computational efficiency but also simplifies tree’s shape without sacrificing the accuracy of detection.
DiabTrack: Sistem Prediksi Dini Diabetes Melitus Tipe 2 berbasis Web menggunakan Algoritma K-Nearest Neighbors Pangestu, Aditya Gilang; Winarno, Sri; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29691

Abstract

Type 2 diabetes mellitus is a chronic disease that is often not detected early enough, increasing the risk of serious complications. Based on this, early detection of this disease is very important to reduce its negative impact. This research aims to develop the DiabTrack system, a web-based prediction system using the K-Nearest Neighbors (KNN) algorithm. This type of research is development research using the Rapid Application Development (RAD) model, including the requirements planning, design workshop, and implementation stages. The dataset used comes from Kaggle, containing 53,000 samples and 8 features. The model is trained using the KNN algorithm and the SMOTE technique to balance the data. Evaluation results show that the KNN model achieves an accuracy of 99.17%, a recall of 100%, and an F1-score of 94%, making it the chosen algorithm for the DiabTrack website. Additionally, Black Box testing results indicate that all features in the DiabTrack system function as expected, helping the public monitor their health conditions while serving as an initial analysis tool for medical professionals.
Performance Comparison of IoT Classification Models using Ensemble Stacking and Feature Importance setiawan, nabila putri; Nugraha, Adhitya; Luthfiarta, Ardytha; Mulyana, Yudha
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4673

Abstract

Internet of Things (IoT) security is becoming a top priority as the number of connected devices increases online. This research utilizes the CIC IoT ATTACK 2023 dataset from the University of Brunswick, which consists of 46 million data on various types of attacks on IoT devices, such as DDoS, DoS, Brute Force, Spoofing, and Mirai attacks. To address the imbalance in the dataset, a random undersampling technique is applied to ensure the machine learning model is not biased towards the majority class. The ensemble learning approach was chosen due to its ability to combine the strengths of multiple algorithms, thus improving accuracy and stability in detecting complex IoT attacks. The algorithms used include gradient boosting, bagging, voting, and stacking. In particular, the stacking model, which combines the bagging classifier and gradient boosting, achieved the highest accuracy of 93%. Although the accuracy of the stacking model decreased to 92.4% after feature selection, the precision, recall, and F1-score remained high at 92.0. In addition, the computation time was also reduced from 2111.69 seconds to 1208.27 seconds. These findings indicate that ensemble learning approaches and feature selection techniques have great potential in improving IoT security, providing more reliable and efficient threat detection solutions.
Co-Authors Abu Salam Amanda, Nabila Rizka Ardytha Luthfiarta Ariel Immanuel Prayogo Astuti, Yani Parti Cahya, Leno Dwi Dheo Rimbano Dwi Larso Dzaki, Muhammad Hafizh Egia Rosi Subhiyakto, Egia Rosi Erick Irawadi Alwi, Erick Irawadi Erwin Yudi Hidayat Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Gaffar, Andi Widya Mufila Ganiswari, Syuhra Putri Gustian, Dinda Aulia Hafiizhudin, Lutfi Azis Harun Al Azies Huda, Alam Muhammad Idayati, Irma Ika Novita Dewi Imam Muttaqin, Almas Najiib Joshua Calvin Kurniawan Krisna, Julius Immanuel Theo Kurniawan, Joshua Calvin Md. Mahadi Hasan, Md. Mahadi Miftahussilmi, Ray Ikhsandri Mulyana, Yudha Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nisa, Laila Rahmatin Novandian, Yohanes Deny Novanto , Fandy Novanto , The, Fandy Octaviani, Dhita Aulia Pangestu, Aditya Gilang Paramita, Cinantya Prayogo , Ariel Immanuel Primadya, Nauval Dwi Rachmad Hidayatullah, Rachmad Riyanto, Azizu Ahmad Rozaki Rumita, Dea Salsabila, Pramesya Mutia Setiawan, Dicky setiawan, nabila putri Sri Winarno Sundari Sundari Syarifah, Ulima Muna The, Fandy Novanto Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine