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All Journal Syntax Jurnal Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sistem dan Informatika JIKO (Jurnal Informatika dan Komputer) Informatika Pertanian Jurnal Sains dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika MULTINETICS JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan International Journal of Basic and Applied Science Computer Science (CO-SCIENCE) J-SAKTI (Jurnal Sains Komputer dan Informatika) CEBONG Journal International Journal of Enterprise Modelling Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Aplikasi
Budi Arif Dermawan
Universitas Pembangunan Nasional Veteran Jakarta
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Detection of Reconnaissance Attacks Using a Hybrid CNN–LSTM on IoT Network Susanto; Budi Arif Dermawan; Rasenda Rasenda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 1 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2535

Abstract

The rapid expansion of the Internet of Things (IoT) has increased connectivity across various sectors but also exposed systems to new and evolving cybersecurity threats. One of the most critical threats is the reconnaissance phase, where attackers gather system information to prepare more sophisticated intrusions. Conventional intrusion detection systems often fail to detect reconnaissance due to similarities with benign traffic. To address this problem of ineffective reconnaissance detection, this study proposes a hybrid detection framework that combines autoencoder-based feature extraction with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifier. The autoencoder, an unsupervised neural network that compresses input data and reconstructs it with minimal loss, is used to reduce data dimensionality and learn meaningful hidden features. The CNN captures spatial patterns and LSTM models temporal dependencies in network traffic. Experiments were conducted using the CICIoT2023 dataset, focusing exclusively on reconnaissance attacks. The evaluation metrics include accuracy, precision, recall, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and F1-score. Results show that the proposed model achieves an overall accuracy of 99.79%, specificity of 0.9994, precision of 0.9948, recall of 0.9445, and F1-score of 0.9648. Class-level analysis demonstrates high performance across most attack types, though Ping Sweep exhibits a lower recall of 0.6853 despite achieving perfect precision. These results demonstrate that the hybrid CNN–LSTM model with autoencoder-based feature extraction can effectively detect reconnaissance attacks in IoT networks. The approach enhances detection accuracy, reduces false alarms, and provides a promising foundation for improving real-world IoT security monitoring systems.
Comparative Analysis of Machine Learning Models for Burnout Prediction in Generation-Z Anis Masruriyah; Sanggi Bayu Ardika; Ade Hikma Tiana; Budi Arif Dermawan
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.13081

Abstract

Burnout has become an increasingly prevalent mental health issue among Generation Z due to the interaction of psychological and behavioral factors in a highly digitalized environment. This study aims to predict burnout risk levels using a multi-class machine learning classification approach. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing data understanding, preprocessing, modeling, and evaluation. A synthetic dataset containing 10,000 records and 22 psychological, behavioral, and lifestyle attributes was used to classify burnout risk into three categories: low, medium, and high. To address class imbalance and ensure reliable performance estimation, Stratified K-Fold cross-validation was employed. Logistic Regression was implemented as a baseline linear model, while Random Forest represented a non-linear approach. Experimental results demonstrate that Random Forest achieved the best performance, obtaining a macro F1-score of 0.988 and outperforming Logistic Regression in multi-class burnout prediction. Feature importance analysis further revealed that psychological variables, particularly the wellbeing index and anxiety score, contributed more substantially to burnout prediction than behavioral variables such as screen time. These findings indicate that internal psychological conditions are stronger predictors of burnout risk than external digital behaviors. This study provides a comparative evaluation of linear and non-linear machine learning models in an imbalanced multi-class setting and offers an interpretable framework to support data-driven strategies for early burnout detection and mental health intervention.
Reconfigurable Metasurface Panels for Active Electromagnetic Shielding of Protective Domes Hengki Tamando Sihotang; Budi Arif Dermawan; Rasenda Rasenda; Galih Prakoso Rizky A
Cebong Journal Vol. 4 No. 3 (2025): July: Green dan Blue Economy
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cebong.v4i3.420

Abstract

The increasing complexity of electromagnetic (EM) environments in defense and communication systems necessitates shielding solutions that are both adaptive and efficient. Conventional static shielding domes, while effective in blocking electromagnetic interference (EMI), are inherently limited by their fixed frequency response, high structural weight, and lack of real-time adaptability. This research investigates the design and performance of reconfigurable metasurface panels for active electromagnetic shielding of protective domes, with the aim of enhancing shielding effectiveness, tunability, and structural efficiency. The study explores the integration of reconfigurable metasurfaces into dome architectures, enabling dynamic control of electromagnetic wave propagation through electronically tunable elements. Performance metrics including shielding effectiveness (in dB), tunable frequency ranges, angular stability, and real-time adaptability were evaluated and benchmarked against conventional static shielding designs. Results indicate that reconfigurable metasurface domes achieve superior shielding performance across wide frequency bands while offering significant weight reduction and improved adaptability. These characteristics make them well-suited for critical applications such as military radomes, satellite communication shelters, aerospace systems, and secure civilian infrastructures. However, challenges remain regarding large-scale fabrication, integration complexity, power requirements for active tuning, and environmental durability. Despite these limitations, the findings highlight the transformative potential of reconfigurable metasurfaces as the foundation of next-generation adaptive shielding technologies. This research demonstrates that reconfigurable shielding domes not only address the shortcomings of static designs but also pave the way for resilient, flexible, and future-proof electromagnetic protection systems.
DETEKSI SERANGAN SQL INJECTION MENGGUNAKAN ALGORITMA MACHINE LEARNING PADA JARINGAN IOT Rasenda Rasenda; Susanto Susanto; Budi Arif Dermawan
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 10 No. 2 (2025): JUTIM (JURNAL TEKNIK INFORMATIKA MUSIRAWAS) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v10i2.2795

Abstract

SQL Injection merupakan salah satu bentuk serangan siber yang paling berbahaya karena memungkinkan penyerang untuk mengakses, memodifikasi, atau menghapus data secara ilegal melalui manipulasi perintah SQL. Sistem deteksi berbasis aturan memiliki keterbatasan dalam menghadapi pola serangan baru yang bersifat dinamis dan sulit dikenali. Penelitian ini bertujuan mengembangkan model deteksi serangan SQL Injection dengan pendekatan machine learning menggunakan kombinasi Autoencoder dan Algoritma Machine Learning. Autoencoder digunakan untuk mengekstraksi fitur dan mendeteksi pola anomali pada data input, sedangkan Algoritma Machine Learning berperan sebagai model klasifikasi untuk membedakan antara permintaan normal dan serangan. Data yang digunakan terdiri atas payload berlabel yang mencakup input normal dan serangan SQL Injection, yang selanjutnya diproses melalui tahapan normalisasi, ekstraksi fitur, dan pelatihan model. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian diharapkan menghasilkan model deteksi yang adaptif, mampu mengenali pola serangan baru, serta memiliki tingkat kesalahan deteksi yang rendah pada sistem keamanan jaringan IOT.
Analisis Sentimen Ulasan Aplikasi M-Pajak pada Google Play Store Menggunakan XGBoost Theresia Aurelly Claudia Budianto; Budi Arif Dermawan; Mohamad Jajuli
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 3 No 1 (2025): Vol 3 No 1 (Jurnal Sistem Informasi dan Aplikasi)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v3i1.12440

Abstract

Pada era digital ini, aplikasi M-Pajak menjadi inovasi pemerintah Indonesia yang bertujuan memudahkan wajib pajak dalam memenuhi kewajiban perpajakan secara online. Meskipun telah diunduh lebih dari satu juta kali, aplikasi ini masih menghadapi berbagai kendala teknis yang tercermin dari rendahnya rating di Google Play Store. Penelitian ini bertujuan untuk mengidentifikasi fitur-fitur yang paling banyak mendapat kritik negatif dari para pengguna. Berdasarkan hasil analisis, fitur yang paling banyak menerima kritik negatif yaitu terkait proses login dan verifikasi efin. Hal ini menunjukkan bahwa kendala teknis dan administratif masih menjadi kendala utama dalam pengalaman pengguna aplikasi. Metode yang digunakan adalah Knowledge Discovery in Database (KDD) dengan algoritma XGBoost sebagai klasifikasi model. Dengan menggunakan Stratified K-Fold untuk membagi data, model memperoleh akurasi sebesar 95%, dan saat diuji menggunakan data baru yang berbeda dari data pelatihan, model menghasilkan akurasi sebesar 93%. Pengembang aplikasi M-Pajak menyarankan melakukan perbaikan pada sistem login, verifikasi, dan pengajuan agar pengalaman pengguna menjadi lebih baik dan kendala teknis dapat diminimalkan.
Prediksi Curah Hujan Kota Bogor Menggunakan Algoritma Random Forest Maesha Ayu Syaharani; Budi Arif Dermawan; Riza Ibnu Adam
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 3 No 1 (2025): Vol 3 No 1 (Jurnal Sistem Informasi dan Aplikasi)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v3i1.12459

Abstract

Kota Bogor merupakan salah satu wilayah di Indonesia dengan intensitas curah hujan yang tinggi sepanjang tahun. Tingginya curah hujan tersebut menjadi salah satu faktor penyebab terjadinya bencana seperti banjir dan tanah longsor. Pengembangan model prediksi curah hujan menjadi kebutuhan penting untuk mendukung perencanaan dan mitigasi risiko bencana oleh pemerintah daerah Kota Bogor. Penelitian ini bertujuan untuk membangun model prediksi curah hujan harian. Rancangan penelitian menggunakan metodologi Knowledge Discovery in Database (KDD) dengan algoritma Random Forest sebagai metode utama dalam analisis. Model Random Forest diterapkan dengan penyesuaian Quantile Transform dan Grid Search untuk memperoleh nilai parameter terbaik. Hasil penyesuaian menunjukkan bahwa kinerja model optimal dengan bootstrap:True, n_estimators: 500, max_depth: 5, dan max_features: sqrt. Model diuji menggunakan K-Fold Cross Validation pada beberapa nilai K = 3, 5, 7, 10, dan 15 yang diukur berdasarkan selisih antara nilai prediksi dan aktual. Nilai K = 15 menunjukkan tingkat kesalahan terendah dengan RMSE sebesar 21,92 mm dan MAE sebesar 15,21 mm.
Co-Authors Ade Hikma Tiana Adhi Rizal Agung Siswoyo Aini Lailla Asri, Nur Alfafisabil, Wahyu Alfanda Novebrian Maharadja Aliffia Kulsumarwati Alrasyid, Wildan Anggara, Jerry Anis Fitri Nur Masruriyah Anis Masruriyah Apriade Voutama Ashari, Aliffia Novsiyanti Betha Nurina Sari Carudin Cika Oktavia Daffa Rafif Agustian Dwi Ely Kurniawan Eddy Ryansyah Erliza Hambali Fuza Putra Arwanda Galih Prakoso Rizky A Galih Prakoso Rizky A Haerudin Hannie, Hannie Hengki Tamando Sihotang Ibnu Adam, Riza Ikhsal, Muhammad Fachry Intan Purnamasari Iqbal Maulana Iqbal Maulana Isy Karima Fauzia LILIK BUDIPRASETYO Loemongga Oktaria Sihombing Maesha Ayu Syaharani Muhammad Hilmi Syarif Nana Mulyana Maghfur Natcha Kwintarini Suparman Nur Padilah, Tesa Nurul Afifah Arifuddin Permatasari, Ismi Aprilianti Putri Dwi Rahayu Putri, Dyas Rahma Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda Rasenda, Rasenda Renaldy Permana Sidiq Rijal Abdulhakim Riza Ibnu Adam Riza Ibnu Adam, Riza Ibnu Rizky A, Galih Prakoso Rohanah, Aan Sanggi Bayu Ardika Sofi Defiyanti Sukarno Wijaya, Nanda Susanto Susanto Susanto Susilawati Susilawati Syafi'i, Muhammad Tesa Nur Padilah Tesa Nur Padilah Theresia Aurelly Claudia Budianto Wahyu Alfafisabil Wildan Alrasyid Yuyun Umaidah