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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Sinergi JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Susanto Susanto
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Penerapan Algoritma Apriori Sebagai Rekomendasi Menu Itemsets di Trotoar Steak Kafe Susanto Susanto; Budi Santoso; Putri Danisa
semanTIK Vol 5, No 2 (2019): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1284.755 KB) | DOI: 10.55679/semantik.v5i2.7078

Abstract

Bisnis kecil dibidang makanan dan minuman  sekarang sedang marak. Terutama pembisnis yang mengedepankan konsep  kafe dan online. Trotoar steak cafe, sebagaimana prinsip berbisnis, mencari keuntungan merupakan tujuan operasional kafe, maka untuk memenuhi tujuan operasional sekaligus mempertahankan kegiatan operasional dalam persaingan dunia bisnis, diperlukan suatu strategi yang dapat meningkatkan penjualan. Tidak mudah untuk menumbuhkan minat beli sebelum akhirnya konsumen memutuskan untuk membeli suatu produk. Salah satu bentuk promosi agar lebih terarah dan tepat sasaran salah satu caranya adalah mengetahui selera beli konsumen  yang  dapat diamati melalui data-data pembelian di Trotoar Steak Kafe Lubuklinggau. Minimnya alat hitung untuk menentukan pola kombinasi menu yang paling di minati oleh konsumen maka dibutuhkan sistem  yang nantinya dapat membantu proses promosi penjualan di Trotoar Steak Kafe menggunakan data mining algoritma apriori. Algoritma Apriori sangat dimanfaatkan dalam proses penjualan, dengan memberikan hubungan antar data dan penjualan, dalam hal ini makanan atau minuman yang dipesan sehingga akan didapat pola pembelian konsumen. Pihak kafe memanfaatkan informasi tersebut untuk mengambil tindakan bisnis yang sesuai, dalam hal ini informasi dapat menjadi bahan pertimbangan untuk menentukan strategi penjualan selanjutnya.
Effective and efficient approach in IoT Botnet detection Susanto Susanto; Deris Stiawan; M. Agus Syamsul Arifin; Mohd. Yazid Idris; Rahmat Budiarto
SINERGI Vol 28, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.1.004

Abstract

Internet of Things (IoT) technology presents an advantage to daily life, but this advantage is not a guarantee of security. This is because cyber-attacks, such as botnets, remain a threat to the user. Detection systems are one of the alternatives to maintain the security of IoT network. A reliable detection system should effectively detect botnets with high accuracy levels and low positive rate. It should be efficient to perform detection quickly. However, data generated by IoT networks have high dimensions and high scalability, so they need to be minimized. In network security analysis process, high-dimension data pose challenges, such as the dimension curse correlation between different dimensions, which causes features that are hard to define, datasets that are mostly unordered, cluster combination, and exponential growth. In this study, we applied feature reduction using the Linear Discriminant Analysis (LDA) method to minimize features on the IoT network to detect botnet. The reduction process is carried out on the N-BaIoT dataset which has 115 features reduced to 2 features. Performing feature reduction with detection systems has become more effective and efficient. Experimental result showed that the application of LDA combined with machine learning on the classification Decision Tree method was able to detect with accuracy that reached 100% in 98.58s with only two features.
IoT Botnet Detection Using Autoencoders and Decision Trees Susanto, Susanto; Arifin, M. Agus Syamsul; Wijaya, Harma Oktafia Lingga
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1693

Abstract

The use of IoT devices has grown rapidly, leading to an increase in cyber attacks that pose greater security and privacy threats than ever before. One such threat is botnet attacks on IoT devices. An IoT botnet is a group of Internet-connected IoT devices infected with malware and remotely controlled by an attacker. Machine learning techniques can be employed to detect botnet attacks. The use of machine learning-based detection methods has been shown to be effective in identifying cyber attacks. The performance of the detection system in machine learning can be improved by utilizing data reduction methods. The data reduction process in classification is used to overcome the problem of scalability and computation resources in the IoT. This paper proposes a detection system using the Autoencoder reduction method and the Decision tree classification method. The test results demonstrate that the Deep Autoencoder algorithm can reduce data and memory usage from 1.62 GB to 75.9 MB, while also improving the performance of decision tree classification, resulting in a high level of accuracy up to 100%. The Autoencoder approach in conjunction with the Decision Tree exhibits superior capabilities compared to previous studies.
Deteksi Aktifitas Malware pada Internet of Things menggunakan Algoritma Decision Tree dan Random Forest Syamsul Arifin, M. Agus; Tri Susilo, Andri Anto; Susanto, Susanto; Martadinata, A. Taqwa; Santoso, Budi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1903

Abstract

The Internet of Things (IoT) has become an integral part of modern life, connecting smart devices to enhance efficiency and convenience. However, with the increased adoption of IoT, cybersecurity threats, particularly malware, have also risen. This research focuses on detecting malware attacks in IoT networks using machine learning algorithms, specifically Decision Tree and Random Forest. The dataset used is CICIoT2023, which includes various types of IoT network traffic such as BenignTraffic, Mirai-greeth_flood, Mirai-greip_flood, and Backdoor_Malware. In this study, both algorithms demonstrated exceptionally high accuracy on the training data, reaching 100%, and on the test data, achieving 99.94% accuracy for the Random Forest algorithm and 99.90% for the Decision Tree algorithm. Although the performance of both algorithms on the training data was almost identical, Random Forest showed better performance in detecting the Backdoor_Malware class compared to Decision Tree when using test data. Random Forest achieved a precision of 99%, recall of 64%, and F1-Score of 78%, while Decision Tree achieved a precision of 71%, recall of 72%, and F1-Score of 72%. Results from 10-fold cross-validation indicate that the models did not experience overfitting, suggesting reliable and well-generalized models. This research provides insights that the Random Forest algorithm is more effective in detecting malware attacks in IoT networks compared to Decision Tree, particularly in identifying the Backdoor_Malware class. These findings are expected to contribute to the development of more efficient and reliable malware detection systems for IoT networks.
Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM) Septini, Ayu; Susanto; Elmayati
Bulletin of Computer Science Research Vol. 5 No. 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i2.475

Abstract

This study aims to analyze public sentiment toward OpenAI ChatGPT technology on Twitter using the Support Vector Machine (SVM) method. The background of this research is based on the increasing global use of the internet and artificial intelligence (AI), as well as the role of social media as a platform for people to express their opinions. This study employs a qualitative research approach using the Support Vector Machine method, with data collection conducted through primary data obtained by crawling data from Twitter. The research uses data collected from 4,305 Indonesian-language tweets gathered between January and September 2023. These tweets were then classified into positive, neutral, and negative sentiments using the SVM method. The results indicate that out of the total collected data, 2,196 tweets had a neutral sentiment, 1,500 tweets had a positive sentiment, and 591 tweets had a negative sentiment. In the model performance evaluation, training data with an 80:20 ratio achieved the highest accuracy of 94.25%, while testing data with a 70:30 ratio achieved the highest accuracy of 93.16%. Additionally, the use of 10-fold cross-validation on training data resulted in an accuracy of 89.94%, while testing data achieved an average accuracy of 78.17%.
IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning Susanto; Stiawan, Deris; Santoso, Budi; Sidabutar, Alex Onesimus; Arifin, M. Agus Syamsul; Idris, Mohd Yazid; Budiarto, Rahmat
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5871

Abstract

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.
Perbandingan Algoritma Decision tree dan Gradient Boosting pada Model Sistem Deteksi Serangan Siber di Jaringan Internet of Things Arifin, M. Agus Syamsul; Armanto, Armanto; Susanto, Susanto; Martadinata, A. Taqwa
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 1 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i1.26096

Abstract

Internet of things (IoT) memberikan banyak manfaat dimana membuat banyak perangkat pintar semakin dekat dan mudah digunakan. Penerapan teknologi IoT yang semakin luas memberikan banyak ancaman bari dalam segi keamanan data karena banyak perangkat yang terhubung dengan protocol yang beragam untuk mengatasinya dibutuhkan sebuah  Intrusion Detection System (IDS) yang handal untuk mendeteksi serangan dalam jaringan IoT. Dalam penelitian ini akan membangun sebuah model IDS menggunakan algoritma decision tree dan gradient boosting kemudian membandingkan performanya. Dataset yang digunakan pada penelitian ini menggunakan dataset dari CICIoT2023 karena kelas yang tidak seimbang dan ukuran dataset yang besar teknik Random Under Sampling (RUS) digunakan juga dalam penelitian ini. Hasil dari penelitian menunjukkan performa yang baik untuk setiap model IDS yang dibuat. Untuk data latih ketika tanpa menggunakan maupun teknik RUS algoritma decision tree mendapatkan akurasi tinggi mencapai 100% namun ketika menggunakan data uji gradient boosting mendapatkan hasil yang lebih baik yaitu 99,10% untuk sebelum penerapan teknik RUS dan 76,31% setelah penerapan teknik RUS.
Perancangan dan pelatihan Aplikasi Konsultasi Online Inspektorat Daerah Kabupaten Musirawas Susanto, Susanto; Lingga Wijaya, Harma Oktafia; Hartansyah, Dendi; Rusdiyanto, Rusdiyanto
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 1 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Inspektorat, sebagai lembaga pengawas dalam konteks pemerintahan, memegang peran sentral dalam menjamin akuntabilitas, transparansi, dan efektivitas dalam pelaksanaan kebijakan publik. Dalam era digital dan informasi, tantangan yang dihadapi oleh inspektorat semakin kompleks, membutuhkan pendekatan yang lebih terintegrasi dan efisien dalam pengelolaan informasi. Dengan Perkembangan teknologi informasi telah memberikan peluang baru dan perubahan paradigma dalam penyelenggaraan pemerintahan. Sistem Informasi Konsultasi memungkinkan inspektorat untuk memanfaatkan teknologi ini untuk meningkatkan efisiensi, kolaborasi, dan akurasi dalam pelaksanaan tugasnya. Permasalahan yang timbul saat ini Inspektorat kabupaten musirawas seringkali dihadapkan pada volume data dan informasi yang besar terkait dengan berbagai kasus dan pemeriksaan. Manualnya pengelolaan informasi dapat menyulitkan inspektur untuk mengidentifikasi pola, tren, atau temuan penting, dan juga Dalam upaya meningkatkan efisiensi operasional, inspektorat mencari cara untuk mengotomatisasi proses-proses yang dapat diotomatisasi. Sistem Informasi Konsultasi menyediakan alat otomatisasi untuk tugas-tugas rutin, membebaskan waktu inspektur untuk fokus pada analisis dan evaluasi. Metode yang dilakukan untuk PKM kali ini ada 2 arah yang pertama yaitu pembuatan aplikasi sesuai dengan kebutuhan dari inspektorat kabupaten musirawas selanjutnya metode ke dua yaitu ceramah dan praktek untuk memberikan input kepada peserta dari inspektorat kabupaten musirawas dalam penggunaan sistem informasi yang telah dibuat. Hasil pengabdian kepada masyarakat ini menghasilkan aplikasi konsultasi online inspektorat daerah kabupaten musirawas. Dan dari tingat pemahaman peserta tentang penggunaan aplikasi tersebut sebesar 90%.
Detection of Reconnaissance Attacks Using a Hybrid CNN–LSTM on IoT Network Susanto; Dermawan, Budi Arif; Rasenda, Rasenda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (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.
DETEKSI SERANGAN SQL INJECTION MENGGUNAKAN ALGORITMA MACHINE LEARNING PADA JARINGAN IOT Rasenda, Rasenda; Susanto, Susanto; Dermawan, Budi Arif
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.
Co-Authors Andri Anto Tri Susilo, Andri Anto Armanto, Armanto Budi Arif Dermawan Budi Santoso Deris Stiawan Deris Stiawan Dwi Budi Santoso Elmayati Hartansyah, Dendi Lingga Wijaya, Harma Oktafia M. Agus Syamsul Arifin Martadinata, A. Taqwa Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Putri Danisa Rahmat Budiarto Rahmat Budiarto Rasenda, Rasenda Rusdiyanto Rusdiyanto, Rusdiyanto Septini, Ayu Sidabutar, Alex Onesimus Syamsul Arifin, M. Agus