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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Proceeding SENDI_U Jurnal Teknik Informatika UNIKA Santo Thomas JurTI (JURNAL TEKNOLOGI INFORMASI) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmu Teknik dan Komputer JIKA (Jurnal Informatika) MEANS (Media Informasi Analisa dan Sistem) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Informatika Terpadu Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Janitra Informatika dan Sistem Informasi Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi Journal of Software Engineering and Information System (SEIS) Jurnal Teknologi Ilmu Komputer NERO (Networking Engineering Research Operation) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika)
Suarna, Nana
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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OPTIMASI MODEL XGBOOST UNTUK PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN OPTUNA Optarina, Yasni; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.10527

Abstract

Heart disease is one of the leading causes of mortality worldwide, emphasizing the need for accurate early detection systems. Machine learning models such as XGBoost have demonstrated strong performance in medical classification tasks; however, their effectiveness is highly dependent on optimal hyperparameter configurations. This study aims to improve the performance of XGBoost for heart disease classification by applying hyperparameter optimization using the Optuna framework with the Tree-structured Parzen Estimator (TPE) algorithm. The UCI Heart Disease dataset, consisting of 918 records, is used in this study. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to the training data. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results show that the optimized XGBoost model achieves an accuracy of 89.13%, outperforming the baseline model with 87.50%, and improves recall from 87.50% to 89.10%. In addition, the optimized model attains a higher ROC-AUC value of 0.9319, indicating improved classification stability. These findings demonstrate that Optuna-based hyperparameter optimization effectively enhances the performance and reliability of XGBoost, making it suitable for supporting early heart disease diagnosis in medical decision support systems.
Application of the K-Means Algorithm in the Segmentation of 3kg Lpg Customers Ananda, Ginaselvia; Suarna, Nana; Bahtiar, Agus; Arif Rinaldi Dikananda; Faturrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1853

Abstract

This research was motivated by PT Sumber Perkasa Mandiri's need to understand the purchasing patterns of 3 kg LPG gas customers more accurately in order to improve the effectiveness of its marketing strategy. The purpose of this study was to apply the K-Means Clustering algorithm to form customer segmentation based on transaction behavior. The method used is a quantitative approach with sales data analysis of 850 records through the stages of data selection, preprocessing, attribute transformation, and modeling using RapidMiner Studio. Model evaluation was carried out using the Davies-Bouldin Index to determine the optimal number of clusters. The results of the study show the formation of two main clusters, namely the premium customer cluster with high purchase frequency and high loyalty, and the low-activity customer cluster that only makes purchases when necessary. The best DBI value at K=2 of 0.057 indicates excellent cluster separation quality. These findings conclude that K-Means Clustering is effective in identifying differences in consumption behavior, and its implications provide a strategic basis for companies to design loyalty programs for high-value customers and more intensive promotions for low-activity customers.
Comparison of Balancing Strategies for Classifying Guava Fruit Diseases Putri Nabilla; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1859

Abstract

The problem of class imbalance often poses an obstacle in deep learning-based image classification, especially in the domain of digital agriculture. The imbalance in data distribution makes it easier for models to recognize the majority class, while performance for the minority class declines. This study aims to analyze the effectiveness of three strategies for handling class imbalance: Weighted Loss Function, Oversampling, and a combination of Weighted Loss and Oversampling, in improving the performance of image classification of guava fruit diseases using a transfer learning-based MobileNetV2 architecture. The dataset consists of 3,784 images of three disease classes, namely Anthracnose, Fruit_Fly, and Healthy_guava, which show an imbalanced distribution. The research was conducted through the stages of Exploratory Data Analysis (EDA), pre-processing, augmentation, model training with four scenarios, and evaluation using Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. The results showed that the Combination model (Oversampling and Weighted Loss) performed best on the minority class with an F1-score of 0.9630, the highest among all models. The Oversampling strategy produced the highest Macro F1-score of 0.9617, while Weighted Loss provided a significant improvement in classification sensitivity but was still below the combination model. Thus, it can be concluded that the combination strategy is the most effective approach in improving the sensitivity of the model to minority classes, while Oversampling excels in the overall performance stability of the model.
Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Gunawan, Arya; Suarna, Nana; Bahtiar, Agus; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Pengembangan Model IDS Berbasis Deep Reinforcement Learning untuk Prediksi dan Mitigasi Serangan Siber Dalam Network Traffic Analysis Martanto; Suarna, Nana; Kurnia Putri, Dede; Mardiana, Ana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Intrusion Detection System (IDS) merupakan komponen krusial dalam pertahanan jaringan modern, berfungsi mendeteksi dan merespons ancaman siber secara cepat dan akurat. Penelitian ini menawarkan kontribusi baru melalui perancangan lingkungan Markov Decision Process (MDP) yang lebih realistis, integrasi reward shaping adaptif, serta evaluasi komprehensif multi-algoritma Deep Reinforcement Learning (DRL) yaitu Proximal Policy Optimization (PPO), Deep Q-Network (DQN), Advantage Actor-Critic (A2C) dan perbandingannya dengan model supervised learning mutakhir . Dataset CICIDS2017 dan UNSW-NB15 digunakan sebagai sumber data lalu lintas jaringan, mencakup berbagai jenis serangan dan lalu lintas normal. Lingkungan pelatihan dirancang khusus untuk memungkinkan agen DRL belajar melalui interaksi langsung dengan data, dengan reward function yang memandu agen untuk meningkatkan akurasi deteksi dan meminimalkan kesalahan. Metodologi penelitian meliputi perancangan arsitektur model DRL, proses pelatihan selama 200.000 time steps, serta evaluasi kinerja model berdasarkan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi menunjukkan DQN mencapai akurasi tertinggi pada pendekatan DRL (89–92%), namun secara keseluruhan model supervised learning seperti Random Forest dan CNN masih melampaui DRL dengan akurasi 98–99%. Temuan ini mengonfirmasi bahwa DRL memiliki potensi kuat dalam adaptasi dinamis, tetapi masih memerlukan optimasi lebih lanjut untuk menyaingi metode supervised pada klasifikasi statis. Penelitian ini juga menghadirkan blueprint integrasi IDS–DRL dengan SOC dan firewall adaptif, yang memberikan landasan implementatif pada sistem keamanan nyata. Penelitian ini berkontribusi pada pengembangan IDS adaptif yang mampu melakukan deteksi dan mitigasi secara real-time dengan tingkat akurasi tinggi. Keterbatasan penelitian mencakup kebutuhan komputasi yang tinggi dan potensi ketidakstabilan pelatihan, yang membuka peluang untuk penelitian lanjutan dengan optimasi arsitektur dan integrasi teknik transfer learning.   Abstract An Intrusion Detection System (IDS) is a crucial component of modern network defense, detecting and responding to cyber threats quickly and accurately. This research proposes the development of a Deep Reinforcement Learning (DRL)-based IDS for cyberattack prediction and mitigation using three main algorithms, namely Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C). The CICIDS2017 dataset is used as a source of network traffic data, covering various types of attacks and normal traffic. The training environment is specifically designed to allow DRL agents to learn through direct interaction with the data, with a reward function that guides the agent to improve detection accuracy and minimize errors. The research methodology includes designing the DRL model architecture, training for 20,000 time steps, and evaluating model performance based on accuracy, precision, recall, and F1-score metrics. The experimental results show that DQN has the best performance with an accuracy of 92.39%, a precision of 91.04%, a recall of 92.39%, and an F1-score of 91.50%, followed by A2C and PPO. Confusion matrix analysis and performance visualization show that DQN excels in detecting majority and minority classes with a low number of false positives and false negatives. Theoretical discussions link the results of this study to the fundamental principles of DRL, where agents learn adaptive detection strategies to attack dynamics, and their relevance to real-world applications such as Security Operation Centers (SOCs) and adaptive firewalls. Comparisons with previous research confirm that the DRL approach can offer significant improvements over traditional IDS and supervised learning methods. This research contributes to the development of an adaptive IDS capable of real-time detection and mitigation with high accuracy. Limitations include high computational requirements and potential training instability, which opens up opportunities for further research with architecture optimization and the integration of transfer learning techniques.
Co-Authors Abdul Rasyid Ade Kurnia, Dian Adrian, Teguh Afiasari, Nur Aini Nurul Ainisa, Nurul Al Maeni, Nurul Al Muharom, Nurul Ibnu Alfudola, Mahfudz Amal Rois, Moh. Ichlasul Amalia, Rosnita Amarda, Juan Ameliana, Nikan Amer, Abdu Shobarudin Ananda, Ginaselvia Andi Setiawan Anggara, Doni Anggriani, Sulistia Anita Yuliyanti Apriliana Janatu Marwa Arif Fitriyanto, Goffar Arif Rinaldi Dikananda Arifqi, Tri Arya Gunawan Auliya, Suci Ayuni, Putri baihaqqi, Farisky Dalifah, Nurul Danar Dana, Raditya Dendy Indriya Efendi Dewi, Sophiyanti Dienwati Nuris, Nisa Dwi Prasetyo Dwilestari , Gifthera Efendi , Dendy Indriya Effendy, Dendy Indria Fachry Abda El Rahman Fadhil, Fadhil Yudistianto Faisal, Muhammad Faisal Faturrohman FAUZAN, AKMAL Fikri, Moh.Yusuf Firmansyah, Fajar Frihandiansah, Riyandi Fuadi Ahmad, Cecep Gifthera Dwilestari Gilang Perwati, Intan Hamdan Mubarok, Nabil Hartiansyah, Fernandar Dwi Hermawan, Bagus Hermawan, Ramdan Hidayah, Nurni Hidayat, Pierre Galuh Hidayattullah, Rizky Iin, Iin Iis Riyana Illahi, Asep Wahyu Indriya Efendi, Dendy Irfan Ali, Irfan Irma Purnamasari, Ade JUBAEDAH JUBAEDAH, JUBAEDAH Julianti, Okta Nur Kaslani Khaeru, Abdullah Khaerul Anam Kholifa, Nur Kurnia Putri, Dede Kusmawanti, Nisa Laelatul Azizah, Novi Lestari, Gifthera Dwi Mardiana, Ana Marta, Puji Pramudya Martanto Martanto . Marthanu, Indra Wiguna Mar’atun Sholihah, Oliffia Masjunedi, Masjunedi Maulida, Nida Muhamad Andika, Agus Muhammad Taufik Hidayat, Muhammad Muharam, Arbi Adi Muharromah, Oom Mustofa, Kafit Nining Rahaningsih Nugraha, Rifqi Nugroho, Rizwar Adi Nur Amalia, Ocsana Nur Apriliani, Nur Nurdin Nurhayah, Nurhayah Nuri Nuri Nurjanah, Nurul Nurliana, Nicky NURUL AZIZAH Nurwanda, Nurwanda Nurzaman Nurzaman Odi Nurdiawan Oktaviany, Nurul Optarina, Yasni Pajri, Riki Peni Peni Pii, Iwan Pratama, Denni Pratiwi, Intan Pratiwi, Yulita Prihartono, Wiily Prihartono, Willy PUJI LESTARI Purnamasari, Ade Irma Purnamasari, Ade Irma Purnamasari Putri Nabilla Putriana, Puput R, Nining Raditya Danar Dana Rahaningsi, Nining Ramdani, Rizki Retnasari, Peni Rinaldi Dikananda, Arif Rinata, Ustri Ani Rini Astuti Rohendi, Ghina Fitria Rohman, Dede Rokhmatan Khaerullah, Rizal Sajidan, Dzikri Samodra Anugrah, Syawal Saniyah, Nilta Saputra, Adi Zulkarnaen Sariah Sariah Sayuti Hanapiah, Neneng Sidik, Rahmat Siti Nurhasanah Solihudin, Dodi Suarna, Annisa Annastia Sukma, Siti Hatmara Susana, Heliayanti Susana, Heliyanti Talia, Agita Hany Tati Suprapti Taulani, Taulani Tri Ginanjar Laksana Triawan, Eri Triya, Pita Widiya, Putri Wirdiyan, Farhan Azfa Wulandari, Maryam Yudhistira Arie Wijaya Zaelani, Nursehan