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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ipteks Terapan The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Informatika Dan Tekonologi Komputer (JITEK) pendidikan, science, teknologi, dan ekonomi Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik Jurnal Informatika Dan Tekonologi Komputer
Kurniabudi, Kurniabudi
Universitas Dinamika Bangsa
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Other

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Prediction of Indonesian Banking Stock Prices Using a Hybrid LSTM and XGBoost Model with Optuna Based Hyperparameter Optimization Admaja, Admaja; Kurniabudi, Kurniabudi; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5715

Abstract

Stock price prediction is a critical task in investment decision-making, particularly in highly volatile financial markets such as the Indonesian banking sector. While Long Short-Term Memory (LSTM) networks are effective in modeling temporal dependencies, they often fail to capture nonlinear residual patterns in financial time-series data, and their performance is highly sensitive to hyperparameter selection. To address these limitations, this study proposes a residual learning–based hybrid LSTM–XGBoost framework optimized using Optuna for predicting stock prices of major Indonesian banking stocks, namely BBCA, BBNI, BBRI, and BMRI. LSTM is employed as the base learner to model log-return sequences, while XGBoost is used to learn nonlinear residual structures from LSTM predictions. Latent embeddings extracted from the LSTM are further refined using Principal Component Analysis (PCA) to reduce redundancy and improve generalization. Hyperparameters of the LSTM, PCA, XGBoost, and calibration components are jointly optimized using Optuna with a Tree-structured Parzen Estimator (TPE) strategy. Experimental results demonstrate that the Optuna-optimized hybrid model consistently outperforms the baseline hybrid model across all datasets, achieving lower Mean Absolute Percentage Error (MAPE) values of 1.196% for BBCA, 1.67% for BBNI, 1.53% for BBRI, and 1.70% for BMRI. Additional stability analyses confirm that the proposed framework delivers stable and reliable predictions on unseen data. These findings provide a scalable hybrid forecasting framework that contributes to the development of intelligent financial decision-support systems and demonstrates the effectiveness of adaptive hybrid deep learning optimization techniques in real-world time-series prediction problems within the field of informatics.
Information Gain-Based Feature Selection and Machine Learning Classification for DDoS Attack Variant Detection in Cloud Computing Environment Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin; Mellyati, Denia Igesti Nur
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5752

Abstract

Cloud computing environments face significant security vulnerabilities from Distributed Denial of Service (DDoS) attacks, which can cause system failures and service disruptions. Despite various existing detection methods, challenges remain regarding high computational overhead and suboptimal accuracy due to redundant features in complex datasets. This study aims to identify the optimal feature subset and evaluate its impact on detection performance across multiple machine learning algorithms for multi-class DDoS variants. The research methodology employs a two-stage approach: feature selection using Information Gain (IG) to reduce 47 original features into subsets of 8, 10, 15, and 20, followed by classification using Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB) on the CICIoT2023 dataset. Experimental results demonstrate that the Decision Tree model with an optimized subset of only 8 features, primarily Inter-Arrival Time (IAT), Header_Length, and Tot_size, achieves a superior accuracy of 99.97%. While Naïve Bayes performs well in binary classification, its accuracy drops significantly to approximately 30% in multiclass settings. This study concludes that IG-based feature selection reduces computational complexity by 30-40% while maintaining high performance across 12 DDoS variants. These findings provide a practical framework for scalable and efficient intrusion detection systems suitable for real-time deployment in resource-constrained IoT-cloud environments.
PELATIHAN PENERAPAN AI AMAN DAN ETIS UNTUK SISWA/I SMA NEGERI 13 KOTA JAMBI Willy Riyadi; Fachruddin; Kurniabudi; Raka Jumersyah Pratama
Jurnal Pengabdian Masyarakat UNAMA Vol 5 No 1 (2026): JPMU Volume 5 Nomor 1 April 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2026.5.1.2715

Abstract

Tingginya tingkat adopsi Artificial Intelligence (AI) di kalangan Generasi Z telah membawa perubahan signifikan dalam metode pembelajaran. Berbagai data menunjukkan adanya peningkatan penggunaan AI untuk keperluan edukasi. Namun demikian, siswa SMA Negeri 13 Kota Jambi belum secara optimal memanfaatkan AI sebagai sarana pendukung pembelajaran yang inovatif. Di sisi lain, pesatnya pemanfaatan teknologi AI tersebut belum diimbangi dengan tingkat kesadaran yang memadai terhadap risiko keamanan siber, seperti pencurian data pribadi dan penipuan daring (phishing) yang semakin berkembang. Kondisi ini menimbulkan kesenjangan antara kemampuan pemanfaatan teknologi dan literasi keamanan digital di kalangan siswa. Sebagai upaya menjawab permasalahan tersebut, kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk menyelenggarakan workshop interaktif bagi siswa SMA Negeri 13 Kota Jambi. Metode pelaksanaan kegiatan meliputi pemaparan materi, studi kasus, serta simulasi praktis guna memberikan pemahaman yang menyeluruh terkait penerapan AI secara aman dan etis. Luaran yang diharapkan dari kegiatan ini adalah meningkatnya kemampuan siswa dalam memanfaatkan AI secara inovatif dan bertanggung jawab untuk mendukung proses pembelajaran, sekaligus tumbuhnya kewaspadaan serta keterampilan praktis dalam menghadapi berbagai ancaman siber. Dengan demikian, kegiatan ini diharapkan dapat berkontribusi dalam membentuk generasi digital yang cerdas, tangguh, dan beretika
ANALISIS MINAT MAHASISWA UNIVERSITAS DINAMIKA BANGSA JAMBI DALAM PENGGUNAAN MENDELEY DENGAN MENGGUNAKAN METODE TAM Nabila Kamila Hasna; Kurniabudi Kurniabudi; Chindra Saputra
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 1 No. 1 (2021): Maret : Jurnal Informatika dan Teknologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v2i1.219

Abstract

The acknowledgment of innovation is a significant element in the supportability of a data innovation. The motivation behind this study was to decide and break down understudy acknowledgment and interest in utilizing the Mendeley application. This study utilizes an estimation model, specifically Technology Acceptance Model 3, in which the estimation model was created by Venkatesh and Bala. The model has eight factors and eight theories. Models and theories were gotten from information handling through an internet based poll comprising of 100 respondents from Mendeley application clients among Jambi Bangsa Dinamika Bangsa University understudies. The information from the respondents' outcomes were handled utilizing the Structural Equation Model (SEM) helped by SmartPLS 3 Software. The outcomes got in this study were that the TAM 3 utilized had a positive and huge impact between every factor, so it was reasoned that the Mendeley application was acknowledged by University understudies. The elements of the Jambi Nation and understudies who are keen on utilizing the Mendeley application are impacted by two principle aspects of TAM, in particular the view of value that has a positive (0.728) and critical (0.000) impact and the impression of convenience has a positive (0.613) and huge (0.000) impact so the two factors are extremely demonstrated to give positive and huge impact on interest in utilizing Mendeley.
ANALISIS KEPUASAN PENGGUNA MARKETPLACE PADA APLIKASI FACEBOOK MENGGUNAKAN METODE DELONE AND MCLEAN Putri Nawang Wulan; Kurniabudi Kurniabudi; Imam Rofi’i
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 3 No. 2 (2023): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v3i2.1838

Abstract

Trading in the modern era currently uses a buying and selling system, not directly face to face but only with one finger and in one place, not limited by space and time, which is commonly referred to as buying and selling online and online transactions in one container, namely the website and very there are many types of marketplaces as long as technology continues to develop and changes in the quality of technology continue, in this study the author wants to analyze the Facebook Marketplace with the Delone and McLean method, which has problems such as vulgar advertisements that can be accessed by minors, the sale of goods-prohibited items and there are still many users who violate the rules and conditions set by the Facebook Marketplace. The purpose of this study is to identify and analyze these problems and also to find out which factors have the most influence on user satisfaction with the Facebook Marketplace. Based on the results of processing questionnaire data from the people of Jambi City, it was found that 4 of the 9 hypotheses that had a positive and significant effect were service quality to users, use to user satisfaction, use to net benefits and user satisfaction to net benefits.
Co-Authors Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Rahim Abdul Rahim Admaja, Admaja Ahmad Heryanto Albertus Edward Mintaria Albertus Edward Mintaria Ammar panji Pratama Bedine Kerim Bedine Kerim Candra Adi Rahmat Chindra Saputra Darmawijoyo, Darmawijoyo Dede Andri Wahyudin Denia Igesti Nur Mellyati Deris Stiawan Dodi Sandra Dodi Sandra Dodo Zaenal Abidin Dr. Hendri, S.Kom., S.H., M.S.I., M.H Eko Arip Winanto Eko Arip Winanto Elvi Yanti Elvi Yanti Elvira Rosanda Erick Fernando Erick Fernando Erick Fernando B311087192 Fachruddin Febriyan Nurmansyah Harid, Harid Harris, Abdul Hendri Hendri Hendri Hendri Hendy Saryanto Herry Mulyono Ibnu Sani Wijaya Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Rofi’i Irawan, Beni Irfan, Fadhel Muhammad Jasmir, Jasmir Kurniabudi Kurnianto Basuki Lola Yorita Astri, Lola Yorita Mellyati, Denia Igesti Nur Minal Juadli Mintaria, Albertus Edward Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Arief Maulana Muhammad Rafly Ramadhan Muhammad Riza Pahlevi Nabila Kamila Hasna Nurhadi Pandapotan Siagian Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri Nawang Wulan Rahman saibi Rahmat Budiarto Rahmat Budiarto Raka Jumersyah Pratama Realensi Realensi Rilis Pebriyanti Siringo Ringo Risky Radison Nasution Ryan Sihopong Parlindungan Siregar Samsuryadi Samsuryadi Setiawan Assegaf Sharipuddin, Sharipuddin Sharipuddin, Sharipuddin Shelby Amalia Sandi Siagian, Pandapotan Suwaldo Aris Ferry Hutabarat Syamsul Arifin Syifqi, Achmad Tasya Nurdin Valensia, Vally Veronica Veronica VERONICA VERONICA WILLY RIYADI Willy Riyadi Winarno Wirmaini, Wirmaini Yudi Novianto Yudi Novianto Yundari, Yundari Zulwaqar Zain Mohtar