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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Eksplora Informatika Infotekmesin Bubungan Tinggi: Jurnal Pengabdian Masyarakat CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Sewagati
Ary Mazharuddin Shiddiqi
Informatics Department, Institut Teknologi Sepuluh Nopember, Surabaya
Religion Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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APPLIED MACHINE LEARNING IN LOAD BALANCING Junaidi Junaidi; Prasetyo Wibowo; Dini Yuniasri; Putri Damayanti; Ary Mazharuddin Shiddiqi; Baskoro Adi Pratomo
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a940

Abstract

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be.
GCRFP - PAGE REPLACEMENT FOR SOLID STATE DRIVE USING GHOST-CACHE Wahyu Suadi; Supeno Djanali; Waskitho Wibisono; Radityo Anggoro; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a986

Abstract

State Drive (SSD) is an alternative to data storage that is popular today, widely used as a media cache to speed up data access to the hard disk (HDD). This paper proposes page replacement technique on SSD cache that used frequency and recency parameter, alternately. The algorithm is selected adaptively based on trace input. This method helps to overcome changes in access patterns while minimizing the number of write processes to SSD. The proposed algorithm can choose a replacement technique that suits the user access pattern so that it can bring a better hit rate. The proposed algorithm is also integrated with the ghost-cache mechanism so that the reduction in the number of writing processes to SSD is significant. The experiment runs using a real dataset, describing trace of data read, and data write taken from real usage. The trial shows that the proposed algorithm can give good results compared to other similar algorithms.
EMERGENCY PROCESSES HANDLING IN URBAN AREA USING MODIFIED DIJKSTRA METHOD Prima Wiratama; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1047

Abstract

Emergency Aid has a very vital role in saving the patient's life. The emergency process involves two stages, namely the pre-hospital and the hospital stage. Striving for the entire emergency process is to have the fastest response time. The initial part of emergency treatment (pre-hospital) is determining the shortest and fastest route to the hospital. In addition, the availability of the targeted hospital must also be considered. We modified Dijkstra's Algorithm to produce the shortest route and the fastest time by considering the availability of the targeted hospital to support the handling of the emergency process. The modification made to the Dijkstra algorithm replaces the weight of Dijkstra's distance with a quantity representing the congestion rate and distance. Besides, the event time is estimated to determine the status of the intended hospital. As a result, Dijkstra's modification method can produce a more efficient and faster route.
MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM Hazna At Thooriqoh; M. Naufal Azzmi; Yoga Ari Tofan; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1054

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.
AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD Doni Putra Purbawa; Azzam Jihad Ulhaq; Gusna Ikhsan; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 1, January 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i1.a1060

Abstract

SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%.
DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT Muhammad Faisal Fanani; Umi Laili Yuhana; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1238

Abstract

The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance. 
A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting Ary Mazharuddin Shiddiqi; Bagaskoro Kuncoro Ardi; Bilqis Amaliah; I Komang Ari Mogi; Agung Mustika Rizki; Bintang Nuralamsyah; Ilham Gurat Adillion; Moch. Nafkhan Alzamzami
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1264

Abstract

Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.
Multi-task Temporal Deep Learning Model for Real Time Intrusion Detection System Christian Budhi Sabdana; Noriandini Dewi Salyasari; Izra Noor Zahara Aliya; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1446

Abstract

The rapid expansion of Internet of Things (IoT) ecosystems has enabled large-scale interconnected smart environments while simultaneously exposing IoT devices to increasingly sophisticated cyber threats. To address these challenges, machine learning and deep learning–based intrusion detection systems (IDS) have been widely adopted; however, many existing approaches suffer from limited generalization, insufficient temporal modeling, and poor performance under extreme class imbalance. In this study, we investigate a multi-task stacked Long Short-Term Memory (LSTM) architecture for IoT intrusion detection, where binary anomaly detection and multi-class attack classification are jointly learned within a unified temporal framework. The proposed model examines different inter-path knowledge transfer mechanisms, including additive, gated, and attention-based aggregation, to enhance discriminative attack representation learning. A topology-constrained shuffling strategy is further introduced to preserve intra-flow temporal dependencies while reducing reliance on fixed traffic ordering. Experimental results on the Edge-IIoTset dataset show that all models achieve high binary detection performance (F1-score above 97%), while attention-based aggregation consistently outperforms static fusion strategies for multi-class classification, yielding superior macro F1-score and AUC-PR under severe class imbalance. These findings emphasize the importance of context-aware information sharing and temporal structure preservation for robust and adaptive IoT intrusion detection systems.
ECO-FISH: Enhanced Cloud Task Scheduling Using an Opposition-Based Artificial Fish Swarm Algorithm Shiddiqi, Ary Mazharuddin; Ciptaningtyas, Henning Titi; Leonardo, Jonathan; Rahma, Fayruz
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5340

Abstract

The rapid expansion of cloud computing has increased the complexity of task scheduling and resource management across heterogeneous and dynamic environments. Conventional heuristic methods often suffer from premature convergence, resulting in imbalanced virtual machine (VM) utilization. To address these challenges, this study proposes ECO-FISH, a hybrid Opposition-Based Artificial Fish Swarm Algorithm (AFSA) designed for efficient cloud task scheduling. AFSA is selected for its swarm intelligence behaviors—prey, follow, and swarm—which enable effective local exploration with relatively low computational cost. To enhance global exploration, Opposition-Based Learning (OBL) is incorporated by evaluating opposite task–VM mappings, allowing the algorithm to escape local optima and maintain population diversity. This synergy improves the balance between exploration and exploitation while retaining algorithmic simplicity. The proposed ECO-FISH algorithm is implemented using CloudSim and benchmarked against GA, PSO, and the baseline AFSA using three workload distributions: uniform, normal, and stratified. Experimental results demonstrate that AFSA alone reduces makespan by 28–45%, increases throughput by 34–84.9%, and improves utilization by 44.12–64.59% compared to GA. The OBL enhancement in ECO-FISH provides additional gains of up to 1.6%, showing the most significant improvement under heterogeneous, stratified workloads with high variance. Overall, AFSA performs well on uniform datasets, while ECO-FISH (AFSA with OBL) exhibits superior adaptability and stability in variable cloud environments.
Repolink: A Repository Driven Technique for Reconstruct-ing Missing Links in Business Process Model Kristina , Kristina; Shiddiqi, Ary Mazharuddin; Siahaan, Daniel Oranova; Forca, Adrian
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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

Abstract

Background: The development of modern organization emphasizes the importance of accurate and comprehensive business process models (BPMs). BPMs serves to provide clear work standards for business actors. Business Process Model and Notation (BPMN) is widely used to model and analyse business processes. However, BPM models in practice often contain missing or inconsistent control-flow links, which reduce model correctness and limit effective analysis. Existing BPM retrieval approaches mainly focus on similarity measurement and provide limited support for explicit missing-link reconstruction. Objective: This study aims to propose a repository-driven approach to detect and reconstruct missing control-flow links in BPMN models while preserving computational efficiency and explainability. Methods: This study employs a quantitative experimental methodology on the use of an application called Repolink., a graph-based technique that transforms BPMN models into directed graphs and computes structural similarity values using Graph Edit Distance combined with semantic weighting. A query BPMN model is compared against a repository of reference BPMN models to identify structural inconsistencies. Missing links are detected using adjacency comparison supported by forward and reverse mappings. Results: The results show that Repolink can detect and reconstruct missing control-flow links in various BPMN structures, including branching and loop-related patterns. It is also able to significantly generate efficient retrieval with an overall time complexity of , where  is the number of nodes and  is the number of repository models. Compared to existing methods, Repolink provides higher explainability by explicitly reporting missing edges. Conclusion: Repolink effectively supports missing-link reconstruction in BPMN models through a repository-driven and explainable approaches. While the method focuses on structural analysis rather than full behavioural semantics, it offers a practical solution for BPMN conformance checking and model debugging.   Keywords: Information Retrieval, Diagram Similarity, Structural Semantic, Graph Edit Distance, Greedy Algorithm
Co-Authors A. Zainal Abidin Agung Mustika Rizki Al Kanza, Kalyana Putri Alzamzami, Moch. Nafkhan Ano Rangga Rahardika Aris Tjahyanto Arsyad, Hammuda Arvina Anggie Kharizma Azzam Jihad Ulhaq Bagaskoro Kuncoro Ardi Bagus Jati Santos Baskoro Adi Pratomo Baskoro Adi Pratomo Bayu Arnel Premdan Afristo Bilqis Amaliah Bintang Nuralamsyah Budi Triyono Christian Budhi Sabdana Daniel Oranova Siahaan Dedy Yanto Dian Saptarini Diani, Nabila A'idah Dini Yuniasri Dion Devara Aryasatya Doni Putra Purbawa Emerson Eridiansyah Z Fadhila, Farah Dhia Faiz Ainun Karima Faosan Mapa Fayruz Rahma Fikriansyah, Irsyad Forca, Adrian Gusna Ikhsan Hazna At Thooriqoh Henning Titi Ciptaningtyas Henry Pratama Hudan Studiawan I Komang Ari Mogi I. D. A. A Warmadewanthi Ilham Gurat Adillion Izra Noor Zahara Aliya Joses, Steven Junaidi Junaidi Kristina , Kristina Leonardo, Jonathan M. Naufal Azzmi Mardianto, Ricky Moch. Nafkhan Alzamzami Muhammad Faisal Fanani Nadia Sephia Audina Nanik Suciati Noriandini Dewi Salyasari Nuniek Fahriani Nuniek Fahriani, Nuniek Pradana, Mares Pramudya, Rafli Raihan Prasetyo Wibowo Pratama, Rivanda Putra Prima Wiratama Prinandika, Arya Gading Putri Damayanti Putu Ayu Sinthia A. Quinevera, Stefanie Radityo Anggoro, Radityo Rakhmadany Primananda Royyana M. Ijtihadie Royyana Muslim Ijtihadie Santoso, Bagus Jati Santoso, Bagus Jati Supeno Djanali Teja, Andika Rahman Tohari Ahmad Victor Hariadi Vinorian, Muhammad Ersya Wahyu Suadi Waskhito Wibisono Waskitho Wibisono Yoga Ari Tofan Yudhi Purwananto Yuhana, Umi Laili Yulvida, Donata Zaini, Alfa Fakhrur Rizal