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All Journal Jurnal Computech & Bisnis (e-Journal) Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika JUSTIFY : Jurnal Sistem Informasi Ibrahimy Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Science and Mathematics Education International Journal of Computer Technology and Science Bulletin of Intelligent Machines and Algorithms
Setiana, Elia
Universitas Informatika Dan Bisnis Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

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Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
Penerapan AI , IoT, dan CPS dalam Industri Manufaktur Menuju Efisiensi Operasional Balkhi Razzas Qasvini; Osin Lirfaija Salamanang; Muhamad Arya Ananda; Elia Setiana
JUSTIFY : Jurnal Sistem Informasi Ibrahimy Vol. 5 No. 1 (2026): JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Publisher : Fakultas Sains dan Teknologi, Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/justify.v5i1.9951

Abstract

In an era of exponential industrial transformation, the integration of advanced technologies has evolved into a key strategic asset for manufacturing organizations. Speed and accuracy in production lines and supply chain management have become critical factors in maintaining a competitive edge in volatile markets. This research article aims to critically analyze and synthesize the role of Information Technology (IT) integration—specifically Artificial Intelligence (AI), the Internet of Things (IoT), and Cyber-Physical Systems (CPS)—in mitigating operational risks and improving production process efficiency. Using a descriptive-qualitative analysis approach based on a systematic literature review, this article examines how IT infrastructure influences an organization’s production architecture. The results of the analysis show that the implementation of AI- and IoT-based IT can reduce machine maintenance costs by up to 35%, increase the accuracy of predictive maintenance to 92%, and transform operational methodologies from traditional reactive maintenance to data-driven maintenance
ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Valencia Claudia Jennifer Kaunang; Nur Alamsyah; Reni Nursyanti; Budiman Budiman; Venia R Danestiara; Elia Setiana
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
A Trigger Aware, Event Centric, and Uncertainty Calibrated Neuro-Symbolic Framework for Actionable Cyber Threat Intelligence from Indonesian Online News Elia Setiana; Muhamad Achya Arifudin; Nur Alamsyah
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i5.31

Abstract

Online news can provide timely cyberthreat signals, but duplicative reporting, fragmented event descriptions, resource-constrained Indonesian language text, and uncalibrated model confidence limit its operational use. This study presents A Trigger-Aware, Event-Centric, and Uncertainty-Calibrated Neuro-Symbolic Framework for Actionable Cyber ​​Threat Intelligence from Indonesian Online News (TRACE-CTI-ID), a proof-of-concept framework that integrates exact deduplication, event-centric clustering, trigger-aware semantic representation, neuro-symbolic fusion, ordinal risk estimation, conformal uncertainty, mitigation mapping, and an event-centric knowledge graph. The experiment used 711 Liputan6 records collected on March 14, 2025. Exact deduplication reduced the corpus to 79 unique headlines, which were automatically consolidated into 26 events. Splitting the separate events resulted in 30 training articles, 5 calibration articles, and 44 test articles with zero event leakage. The calibrated neuro-symbolic model achieved a micro-F1 of 0.283 and a macro-F1 of 0.441, outperforming the baseline TF-IDF of 0.074 and 0.013, respectively. However, ordinal severity prediction remained weak with an accuracy of 0.136, a macro-F1 of 0.138, and a mean absolute error of 2.023. Conformal coverage was also unstable, and the abstention mechanism did not direct uncertain articles to human review. These findings demonstrate the technical feasibility of the integrated pipeline while also demonstrating that silver labeled, title only, and single source data are insufficient for final operational validation. Therefore, the key contribution is a transparent, leak aware evaluation architecture and protocol that can be strengthened through full text collection from multiple sources and independent expert annotation.
Analisis Perancangan Sistem Pakar Pola Latihan Untuk Mencapai Body Goals Menggunakan UML Elia Setiana; Budiman Budiman; R. Yadi Rakhman A; M. Rizki Ramadhan
INTERNAL (Information System Journal) Vol. 6 No. 2 (2023)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v6i2.853

Abstract

Technological developments and awareness of the importance of health and physical fitness have encouraged people to look for effective solutions in achieving their desired body goals. Expert systems are one potential approach to assist individuals in designing exercise patterns that suit their goals. This system development method uses UML as a tool for analyzing and designing expert system structures. This expert system will utilize expert knowledge in the fields of fitness and nutrition to provide personalized and effective recommendations. Additionally, integration with technology will allow users to monitor their progress in real-time and receive recommendation updates according to their individual progress.The results of this research are architectural designs consisting of Usecase Diagrams, Activity Diagrams, Class Diagrams, Sequence Diagrams, Deployment Diagrams which can then be used as a reference for creating this expert system application system so that it is hoped that the complete system can become a useful tool and can make a contribution. positive in helping users achieve their body goals with a more focused and effective approach.
FORECASTING STOCK MARKET MODEL: A SYSTEMATIC LITERATURE REVIEW Elia Setiana; Kusrini Kusrini; Tonny Hidayat; Dhani Ariatmanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.7991

Abstract

The increasing digitisation of stock markets and the growing diversity of financial data sources have intensified the need for accurate, robust, and risk-aware stock market forecasting. This systematic literature review synthesises recent evidence to examine the effectiveness of forecasting methods under different data and market conditions, the characteristics of commonly used benchmark datasets, the contribution of preprocessing strategies, and the evaluation and validation practices applied in stock market forecasting. Following the PRISMA framework, 71 peer-reviewed studies retrieved from the Scopus database were systematically screened, classified, and analysed. The evidence mapping shows that sequence-based deep learning models, including LSTM, GRU, and CNN–LSTM, represent the largest methodological group at approximately 41%, followed by transformer- and attention-based approaches at around 16%. Volatility-oriented econometric and classical statistical models account for approximately 18% and 14%, respectively, while probabilistic and quantile-based approaches remain limited. The findings indicate that forecasting performance is strongly context-dependent: classical models remain effective for relatively stationary univariate series, volatility-oriented models are particularly relevant when clustering and spillover effects are present, and deep learning and transformer-based approaches are more suitable for multivariate, nonlinear, and feature-rich settings. Overall, the review highlights the need for greater integration of uncertainty-aware evaluation, regime-sensitive validation, and risk-oriented forecasting frameworks.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Akbar, Imannudin Balkhi Razzas Qasvini Budiman Budiman Budiman Chairul Habibi Danestiara, Venia R Dhani Ariatmanto Dirham Triyadi Dirham Triyadi Graha Prakarsa Hamzah, Encep Ikhsan Nur, Muhammad Kaunang, Valencia Claudia Jennifer Kusrini M. Rizki Ramadhan Marwondo Muhamad Achya Arifudin Muhamad Arya Ananda Muhammad Fajar Romadhon Muhammad Rizki Ramadhan Mukhammad Restu Febriansyah Putra Muliasari, Lina Nayla Nurul Azkiya Nugraha, Arif Bakti Nur Alamsyah Nur Alamsyah NUR ALAMSYAH Nursyanti, Reni Osin Lirfaija Salamanang R. Yadi Rakhman A R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Reni Nursyanti Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Sardjono Syahira Putri Himmaniah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga TONNY HIDAYAT Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Venia R Danestiara Venia Restreva Danestiara Wiyanudin