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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Journal of Indonesian Tourism and Development Studies Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Informasi
Devi Fitrianah
Fakultas Ilmu Komputer, Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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Exploring the recurrent and sequential security patch data using deep learning approaches Alam, Falah Muhammad; Fitrianah, Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4160-4171

Abstract

The ever-changing nature of vulnerabilities and the intricacy of temporal connections make the classification of security patch data, both sequential and recurrent, a formidable challenge in cybersecurity. The goal of this research is to improve the efficacy and precision of security patch management by optimizing deep learning models to deal with these issues. In order to assess their performance on the PatchDB dataset, four models were used: recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM). Metrics like F1-score, area under the receiver operating characteristic curve (AUC-ROC), recall, accuracy, and precision were used to evaluate performance. When it came to processing sequential data, the GRU model was the most efficient, with the best accuracy (77.39%), recall (65.63%), and AUC-ROC score (0.8127). With a 75.17% accuracy rate and an AUC-ROC score of 0.7752, the RNN model successfully reduced false negatives. With AUC-ROC scores of 0.7792 and 0.8055, respectively, LSTM and Bi-LSTM had better specificity but more false negatives. To improve cybersecurity operations, decrease mitigation time, and automate the classification of security updates, this study presents a methodology. To improve the models' practicality, future efforts will center on increasing datasets and testing them in real-world settings.
Analisis Rekomendasi Calon Debitur Motor pada PT.XYZ menggunakan Algortima C 4.5 Nurellisa, Lilis; Fitrianah, Devi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020742080

Abstract

PT.XYZ merupakan perusahaan jasa pembiayaan atau leasing dengan berkonsentrasi kepada pembiayaan sepeda motor. Dalam bisnisnya PT.XYZ sering sekali dihadapkan oleh masalah kredit macet atau bahkan penipuan. Hal ini dikarenakan kesalahan dalam pemberian kredit kepada calon debitur yang tidak potensial. Jika tidak ditangani hal ini tentu saja akan berdampak buruk bagi perusahaan. Perusahaan mengalami penurunan kemampuan dalam membayar angsuran pinjaman ke perbankan bahkan dapat berdampak pada kebangkrutan. Dalam hal ini PT.XYZ perlu melalukan analisis untuk menentukan calon debitur yang potensial dengan menggunakan data driven method atau pendekatan berbasis kepada data. Yaitu pengambilan keputusan dengan melihat data pengajuan kredit yang pernah ada sebelumnya yang disebut juga sebagai supervised learning. Algoritma yang digunakan adalah algoritma C4.5 karena algoritma ini dapat mengklasifikasi data yang sudah ada sebelumnya. Dengan algoritma ini akan dihasilkan sebuah pohon keputusan yang akan membantu PT.XYZ dalam pengambilan keputusan. Dengan pengujian menggunakan 3587 sampel data pengajuan kredit dalam kurun waktu 1 tahun akurasi yang didapatkan ialah 97,96%. Dengan begitu hal ini menunjukkan bahwa metode klasifikasi menggunakan algoritma C4.4 berhasil diimplementasikan dengan baik. Hal ini diharapkan dapat membantu PT.XYZ dalam merekomendasikan calon debitur yang potensial. AbstractPT. XYZ is a finance or leasing service company by concentrating on motorcycle financing. In its business, PT. XYZ is often faced with problems of bad credit or even fraud. This is due to an error in giving credit to potential debtors. If it is not handled this, of course, will have a bad impact on the company. Companies experiencing a decline in the ability to repay loan installments to banks can even have an impact on bankruptcy. In this case, PT. XYZ needs to do an analysis to determine potential debtors using data-driven methods or data-based approaches. That is decision making by looking at credit application data that has never been before, which is also called supervised learning. The algorithm used is the C4.5 algorithm because this algorithm can classify pre-existing data. With this algorithm, a decision tree will be produced that will help PT. XYZ in decision making. By testing using 3587 samples of credit filing data within a period of 1 year the accuracy obtained was 97.96%. That way this shows that the classification method using the C4.4 algorithm is successfully implemented properly. This is expected to help PT. XYZ in recommending potential debtors.
Exploring feature selection method for microarray classification Akmal, Muhammad Zaky Hakim; Fitrianah, Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5584-5593

Abstract

Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Impact of artificial light color on microgreen green spinach growth in an IoT-controlled environment Ihsan, Fadhil Azmi; Fitrianah, Devi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp619-628

Abstract

This study investigates the effect of different artificial colors red-blue and white on the growth of green spinach microgreens under an internet of things (IoT) based controlled environment and integrated sensors: DHT22 for temperature and humidity, and YL-69 for soil moisture. The experiment compared plant growth in two lighting scenarios over 10 days evaluating parameters including plant height and number of leaves. Results indicate that spinach microgreens grown under red-blue LED light achieved a slightly higher average height of 4.6cm and more leaves of 50 compared to white LED light with an average height of 4.5cm and 36 leaves. Although the difference between the two lighting conditions appears minor, a t-test was conducted to determine statistical significance. The results show that the difference in the number of leaves is statistically significant, suggesting that morphological responses particularly leaf growth take precedence over vertical steam elongation as an adaptive strategy to optimize environmental conditions.
An integration clustering and multi-target classification approach to explore employability and career linearity Intan Ghayatrie, Nadzla Andrita; Fitrianah, Devi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp189-197

Abstract

This study analyzes job placement waiting times and job linearity among female science, technology, engineering, and mathematics (STEM) graduates using clustering and multi-target classification (MTC) models. The K-means least trimmed square (LTS) algorithm, known for its robustness against outliers, was employed for clustering. With k = 2 and a trimming percentage of 30%, the model achieved a silhouette score of 77%, resulting in two distinct clusters: ideal and non-ideal. To enhance the dataset for classification, synthetic data was generated using the adaptive synthetic (ADASYN)-gaussian method. Principal component analysis (PCA) was used for visualization purposes, along with overlapping histograms, to illustrate that the synthetic data distribution closely resembled the original. For classification, a random forest (RF) model was used to predict both jobs waiting time and job linearity. Hyperparameter tuning produced an optimal model with a classification accuracy of 92%. Cross-validation (CV) confirmed the model’s robustness, with F1-micro and F1-macro scores of 94% and 93%, respectively. Results show that although women in STEM are underrepresented, 73% of the female alumni analyzed belonged to the short job waiting group. Furthermore, a strong negative correlation between GPA and job waiting time suggests that higher-GPA graduates tend to secure employment more quickly.
AI in Tourism: Profiling Indonesian Domestic Tourists for Targeted Marketing Communication Puspita, Virienia; Patria, Raden Laskarko; Fitrianah, Devi; Luthfia, Amia
Journal of Indonesian Tourism and Development Studies Vol. 13 No. 2 (2025)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jitode.2025.013.02.02

Abstract

The clustering algorithm used in this study, based on demographic, psychographic, travel preferences, travel behavior, and technology usage patterns, aims to identify Indonesian tourism consumers and classify them into clusters based on their characteristic groups. This study provides a comprehensive analysis of how artificial intelligence (AI) can contribute to enhancing tourism segmentation, offering insights for the hospitality and tourism industries in designing communication strategies tailored to the preferences of tourism consumers in Indonesia. Using survey data from 1,030 Indonesian domestic tourists, clustering techniques were applied to identify their tourism segments. The results are mapped into five clusters of the Indonesian domestic tourist. These profiles reveal clear differences between clusters in planning styles, spending patterns, and digital engagement. By translating these profiles into a conceptual model for tourism communication and empowerment, the study provides actionable strategies for tourism marketers to design more personalized campaigns, strengthen engagement, and align with sustainable tourism goals. This research contributes to the study of segmentation by integrating technology adoption and sustainability values into the profiles of travellers, while providing a systematic marketing communication framework for tourism destinations and organizations to tailor messages and services for diverse groups of tourism consumers.
Co-Authors Akmal, Muhammad Zaky Hakim Alam, Falah Muhammad Amalia Fitri Hardiyanti Aprilia, Sella Joanita Nur Ayuni, Ghebyla Najla Bodhi, Gabriel Charleen Deni Triyansyah Fitria Habibatul Hamdanah Hamdanah, Fitria Habibatul Hasana, Silviya Hisyam Fahmi Ihsan, Fadhil Azmi Intan Ghayatrie, Nadzla Andrita Luthfia, Amia Nurellisa, Lilis Patria, Raden Laskarko Puspita, Virienia Rendi Rendi, Rendi Rojali, Rojali S.Pd. M Kes I Ketut Sudiana . Sari, Wina Permana Yudho Giri Sucahyo Yudho Giri Sucahyo Yudho Giri Sucahyo