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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Infotekmesin International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknik Informatika (JUTIF) IJISTECH JAMU : Jurnal Abdi Masyarakat UMUS Moneter : Jurnal Keuangan dan Perbankan Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Megantara, Rama Aria; Pergiwati, Dewi; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Naufal, Muhammad; Brilianto, Rivaldo Mersis
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit Alzami, Farrikh; Sambasri, Fikri Diva; Nabila, Mira; Megantara, Rama Aria; Akrom, Ahmad; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Sulistiyawati, Puri
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1524.32-44

Abstract

E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Analisis Performa Class Weight Dan Focal Loss Pada Model Indobert Untuk Klasifikasi Teks Depresi Berbahasa Indonesia Rafi Jonathan Siger; Muhammad Naufal; Farrikh Alzami
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9620

Abstract

The development of depression detection can be done using exploration of social media content. However, the classification of depression indicative texts faces a major challenge in the form of class distribution imbalances, which can degrade the model's generalization capabilities. This study aims to analyze how the method of overcoming class imbalance affects the performance of the IndoBERT model in the classification of Indonesian depression indication texts by emphasizing the analysis of training stability based on the dynamics of training loss and validation loss. The dataset used consists of 3,863 data, data that has gone through the process of cleaning, removing duplicate data, tokenization, encoding, and dividing data into stratification into training data, validation data, and test data. The IndoBERT-base-p1 model was fine-tuned using three training scenarios, namely baseline, class weight, and focal loss with an early stopping mechanism based on validation loss. The test results showed that the baseline IndoBERT scenario produced an accuracy of 77.52%, a weighted precision of 0.7752, a weighted recall of 0.7752, a weighted F1-score of 0.7737, and a ROC-AUC of 0.8528 with a relatively stable training pattern. The class weight method produced an accuracy of 74.68%, a weighted F1-score of 0.7467, and a ROC-AUC of 0.8342 which showed an increase in class discrimination ability but accompanied by a decrease in overall accuracy. Meanwhile, the focal loss method produced an accuracy of 72.87%, a weighted F1-score of 0.7291, and a ROC-AUC of 0.8188 with more balanced training characteristics than the weight class. The findings suggest that handling classroom imbalances does not necessarily improve global performance, so model evaluations need to consider a balance between accuracy, sensitivity, and stability of training.
Attention-Augmented GRU for Stock Forecasting: A Trade-Off Between Directional Accuracy and Price Prediction Error R. Daniel Hartanto; Guruh Fajar Shidik; Farrikh Alzami; Ahmad Zainul Fanani; Aris Marjuni; Abdul Syukur
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15863

Abstract

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.
Co-Authors Abda Abda Abdul Syukur Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Anggi Pramunendar, Ricardus Arifin, Zaenal Aris Marjuni Aris Nurhindarto ARIYANTO, MUHAMMAD Ashari, Ayu Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Azzami, Salman Yuris Adila Brilianto, Rivaldo Mersis Budi, Setyo Candra Irawan Candra Irawan Caturkusuma, Resha Meiranadi Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Choirinnisa, Dina Dewi Agustini Santoso Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Enrico Irawan Erika Devi Udayanti Esa Wahyu Andriansyah Fahmi Amiq Farah Syadza Mufidah Fikri Diva Sambasri Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Firman Wahyudi, Firman Fitri Susanti Ghina Anggun Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Hartono, Andhika Rhaifahrizal Harun Al Azies Hasan Aminda Syafrudin Heni Indrayani Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Ika Novita Dewi Indra Gamayanto Indra Gamayanto Indrayani, Heni Iswahyudi ISWAHYUDI ISWAHYUDI Jumanto Karin, Tan Regina Khariroh, Shofiyatul Khoirunnisa, Emila Krisnawati, Dyah Ika Kukuh Biyantama Kukuh Biyantama Kurniawan Aji Saputra Kurniawan, Defri Kusmiyati Kusmiyati Kusmiyati*, Kusmiyati Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Marjuni, Aris Maulana, Isa Iant Megantara, Rama Aria Mila Sartika Mila Sartika, Mila Mira Nabila Mira Nabila Moch Arief Soeleman Moh Hadi Subowo Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Muhammad Naufal Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih MY. Teguh Sulistyono Nabila, Mira Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Nurwijayanti Pergiwati, Dewi Puji Prabowo, Dwi Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Puspitarini, Ika Dewi R. Daniel Hartanto Rafi Jonathan Siger Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Risky Yuniar Rahmadieni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, M. Hilma Minanur Ruri Suko Basuki Sambasri, Fikri Diva Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Sasono Wibowo Sejati, Priska Trisna Sendi Novianto Sendi Novianto SIGIT MURYANTO Sigit Muryanto, Sigit Sinaga, Daurat Soeleman, Arief Soeleman, M Arief Sofiani, Hilda Ayu Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyono, Teguh Sulistyowati, Tinuk Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Widyatmoko Karis Yuniar Rahmadieni, Risky Yunita Ayu Pratiwi Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati