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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Penelitian Pendidikan IPA (JPPIPA) Dinamisia: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Archive: Jurnal Pengabdian Kepada Masyarakat J-COSCIS : Journal of Computer Science Community Service CONSEN: Indonesian Journal of Community Services and Engagement JURNAL KARYA ILMIAH MULTIDISIPLIN INOVTEK Polbeng - Seri Informatika Journal of Community Outreach in Science & Society
Loneli Costaner
Fakultas Ilmu Komputer, Universitas Lancang Kuning
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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A Parallel Comparative Multi-Scenario Framework For Diabetic Retinopathy Detection Using Three-Tiered Feature Selection Loneli Costaner; Nor Hazlyna Harun
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/xtfckd08

Abstract

Early detection of Microaneurysms (MAs) is vital for diagnosing Diabetic Retinopathy, yet standard deep learning models often struggle with high false-negative rates and overfitting on limited medical datasets. Objective: This study proposes a Parallel Comparative Multi-Scenario Framework to identify the most robust configuration for MA detection. The framework evaluates independent 1D vectorized feature descriptors, each initialized as a high-dimensional 16,384-feature baseline, to avoid the redundancy inherent in feature fusion. Methodology: The system systematically processes six independent descriptors LBP, GLCM, Gabor, Wavelet, Fractal, and LMR across three selection tiers (Filter, Wrapper/RFE, and Embedded). These optimized vectors, reduced from the initial 16,384 dimensions to the most discriminative "Best Subsets," serve as uniform inputs for six classifiers: five traditional Machine Learning (ML) models and a proposed representation-consistent 1D-CNN architecture, resulting in 128 experimental scenarios. Results: Experimental evaluation was conducted on a balanced dataset of 740 fundus images derived from two distinct sources: the publicly available MESSIDOR dataset and a clinically acquired dataset from Hospital Universiti Sains Malaysia (HUSM). The model was trained on MESSIDOR data and subsequently evaluated on an independent HUSM test set to assess generalization performance. The results reveal a significant performance gap. The independent LBP-RFE-SVM scenario achieved the highest performance with an accuracy, recall, and precision of 91.00%. In contrast, the best Deep Learning (DL) configuration, Gabor-ANOVA-1DCNN, reached 87.00% accuracy. Notably, while the 1D-CNN maintained a "performance floor" of 60%, ML demonstrated extreme volatility, dropping to 51.00% with global statistical features. The optimal framework significantly minimized the False Negative Rate (FNR) to 6.76%, missing only 5 out of 74 cases.
Complex Word Identification in Indonesian Children’s Texts: An IndoBERT Baseline and Error Analysis Lisnawita, Lisnawita; Bakar, Juhaida Abu; Rasli, Ruziana Mohamad; Costaner, Loneli; Guntoro, Guntoro
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5501

Abstract

Complex Word Identification (CWI) is a crucial step for building text simplification systems, especially for Indonesian children’s reading materials where unfamiliar vocabulary can hinder comprehension. This study formulates token-level CWI for Indonesian children’s texts and establishes two baselines:  an interpretable rule-based model using linguistic features e.g., length, syllable heuristics, and affix patterns, and an IndoBERT model fine-tuned for token classification. This study construct and annotate a children’s text corpus and evaluate both approaches using standard classification metrics. On the test set (22.584 tokens), IndoBERT achieves an F1-score of 0.9972 for the CWI class, substantially outperforming the rule-based baseline (F1 = 0.8607). The IndoBERT system makes only 39 errors (23 false positives and 16 false negatives), indicating near-perfect performance under the evaluated setting. Furthermore, this study provides an error analysis to highlight remaining failure patterns and borderline cases that are difficult even for contextual models. The resulting benchmark and findings contribute to Informatics/Computer Science by providing a strong baseline and analysis for educational NLP in a low-resource language setting, supporting the development of Indonesian child-oriented NLP resources and downstream text simplification tools.
Empowering Vocational School Students Through Digital Security Training to Prevent Cyber Threats: A Case Study at SMKN 7 Pekanbaru : Pemberdayaan Siswa SMK Melalui Pelatihan Keamanan Digital untuk Mencegah Ancaman Siber: Studi Kasus di SMKN 7 Pekanbaru Guntoro Guntoro; Lisnawita Lisnawita; Winda Monika; Loneli Costaner
CONSEN: Indonesian Journal of Community Services and Engagement Vol. 6 No. 1 (2026): Consen: Indonesian Journal of Community Services and Engagement
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/consen.v6i1.2326

Abstract

Digital devices now form the backbone of nearly every classroom, yet that convenience comes tangled with new cybersecurity peril. Students in vocational tracks sit at the crossroads: they click through learning modules all day but rarely receive targeted instruction on how to keep themselves safe online. Without that practical know-how, the hallways of a single school can quietly accumulate risks like data leaks, identity theft, and rogue software. In response, the present study piloted a campus-based workshop designed to meet learners exactly where they are. Courses were delivered at SMKN 7 Pekanbaru, involving thirty trade students who volunteered despite their busy schedules. Lectures spoke in plain language; hands-on exercises replayed incidents pulled from local news; quick-fire quizzes and spirited group debates stitched it all together. Student mastery was quantified by side-by-side snapshots taken before and after the event, measured against five essential security benchmarks. The opening average sat at a modest 18.7 out of 25; the closing number soared to 24.4. A paired t-test for the twenty-nine complete sets of data returned t(29) = 13.25, p < 0.0001, clearly ruling out chance. Glance at the run charts and the upward drift is obvious: every learner moved forward, and the room buzzed with confidence that had been absent hours earlier. Recent research confirms that focused, brief cybersecurity workshops can significantly boost learners grasp of online threats and the defensive habits they employ. Because the instructional framework proved practical, other institutions are well-positioned to adopt it and thereby reduce the cyber vulnerabilities that affect campus communities.
Optimization of LBP Texture Feature Extraction using Correlation And Mi For SVM-Based Diabetic Retinopathy Classification loneli costaner; lisnawita lisnawita; Guntoro Guntoro
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4vrj4930

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness, making early detection based on retinal fundus images crucial. This study proposes a DR classification method with a primary contribution in feature optimization: integrating the LBP Contrast feature with a Local Binary Pattern (LBP) histogram and performing hybrid feature selection based on Mutual Information (MI) to assess relevance and correlation analysis to reduce redundancy. This method was tested using 168 images from the public Messidor dataset, with 100 images for training and 68 for testing to evaluate performance. Classification was performed using a Support Vector Machine (SVM) with a linear kernel, where model performance was evaluated before and after optimization to measure the significance of the improvement. The results showed a significant improvement after optimization, with accuracy increasing from 88% to 94%, recall increasing from 88% to 100%, and F1-score increasing from 0.92 to 0.96. Although precision decreased slightly from 96% to 93%, increasing recall to 100% is considered more crucial in a medical context as it minimizes the risk of missed positive cases. These findings confirm that the proposed feature optimization approach can significantly improve the accuracy and reliability of the DR detection system, offering potential clinical relevance for supporting early intervention.
Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset Guntoro Guntoro; Lisnawita Lisnawita; Loneli Costaner
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5382

Abstract

The growing number of Internet of Things devices has led to an increased risk of complex and diverse cyberattacks. However, a significant challenge in this domain is the imbalanced class distribution in most Internet of Things datasets, cautilizing classification algorithms to be biased towards the majority class, hindering effective threat detection. This study addresses this issue by leveraging the Random Forest algorithm optimised by the Synthetic Minority Oversampling Technique. This research aims to develop an effective model for detecting cyberattacks in Internet of Things environments by resolving class imbalance issues inside of the CIC-IoT2023 dataset. The methodology involves several stages, comprising data preprocessing and applying Synthetic Minority Oversampling Technique for data balancing. The balanced dataset was then used to train a Random Forest model, by its performance evaluated utilizing accuracy, precision, recall, F1-score, and Cohen's Kappa metrics. The results demonstrate the model's effectiveness, achieving an accuracy of 99.01%, an F1-score of 98.96%, and a Cohen's Kappa of 98.92%. This marks a notable improvement in performance, particularly in detecting minority classes, compared to the model trained devoid of Synthetic Minority Oversampling Technique, that struggled to identify several less common attack types. The outcomes suggest that combining Random Forest by Synthetic Minority Oversampling Technique can significantly enhance the development of intrusion detection systems by improving detection accuracy for all 33 attack types and reducing the risks associated by undetected threats. In conclusion, this study advances Internet of Things cybersecurity by presenting an effective and efficient method for addressing data imbalance in attack detection. Future research should focus on evaluating the model's robustness utilizing more complex datasets and enhancing its performance for real-time deployment on resource-constrained Internet of Things Devices.
Transformasi Digital Rekrutmen SDM Organisasi Filantropi melalui Pelatihan Aplikasi Expert Choice Loneli Costaner; Lisnawita
JCoos: Journal of Community Outreach in Science & Society Vol. 1 No. 1 (2026): Journal of Community Outreach in Science & Society (JCoos)
Publisher : JCoos: Journal of Community Outreach in Science & Society

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

Abstract

Yayasan Khoiru Ummah merupakan organisasi pendidikan filantropi yang banyak menyerap tenaga fundraiser dan administrasi. Permasalahan utama yang dialami mitra adalah belum optimalnya sistem seleksi sumber daya manusia karena ketiadaan standar baku penentuan keputusan yang cepat dan akurat. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk melakukan transformasi digital pada sistem rekrutmen mitra melalui pelatihan penggunaan sistem pendukung keputusan menggunakan aplikasi Expert Choice. Metode pelaksanaan pengabdian terdiri dari tahap observasi, pre-test, pelatihan teori dan praktik selama delapan jam, serta post-test. Evaluasi tingkat pemahaman peserta diukur menggunakan instrumen kuesioner berskala Guttman. Hasil evaluasi menunjukkan bahwa pemahaman awal peserta terhadap metode keputusan terkomputerisasi hanya sebesar 18%. Setelah mengikuti pelatihan secara komprehensif, pemahaman peserta melonjak signifikan menjadi 97.5%. Dapat disimpulkan bahwa pelatihan aplikasi Expert Choice memberikan persentase kenaikan pemahaman sebesar 79.5% dan secara efektif membekali pengurus yayasan dalam mengambil keputusan rekrutmen yang lebih terstruktur dan efisien
Deep Learning Driven Ransomware Detection: A Bibliometric Analysis of Research Trends, Knowledge Structure, and Future Directions Guntoro Guntoro; Lisnawita Lisnawita; Loneli Costaner; Wenni Syafitri
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17163

Abstract

Ransomware has become a major cybersecurity threat because it encrypts data, disrupts services, and evolves rapidly beyond conventional signature-based detection. This study presents a bibliometric analysis of deep learning-driven ransomware detection research, with emphasis on behavioral and dynamic analysis, API-call sequences, system calls, and future research directions. A total of 481 records were retrieved from the Web of Science Core Collection; after screening one retracted publication and two editorial materials, 478 eligible records remained. The eligible corpus was analyzed using Biblioshiny and Bibliometrix. Results show rapid growth from 2022 to 2025, with IEEE Access, Computers & Security, Sensors, Scientific Reports, and International Journal of Information Security among the prominent sources. Keyword and thematic analyses indicate a shift from static detection toward behavior-aware, sequence-based, explainable, and real-time ransomware detection. The findings highlight the need for robust datasets, cross-dataset validation, low-latency inference, explainable deep learning, and integrated detection systems for practical cybersecurity deployment.
Co-Authors Abdullah Abdullah Ahmad Zamsuri, Ahmad Alfarasy, Febrizal Alyauma Hajjah Antonius Fernando Bakar, Juhaida Abu Bayu Febriadi, Bayu Chan, Andhi Darmanta Sukrianto Deny Jollyta Dwi Oktarina Guntoro Guntoro, Guntoro Gusrianty Gusrianty Gustientiedina Hadi Dwi Putra Hutri Rizkiyah Alda Johan Johan Johar, Olivia Anggie Lisnawita Lisnawita Lisnawita Lisnawita Lisnawita Lucky Lhaura Van FC, Lucky Lhaura Mariza Devega Mhd. Arief Hasan, Mhd. Arief Musfawati Mustika Mustika Nor Hazlyna Harun Pandu Pratama Putra, Pandu Pratama Rasli, Ruziana Mohamad Samsudin, Samsudin Sutejo Sutejo Wahyu Joni Kurniawan Wenni Syafitri Wenny Syafitri Yitro Amazihono Yogi Yunefri, Yogi Yuhelmi Yuhelmi