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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal CoreIT Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Pengabdian Pada Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pertanian Agros MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI International Journal of Community Service Learning Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Ensiklopedia of Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Agrium Zonasi: Jurnal Sistem Informasi Best Journal (Biology Education, Sains and Technology) Jurnal Teknik Informatika (JUTIF) Community Empowerment Journal of Electrical Engineering and Computer (JEECOM) Jurnal Cahaya Mandalika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Margin Eco : Jurnal Ekonomi dan Perkembangan Bisnis Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan J-COSCIS : Journal of Computer Science Community Service Jurnal Ilmiah Fokus Ekonomi, Manajemen, Bisnis & Akuntansi (EMBA) CONSEN: Indonesian Journal of Community Services and Engagement Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat JURNAL KARYA ILMIAH MULTIDISIPLIN Jurnal Agro Fabrica JIPITI: Jurnal Pengabdian kepada Masyarakat Jurnal Ragam Pengabdian INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Guntoro, Guntoro
Universitas Lancang Kuning
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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.
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.
Analisis Pengaruh Suhu terhadap Perkembangan Penyakit Bercak Daun (Cercospora sp.) pada Tanaman Edamame (Glycine max L. Merr.) di Institut Teknologi Sawit Indonesia Simbolon, Anri Arjuna; Guntoro, Guntoro
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/64h2d863

Abstract

This study aimed to determine the effect of temperature and number of conidia on the severity of leaf spot disease caused by Cercospora sp. on edamame plants. The study was conducted from April 16 to May 30, 2026, by observing environmental temperature, the number of conidia captured using spore traps, and the severity and incidence of the disease in edamame plants. The results showed that the temperature during the observation was relatively stable in the range of 29.7–31.3°C with an average of 30.55°C, while the number of conidia increased from 10 conidia at the beginning of the observation to 39 conidia at the end of the observation. The severity of the disease also increased from 0.02 to 0.60, while the incidence of the disease increased from 65.0% to 100% in early May 2026. The results of multiple linear regression analysis showed that temperature had no significant effect partially on disease intensity, while the number of conidia had a significant and more dominant effect in increasing disease severity. Simultaneously, temperature and the number of conidia significantly influenced the disease intensity with an R Square value of 0.567. Thus, the number of conidia is the main factor influencing the development of Cercospora sp. leaf spot disease in edamame plants.
PENDAMPINGAN PERHITUNGAN MAWARIS DENGAN APLIKASI ANDROID PADA PENGURUS DAN JAMAAH MASJID PARIPURNA AL MUHAJIRIN PEKANBARU Guntoro Guntoro; Lasri Nijal
Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada masyarakat Vol. 4 No. 1 (2021): Jurnal Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : Pusat Pengabdian kepada Masyarakat LP2M UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jak.v4i1.10845

Abstract

AbstrakMasjid selain tempat untuk ibadah juga berfungsi untuk menimba ilmu dengan para ustad maupun ustazah dengan dijadwalkan oleh pengurus masjid. Masjid al muharin yang terletak di jalan Umban sari atas Kecamatan Rumbai termasuk masjid paripurna yang dinilai sangat baik oleh pemerintah kota pekanbaru. Diantara permasalahan yang banyak terjadi adalah berkenaan dengan hak waris, rasa ego dan nafsu manusia membuat banyak anggota keluarga gelap mata tanpa perdulikan perasaan kekeluargaan. Mengatasi permasalahan tersebut perlunya melakukan pendampingan pelatihan mawaris dengan menggunakan aplikasi Android bagi pengurus dan jamaah Masjid paripurna Al Muhajirin Pekanbaru. Adapun tahapan pendampingan ini adalah melakukan pretest, pelatihan, evaluasi dan posttest. Hasil dari pendampingan yang telah dilakukan menunjukkan, bahwa tingkat pemahaman masyarakat tentang penghjitungan mawaris masih rendah yaitu sebesar 17.0%, Setelah mengikuti pendampingan,  terjadi kenaikan pemahaman peserta dalam melakukan pelatihan sebesar 76.0 % , sehingga tingkat pemahaman peserta meningkat sebesar 93.0 %, AbstractApart from being a place of worship, the mosque also functions to gain knowledge of ustads and ustazahs, which are planned by the management of the mosque. Al Muharin Mosque, located on Umban Sari Street above the Rumbai District, is a complete mosque considered very good by the Pekanbar City Government. Among the problems that often occur are with regard to inheritance rights, the sense of ego and human desire makes many family members have dark eyes regardless of family feelings. Overcoming these problems, Mawaris training assistance must be provided through the use of an Android application for the Al Muhajirin Pekanbaru Mosque administrators and congregations. The stages of this assistance shall be pre-test, training, evaluation and post-test. The results of the assistance provided show that the level of understanding of the community in calculating the Mawaris  is still low at 17%. After participating in mentoring, the level of understanding of the participants increased to 93% and because the level of understanding of the participants in training increased 76%.  
Co-Authors Abdullah Abdullah Adriano, Mika Fauzan Ahmad Zamsuri, Ahmad Alfarasy, Febrizal Anto Ariyanto Antonius Fernando Bakar, Juhaida Abu Bayu Febriadi, Bayu Bimby, Novia Putri Budia Misri Budianto Hamuddin Budiastuti, Susanti Costaner, Loneli Costaner David Setiawan David Setiawan, David Djunaedi Djunaedi Elfrida Ratnawati Fenty Widya Hamzah Eteruddin Hamzah Hamzah Hari Gunawan Herni Utami Rahmawati Hidayat Hidayat Hutabarat, Charles Parmonangan Idel Waldelmi idel waldelmi, idel Istiatin, Istiatin Jeni Wardi Johar, Olivia Anggie Khaira, Ulfa Lasri Nijal Latifa Siswati Lisnawita Lisnawita Lisnawita Lisnawita Loneli Costaner Lubis, Ahmad Fahmi Alhafiz Maisarah Makhrani Sari Ginting Mariza Devega Maulina, Viny Meilano, Dimas Mhd. Arief Hasan, Mhd. Arief Monika, Winda Monika Muhamad Sadar, Muhamad Muhammad Fikri Muhammad Yusuf Dibisono Mulyara, Budi Musfawati Mustakim Mustakim Novia Putri Bimby Nurhamin Nurhamin Nurholidan Siregar Nurholidan Siregar Nurul Hasanah Pandu Pratama Putra, Pandu Pratama Putri, Riska Adelina Rahmad Dian Rahmad Syah Putra Rasli, Ruziana Mohamad Ratu Mutiara Siregar Riki, Herkulanus Rina Maharany Ririn Sari Wati Rizky Octa Putri Charin Roosmawati, Febriana SANTOSO SANTOSO Sapiri, Muhtar Sasi Utami Simbolon, Anri Arjuna Simorangkir, Jansihar Sinaga, Anisyah Sri Utaminingsih Sudarwati Sudarwati Suhardi Suhardi Sunaryanto, Hadi Sutejo Sutejo Tarigan, Yudi Prananta Taufik, Kemal Wagino Wenni Syafitri Wenny Syafitri Wisard Widsli Kalengkongan Yuhelmi Yuhelmi Yusuf Dibisono, Mhd zamzami Zamzami, Zamzami Zulham Effendi