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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Multica Science and Technology Brilliance: Research of Artificial Intelligence Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Pengabdian Masyarakat Bangsa Jurnal Malikussaleh Mengabdi
Ulya, Athiyatul
Universitas Malikussaleh, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Strategi Pemberdayaan Pemuda dalam Edukasi Literasi Digital untuk Meningkatkan Ketahanan Masyarakat terhadap Penyebaran Berita Hoax Cut Ita Erliana; Muthmainnah Muthmainnah; Athiyatul Ulya; Anni Zulfia; Annisa Karima; Mutammimul Ula
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 4 (2026): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i4.4560

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengimplementasikan strategi pemberdayaan pemuda melalui edukasi literasi digital dalam rangka meningkatkan ketahanan masyarakat terhadap penyebaran berita hoaks. Permasalahan utama mitra adalah rendahnya kemampuan literasi digital, khususnya dalam melakukan verifikasi informasi serta kecenderungan menyebarkan informasi tanpa pengecekan sumber yang memadai. Kegiatan ini dilaksanakan dengan pendekatan pemberdayaan partisipatif yang mengintegrasikan metode case-based learning, peer-to-peer education, pelatihan literasi digital, pendampingan, serta pembentukan komunitas “Duta Anti-Hoaks”. Pelaksanaan kegiatan dilakukan melalui tahapan persiapan, pelatihan, pendampingan, dan evaluasi menggunakan pretest dan posttest untuk mengukur peningkatan kompetensi peserta. Hasil kegiatan menunjukkan adanya peningkatan kemampuan literasi digital pemuda dalam mengidentifikasi hoaks, melakukan verifikasi informasi, serta penggunaan alat pemeriksa fakta (fact-checking tools). Selain itu, terbentuk komunitas “Duta Anti-Hoaks” dan tersusun modul literasi digital berbasis kasus lokal sebagai luaran kegiatan yang mendukung keberlanjutan program. Kegiatan ini menunjukkan bahwa strategi pemberdayaan pemuda melalui integrasi case-based learning dan peer-to-peer education efektif dalam meningkatkan literasi digital serta memperkuat ketahanan masyarakat terhadap penyebaran berita hoaks di tingkat komunitas.
Prediction of Crime Cases in 2025 in India Using the Fuzzy Time Series Chen Model Method Annisa karima; Anni Zulfia; T. Sukma Achriadi Sukiman; Athiyatul Ulya
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5745

Abstract

India's natural beauty and culture, which attract the attention of international tourists, are less able to increase tourist visits due to high crime cases. Tourists' fear of visiting the country has a direct impact on decreasing economic turnover, so the local economy has become very low. Predictions of criminal cases aim to provide an overview of cases that will occur in the next period, therefore the government can take appropriate policies to reduce crime cases. These predictions enable policymakers to plan strategic and data-based preventive measures. The method used is the Fuzzy Time Series Model Chen, because this method can overcome data uncertainty, and offers simplicity and ease in application. Valid and credible criminal statistics data in India is obtained from the site www.kaggle.com. A trusted platform that provides various quality datasets. This data will be used as a basis for the analysis and prediction of criminal cases in India. The results of this research show that in the range of 60 months from January 2020 to December 2024 using the Fuzzy Time Series Chen Model method to predict the number of criminal cases in India produced predictions in January 2025 with cases of 188.36 cases with a MAPE error ratio of 9.08% which is included in outstanding forecasting category.
Crowdsourced Facility Issues Reporting System: A Case Study in Higher Education Institution Athiyatul Ulya; Annisa Karima; Liza Afra; Annisya Febriana Lubis; M. Arsaly Athaaf Neutron
Brilliance: Research of Artificial Intelligence Vol. 6 No. 4 (2026): Brilliance: Research of Artificial Intelligence, Article Research November 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i4.9684

Abstract

Higher education institutions (HEIs) face persistent inefficiencies in facility management due to reliance on manual, paper-based reporting mechanisms, resulting in communication bottlenecks, delayed maintenance, and a lack of traceability. Although crowdsourcing has proven effective in urban civic management, its application within the micro-environment of a university campus remains underexplored. This study aims to design, develop, and evaluate a web-based Crowdsourced Facility Issues Reporting System tailored for HEIs, utilizing a case study at the Faculty of Engineering, Malikussaleh University. Employing a Research and Development (R&D) approach, the system was developed iteratively using the Prototyping method, with requirements derived from interviews and observations analyzed via thematic analysis. The system was implemented using PHP and MySQL, featuring a three-tier role-based access control architecture (Reporter, Faculty Admin, and Bureau Admin). Functional evaluation through Black Box Testing confirmed that all system modules operated according to specifications. A key finding from the implementation is the efficacy of a "Tiered Triage" workflow model, where the Faculty Admin acts as an intermediary verification node. This hierarchical filtration successfully filters invalid or hyper-local issues before they reach the central bureau, preventing administrative overload. Ultimately, the system transforms passive facility users into active stakeholders by providing real-time status tracking, thereby restoring transparency, reducing manual data entry errors, and offering a scalable digital model for facility management in complex institutional bureaucracies.
Comparison of Support Vector Machine Optimization Methods for Chili Pepper Nutrient Deficiency Identification Ilham Sahputra; Usnawiyah Usnawiyah; Athiyatul Ulya; Sirril Muna; Ahmad Rizki
Brilliance: Research of Artificial Intelligence Vol. 6 No. 4 (2026): Brilliance: Research of Artificial Intelligence, Article Research November 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i4.9730

Abstract

Deficiencies of macronutrients such as nitrogen, phosphorus, and potassium in chili pepper plants can cause visible changes in leaf characteristics, which can be utilized as information for identifying plant conditions through digital images. The performance of the Support Vector Machine in classifying leaf conditions is highly influenced by its hyperparameter configuration; therefore, an appropriate optimization strategy is required. This study aims to compare the performance of four Support Vector Machine optimization methods, namely Grid Search, Particle Swarm Optimization, Principal Component Analysis, and Adaptive Boosting, combined with RGB color features and Gray Level Co-occurrence Matrix texture features to classify four conditions of chili pepper leaves: healthy, nitrogen-deficient, phosphorus-deficient, and potassium-deficient leaves. The classification was performed using two image resolutions, namely 120 × 120 and 244 × 244 pixels. This study employed a quantitative approach using 1,040 chili pepper leaf images obtained from primary data and secondary datasets from Kaggle and Roboflow. The research process consisted of data augmentation, feature extraction, Support Vector Machine modeling, and evaluation using accuracy, precision, recall, and F1-score. The experimental results showed that the Particle Swarm Optimization configuration achieved the highest accuracy, reaching 86.06% for 244 × 244 pixel images and increasing to 91.83% for 120 × 120 pixel images. It was followed by Grid Search, which achieved 84.13% and 90.38%, respectively, while Principal Component Analysis and Adaptive Boosting achieved lower accuracy at both image resolutions. This study concludes that the combination of RGB and Gray Level Co-occurrence Matrix features with Particle Swarm Optimization provides the best classification performance for identifying nutrient conditions in chili pepper plants based on leaf images. The findings also indicate that image resolution affects classification accuracy differently across optimization methods.
Co-Authors Ahmad Muklason Ahmad Rizki Anni Zulfia Annisa Karima ANNISA KARIMA Annisya Febriana Lubis Cut Ita Erliana Cut Ita Erliana Effan Fahrizal Emi Maulani Eri Saputra Fakhruddin Ahmad Nasution Feby Artwodini Muqtadiroh Ilham Sahputra Khairul Amna, Khairul Liza Afra Liza Afra M. Arsaly Athaaf Neutron Muharratul Mina Rizky Muliana Muliana Mutammimul Ula Mutammimul Ula Muthmainnah Muthmainnah Rafika Rahmawati Rahma Fitria Rinaldi Mirsa Rizky Putra Fhonna Sahputra , Ilham Sirril Muna Soraya Masthura Hassan Sukiman, T. Sukma Achriadi T. Sukma Achriadi Sukiman Usnawiyah Usnawiyah Veri Ilhadi Zalfie Ardian Zarira Reri Najiha Zufia, Anni Zulfia , Anni