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All Journal INFORMAL: Informatics Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURTEKSI Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pengabdian kepada Masyarakat Nusantara Syntax: Journal of Software Engineering, Computer Science and Information Technology Djtechno: Jurnal Teknologi Informasi Devotion: Journal of Research and Community Service Jurnal Tika jeti International Journal Software Engineering and Computer Science (IJSECS) Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Cakrawala Ilmiah Journal of Innovation Research and Knowledge Prioritas : Jurnal Pengabdian Kepada Masyarakat AMPLITUDO: Journal of Science & Technology Innovation Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Journal Innovations Computer Science Journal of Advanced Computer Knowledge and Algorithms Depik Jurnal Ilmu-Ilmu Perairan, Pesisir, dan Perikanan Jurnal Pengabdian dan Inovasi Masyarakat
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Universitas Islam Kebangsaan Indonesia
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SENTIMENT CLASSIFICATION MODEL BASED ON COMPARATIVE STUDIES USING MACHINE LEARNING TECHNOLOGY PRAYOGA, J; Fajri, T. Irfan; Dristyan, Febri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7105

Abstract

The development of social media has generated large amounts of text data, which is a valuable source for sentiment analysis. This study aims to conduct a comparative study of sentiment classification models on Indonesian-language YouTube comments, specifically comparing lexicon-based approaches, traditional machine learning models (Naive Bayes), and deep learning models (LSTM). Data was collected from YouTube videos themed around the youth generation and demographic bonuses, totaling 9,162 comments that underwent comprehensive text preprocessing. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model outperforms Naive Bayes with an accuracy of 78.78% and an average F1-score of 0.79, compared to Naive Bayes, which only achieves an accuracy of 62.08% and an F1-score of 0.54. Although LSTM offers higher performance, the Naive Bayes model remains relevant due to its simplicity and efficiency. This study makes an important contribution to the selection of sentiment classification models for the Indonesian language and suggests the development of hybrid models and the use of contextual features for more optimal results. The LSTM model outperforms Naive Bayes with an accuracy of 82.15% (improved from 78.78% through enhanced regularization) and an average F1-score of 0.84. Comprehensive hyperparameter tuning via grid search and expanded manual annotation (40% of the dataset with κ=0.83) ensures robust model evaluation and reduces labeling bias. The study provides methodologically sound benchmarks for Indonesian sentiment analysis
Edukasi Internet Sehat dan Aman untuk Pencegahan Kejahatan Siber pada Remaja Fajri, T. Irfan; Selviana, Renita; Budiarto, Balla Wahyu; Eldo, Handry; Suryadi, Dikky
CivicAction: Jurnal Pengabdian dan Inovasi Masyarakat Vol. 1 No. 3 (2025): Artikel Pengabdian Kepada Masyarakat
Publisher : SORATEKNO PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/civicaction.v1i3.263

Abstract

Perkembangan teknologi informasi dan komunikasi telah mendorong meningkatnya intensitas penggunaan internet pada kalangan remaja. Di sisi lain, tingginya aktivitas digital tersebut turut memunculkan berbagai risiko keamanan, seperti penipuan daring, pencurian data pribadi, perundungan siber, penyebaran konten negatif, hingga praktik rekayasa sosial yang berpotensi mengarah pada kejahatan siber. Kondisi ini menunjukkan pentingnya program edukasi internet sehat dan aman sebagai upaya preventif untuk meningkatkan literasi digital serta membangun perilaku bermedia yang bertanggung jawab. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan kesadaran remaja mengenai keamanan berinternet, pengelolaan privasi digital, serta langkah-langkah perlindungan diri dari ancaman kejahatan siber. Metode pelaksanaan menggunakan pendekatan edukatif-partisipatif melalui penyuluhan, diskusi interaktif, simulasi kasus, serta evaluasi menggunakan pre-test dan post-test. Materi yang diberikan mencakup etika bermedia digital, identifikasi modus kejahatan siber yang umum terjadi, penguatan keamanan akun melalui kata sandi kuat dan autentikasi ganda, kewaspadaan terhadap tautan mencurigakan, serta strategi penanganan ketika mengalami insiden keamanan digital. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan peserta secara signifikan setelah mengikuti program edukasi, yang tercermin dari kenaikan skor post-test dibandingkan pre-test, serta meningkatnya kemampuan peserta dalam mengenali potensi risiko dan menentukan tindakan pencegahan yang tepat. Selain itu, remaja menunjukkan respons positif terhadap kegiatan melalui keterlibatan aktif selama sesi dan meningkatnya kesadaran akan pentingnya menjaga jejak digital. Dengan demikian, program edukasi internet sehat dan aman efektif sebagai langkah penguatan literasi digital remaja dan dapat dijadikan model kegiatan pengabdian berkelanjutan untuk mendukung pencegahan kejahatan siber di lingkungan sekolah maupun komunitas.
Mitigating Religious Radicalism and Polarization through the Integration of Artificial Intelligence (AI), Internet of Things (IoT), Blockchain and Cognitive Science Iqbal, Muhammad; Zahraini, Zahraini; Ilmi, Hayatul; Mutasar, Mutasar; Naila, Putri; Marra, Ezio
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.551

Abstract

Education have shifted into digital environments, where algorithm-driven platforms intensify extremist discourse and weaken tolerance among students. Previous studies highlight the limitations of conventional deradicalization programs, which rely on offline seminars or punitive measures and fail to address the digital and cognitive mechanisms of radicalization. To address this gap, this study investigates whether integrating Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and cognitive science can provide an effective and ethical counter-radicalization framework for universities. Guided by the hypothesis that a multidisciplinary approach combining technological detection with cognitive restructuring yields measurable psychosocial impact, a Research and Development (R&D) design was applied across six stages, involving students, faculty mentors, and expert validators in Aceh, Indonesia. The AI–NLP module, fine-tuned with local data, achieved high accuracy (precision 0.94; recall 0.89), while CBT-based cognitive microlearning increased tolerance scores by 28% (p < 0.01) and reduced risky online interactions by 40%. Findings demonstrate that integrating disruptive technologies with cognitive-behavioral methods produces both technical and attitudinal benefits. The study contributes theoretically to technology-mediated deradicalization and practically to policy-driven curriculum design, with implications for cross-cultural scalability and longitudinal research..
Internet of Things (IoT) Integration for Real-Time Monitoring in Smart Cities T. Irfan Fajri; Novi Rahayu; Wasiran; Yusuf Unggul Budiman; Novrini Hasti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3465

Abstract

The advancement of Internet of Things (IoT) technology has opened great opportunities for the implementation of real-time monitoring systems in supporting smart city management. This research aims to develop an IoT integration model that can monitor various urban aspects, such as traffic management, energy consumption, waste management, and air quality, in an efficient and integrated manner. The model is designed to collect, process, and analyze data from various IoT sensors scattered in urban areas, with a focus on delivering information in an integrated manner. urban areas, with a focus on delivering real-time information to the government and the public. The research methodology includes the development of development of an IoT-based system prototype that integrates hardware and hardware and software with the support of cloud computing architecture for data management. data management.
Analysis of Household Electricity Consumption Patterns Using K-Nearest Neighbor (KNN) Method Cut Susan Octiva; Sultan Hady; Dedy Irwan; T. Irfan Fajri; Novrini Hasti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3877

Abstract

The increasing demand for electricity in the household sector poses significant challenges to energy efficiency initiatives and environmental conservation efforts. Examining electricity usage patterns offers a pathway to uncover key determinants that influence consumption levels while formulating more effective strategies for energy management. This study attempts to evaluate electricity consumption patterns in the household sector using the K-Nearest Neighbor (KNN) algorithm. This approach is used to categorize consumption data based on attribute similarities among household units. The findings are expected to encourage more rational electricity usage practices, thereby reducing energy inefficiencies and strengthening efforts to conserve natural resources. Furthermore, the analysis aims to provide actionable insights for households to adopt sustainable habits and for policymakers to design targeted interventions that address peak demand periods and promote the use of energy-efficient technologies. By identifying specific behavioral and technological factors that contribute to high consumption, the results can serve as a basis for tailored programs aimed at minimizing waste and promoting long-term environmental management.
Integrating Zero Trust Architecture with Blockchain Technology to Maintain Data Security in the Cloud T. Irfan Fajri; Handry Eldo; Cut Susan Octiva; Dikky Suryadi; Muhammad Lukman Hakim
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5481

Abstract

Data security concerns have increasingly become a challenge to cloud computing services due to rising incidents of cyberattacks, identity theft, and data manipulation. The perimeter-based security model is ineffective because of vulnerabilities in authentication and access control, thus necessitating an adaptive layered approach. This paper presents attempts to merge Zero Trust Architecture (ZTA) with Blockchain technology as one possible way to ensure confidentiality, integrity, and availability of data in cloud environments. Research methodology comprises a detailed review of related literature, system architecture analysis, and simulation of the conceptual merger using encryption protocols and smart contracts. Results revealed that ZTA significantly reduces the opportunities for unauthorized access through multi-layered verification and least privilege principles while Blockchain provides a decentralized transparent immutable method for recording transactions on data. The hybrid will enhance security substantially against breaches from external attackers and insiders with an already established verifiable audit trail. This paper concludes that such a merger could create a stronger model—one that is more measurable—and sustainable for securing today's cloud infrastructure.
Integrasi Big Data dan AI untuk Pengambilan Keputusan dalam Smart City T. Irfan Fajri; Novi Rahayu; Handry Eldo; Giatika Chrisnawati; Rizkia Shaulita
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3860

Abstract

This research explores the integration of Big Data technology and Artificial Intelligence (AI) in decision-making in the context of Smart City. With the massive growth of data from various sources such as IoT, sensors, and information systems, Big Data is becoming an important foundation for in-depth analysis. Meanwhile, AI provides the ability to process data in real-time, identify patterns, and generate accurate recommendations. This research aims to analyze how the combination of these two technologies can improve efficiency, sustainability, and quality of life in cities. The methods used include literature review and case analysis in several smart cities. The results show that the integration of Big Data and AI can support faster, more precise, and data-driven decision-making, thus encouraging the creation of a smarter and more responsive city.
Co-Authors ., Yustizar Agustian, Febri Akbar Arafat Amri Amri Amrullah Amrullah Amrullah Budiarto, Balla Wahyu Budiman, Yusuf Unggul Chaeroen Niesa Chaeroen Niesa Cut Fadhilah Cut Fadhilah Cut Susan Octiva Cut Susan Octiva Dedy Irwan Dikky Suryadi Dikky Suryadi Eldo, Handry Emma Budi Sulistiarini Fadhilah, Cut Fadhlilah, Cut Faisal Hadi Fajri, T Irfan Febri Dristyan Giatika Chrisnawati Handry Eldo Hasma, Nur Amalia Hera Novianti Ilmi, Hayatul Irfan Fajri, T. Isfannir, Isfannir J. Prayoga, J. Prayoga Jovi Antarea Juanda Khairati, Ulfi Marra, Ezio Maryadi Maryadi Mida Wahyuni Miftahul Jannah Muazziss Najmi Muhammad Aziz Muhammad Iqbal Muhammad Lukman Hakim Muhammad Lukman Hakim, Muhammad Lukman Mursyidah, Nelly Mustaqim Mustaqim Mustaqim Mustaqim, Mustaqim Muttaqin Muttaqin Naila, Putri Niesa, Chaeroen Ningsih, Liasulistia Novi Rahayu Novia Hasdyna Novrini Hasti Nunsina, Nunsina Nur Amalia Hasma Nur Faiza Nursalim Octiva, Cut Susan Putri Ekaresty Haes Putri Raihatul Jannah Rahayu, Novi Rita, Zaitun Ritaqwin Rizkia Shaulita Rozzi Kesuma Dinata Sabrina Aulia Rahmah Salsabila, Cut Syahira Salsabila, Unik Hanifah Selviana, Renita Suharjo Suharjo Sujacka Retno Sultan Hady Suryadi, Dikky T Irfan Fajri Uli Wildan Nuryanto Uswatul Hasanah Uswatul Hasanah, Uswatul Vikki, Zakial Vivi Asbar Wahyuni, Razmi Wasiran Wasiran Welnof Satria Wenny Novita Sari Wirdatul Jannah Yusrizal Akmal Yusuf Unggul Budiman Zahraini Zahraini