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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Technomedia Journal IJISTECH (International Journal Of Information System & Technology) Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) CCIT (Creative Communication and Innovative Technology) Journal International Journal for Educational and Vocational Studies ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IJISTECH Jurnal Manajemen Retail Indonesia (JMARI) Indonesian Journal Accounting (IJAcc) International Journal of Cyber and IT Service Management (IJCITSM) IPSIKOM International Transactions on Artificial Intelligence (ITALIC) Masyarakat: Jurnal Pengabdian Journal of Computer Science and Technology Application
Rafika, Ageng Setiani
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Secure Communication in Solar Panel Monitoring Systems Using SmartPLS Ageng Setiani Rafika; Dhimas Tribuana; Rohim Rohim; Agung Rizky; Chua Toh Hua
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/gtmmg783

Abstract

This study investigates the factors shaping secure communication architecture in solar panel monitoring systems within smart grid environments, focusing on cybersecurity, data integrity, and system reliability. The integration of solar photovoltaic systems with digital monitoring networks enhances real-time energy management but introduces communication vulnerabilities affecting monitoring accuracy and operational stability. This study aims to assess how Network Security, Data Integrity, and System Reliability contribute to Secure Communication Architecture and Monitoring Performance. A quantitative research design was employed, and the proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The research model included five constructs: Network Security, Data Integrity, System Reliability, Secure Communication Architecture, and Monitoring Performance. Results indicate that Network Security significantly and positively affects Secure Communication Architecture, while Data Integrity positively influences both Secure Communication Architecture and System Reliability. Furthermore, Secure Communication Architecture and System Reliability both contribute positively to Monitoring Performance. The measurement model demonstrated acceptable reliability and validity, confirming construct robustness. These findings highlight that effective solar panel monitoring systems depend not only on technical energy infrastructure but also on secure, reliable communication networks that ensure accurate and timely data exchange. The study concludes that strengthening cybersecurity mechanisms, data integrity controls, and communication reliability is essential to improve monitoring performance and support resilient smart grid-based renewable energy management.
PENINGKATAN EFISIENSI ENERGI TERBARUKAN MELALUI DIGITALISASI SISTEM PANEL SURYA BERBASIS INTERNET OF THINGS (IOT) PADA ASOSIASI PENA ILMU INDONESIA Iksan Wahyu Permadi; Ahmad Roihan; Ageng Setiani Rafika; Oleh Soleh; Ignatius Agus Supriyono; Dendy Jonas
Masyarakat: Jurnal Pengabdian Vol. 3 No. 2 (2026)
Publisher : Yayasan Pendidikan Dan Pengembangan Harapan Ananda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58740/m-jp.v3i2.848

Abstract

Pemanfaatan Pembangkit Listrik Tenaga Surya (PLTS) sebagai sumber energi terbarukan memerlukan sistem pemantauan yang efektif untuk menjaga kinerja dan efisiensi operasional, namun Asosiasi Pena Ilmu Indonesia masih melakukan pemantauan kondisi baterai secara manual sehingga informasi mengenai tegangan, arus, dan kapasitas baterai belum dapat diperoleh secara berkelanjutan. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan efektivitas pengelolaan energi terbarukan melalui penerapan sistem digitalisasi monitoring panel surya berbasis Internet of Things (IoT). Sasaran kegiatan adalah pengurus dan anggota Asosiasi Pena Ilmu Indonesia sebagai pengguna sistem PLTS. Metode pelaksanaan meliputi identifikasi kebutuhan mitra, perancangan dan implementasi sistem monitoring berbasis NodeMCU ESP8266 yang terintegrasi dengan sensor tegangan dan sensor arus ACS758, pelatihan penggunaan sistem, pendampingan operasional, serta evaluasi kinerja sistem. Data hasil pengukuran dikirimkan melalui jaringan Wi-Fi menggunakan protokol HTTP GET menuju basis data MySQL sehingga dapat dipantau secara real-time dan tersimpan sebagai data historis. Hasil implementasi menunjukkan bahwa sistem berhasil mengirimkan data parameter kelistrikan secara periodik setiap 10 menit dengan tingkat keberhasilan transmisi mencapai 100% tanpa bentrokan data. Kegiatan ini meningkatkan kemampuan mitra dalam melakukan pemantauan kondisi baterai secara mandiri, mempermudah evaluasi penggunaan energi, serta mendukung pengelolaan energi terbarukan yang lebih efektif dan berkelanjutan.
Security and Privacy Enhancement in Decentralized Digital Data Sharing Environments Muhamad Yusup; Mardiana; Ageng Setiani Rafika; Otniel Feliks Putra Wahyudi; Agung Lorenzo
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vp5s1m02

Abstract

This study examines the suitability of the IPFS as a decentralized architecture for secure digital data exchange. Traditional centralized protocols, such as HTTP, introduce structural vulnerabilities, including single points of failure, metadata exposure, and susceptibility to interception or unauthorized modification. As digital data exchange becomes increasingly essential in various sectors, ensuring data security and privacy has become a growing concern. The primary objective of this study is to evaluate IPFS’s ability to address these vulnerabilities and enhance the security and privacy of digital data-sharing environments. This research employs a structured literature review to synthesize findings from distributed-systems research, cryptographic studies, and peer-to-peer networking analyses. Additionally, the study benchmarks IPFS against traditional storage protocols, such as HTTP and FTP, to assess its advantages and limitations. The results demonstrate that IPFS offers significant advantages, including content-addressed storage, Merkle-DAG verification, and decentralized peer replication. These features improve fault tolerance, ensure data integrity, and reduce the risks of data tampering. However, limitations, such as content availability and reliance on node uptime, are also noted. While IPFS is not a complete security solution, it provides a strong foundational architecture for privacy-preserving, distributed data-sharing workflows when paired with complementary cryptographic and governance frameworks, making it a viable alternative for secure digital data exchange.
AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes Ageng Setiani Rafika; Adam Faturahman; Bintang Nandana Henry; Firdaus Dwi Yulian; Mohammed Hassan
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.61

Abstract

The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.
Cybersecurity Strategies for Preventing Ransomware Attacks in Cloud-Based Applications Ageng Setiani Rafika; Sora Baltasar; Alfri Adiwijaya; Mochamad Heru Riza Chakim; Zhask Stefano Rizky
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.77

Abstract

Ransomware attacks have become a significant threat to cloud-based applications, posing severe risks to organizations' data integrity, financial stability, and operational continuity. This paper explores the challenges of securing cloud environments against ransomware, focusing on vulnerabilities such as inadequate encryption, weak access controls, and multi-tenancy risks. Through an in-depth analysis, the paper identifies the most common types of ransomware targeting cloud applications, including file encryption and data exfiltration ransomware, and discusses the security weaknesses that facilitate these attacks. The paper further evaluates existing cybersecurity strategies, such as data encryption, multi-factor authentication (MFA), and continuous monitoring, highlighting their effectiveness in preventing ransomware attacks. Based on these findings, a comprehensive framework is proposed, combining technical solutions like strong encryption and AI-based threat detection with organizational practices such as regular employee training and backup solutions. The study also emphasizes the importance of collaboration between cloud service providers and organizations to enhance overall cloud security. By adopting a multi-layered approach and integrating emerging technologies, organizations can significantly improve their resilience against ransomware threats. This research contributes to the ongoing dialogue on cloud security by providing actionable recommendations for preventing ransomware attacks and safeguarding cloud-based applications from evolving cyber threats.
Artificial Intelligence for Optimizing Renewable Energy Systems in Sustainable Power Generation Ageng Setiani Rafika; Dendy Jonas; Muchlisina Madani; Oliver Sauntos
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1098

Abstract

The rapid expansion of renewable energy adoption has increased the need for intelligent energy management, as conventional rule based dispatch systems of ten struggle with the dynamic, nonlinear, and uncertain operating conditions of high-penetration renewable grids. Traditional controllers show limited energy utilization efficiency and frequent frequency-standard violations under variable wind and solar conditions. This study proposes and evaluates an integrated Artificial Intelligence (AI) framework combining a Long Short-Term Memory (LSTM) neural network for 24-hour energy demand and generation forecasting with Particle Swarm Optimization (PSO) for real-time dispatch optimization. The framework is tested against a conventional rule-based baseline using three benchmark datasets from the UCI Machine Learning Repository, the National Renewable Energy Laboratory (NREL), and Open Power System Data, covering 36 months of hourly solar and wind observations. The objective is to design and experimentally validate an AI-based optimization framework that improves energy efficiency, reduces operational losses, and enhances grid stability in renewable energy systems. The proposed LSTM-PSO framework reduces Mean Absolute Error (MAE) by 50.7% and Root Mean Square Error (RMSE) by 44.3%. Energy efficiency increases from 76.2% to 91.4%, while energy losses decrease from 20.7% to 9.6%, equivalent to approximately 5,800 tonnes of CO2 equivalent avoided annually at a 100 MW grid scale. The integrated LSTM PSO architecture provides a reliable and scalable basis for AI-driven renewable energy optimization, supporting SDG 7, SDG 9, SDG 11, and SDG 13.
Co-Authors . Isdiarto Abdu Roqy Adam Faturahman Agung Lorenzo Agung Rizky Ahmad Roihan Ahmad Roihan, Ahmad Alfri Adiwijaya Ananda Dian Alifah Andri Ahmad Gozali Andriyansah . Anisa Pujianti Arief Saptono Aris Martono Asep Saefullah Asep Saifudin Asmawati, Ari Bintang Nandana Henry Budi, Danang Surya Chua Toh Hua Dadi Adriana Dedy Prasetya Kristiadi Dendy Jonas Dendy Jonas Dendy Jonas Managas Derry Prasetyo Deviana Ika Putri Dhimas Tribuana Dwi Julianingsih Eduard Hotman Purba Erick Febriyanto Euis Sitinur Aisyah Faridah, Ida Fauzan, Jimmy Ferry Firmansyah Ferry Sudarto Fikrah Syafa’ah Finnike Maysarah Firdaus Dwi Yulian Fitroh Diah Widiarti Giandari Maulani, Giandari Gregory, Jesus Gulo, Nitema H. Suhada Hanifah Yunan Putri Henderi . Hendra Kusumah Hendra Kusumah Hidayati Hidayati Hiroshi Kenta I Gusti Wayan Murjana Yasa Iksan Wahyu Permadi Ilamsyah Ilamsyah, Ilamsyah Imam Aji Santoso jawahir, Jawahir Jesus Gregory Ki Ahmad Saputro Lena Magdalena Mardiana Mardiana Mardiana Mardiana Mardiana Marviola Hardini Mayang Septiawati Meidy Surya Hadi Putra Mochamad Heru Riza Chakim Mohammed Hassan Muchlisina Madani Muhamad Yusup Mukti Budiarto Nugroho, Purnomo Satria Nurlaila Suci Rahayu Rais Oleh Soleh Oliver Sauntos Otniel Feliks Putra Wahyudi Padeli Po Abas Sunarya Pramita Retno Ayuning Tyas Purnomo Satria Nugroho Putri, Adisa Lahmania Raharjo, Ristian Rani Putri Merliasari Ridho Firdaus Ristian Raharjo Riyan Nova Saputra Rohim Rohim Safriyati, Evi saifudin, Asep Sangaji, Aziz Andrean Siskawati Sanusi Sora Baltasar SRI RAHAYU Sri Sulistyaningsih Sudaryono Sudaryono Sunandar, Endang Suparman, Ade Supriyono, Ignatius Agus Tamara Dian Anggiani Tarigan, Pelinta Triyono Triyono Wahyu Budianto Warseno Winda Larasati Winda Larasati Wirawan, Mohammad Fiki Wisnu Dwi Andoyo Yasin Nur Hidayat Yusuf Firdaus Zakaria, Noor Azura Zebua, Selamat Zhask Stefano Rizky