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All Journal Indonesian Journal of Geography Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control at-tamkin: Jurnal Pengabdian kepada Masyarakat CYCLOTRON Journal of Computer Science and Informatics Engineering (J-Cosine) RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) JFIOnline Infotekmesin Buletin Ilmiah Sarjana Teknik Elektro Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Frontier Energy System and Power Engineering jurnal syntax admiration Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Unri Conference Series: Community Engagement International Journal of Robotics and Control Systems International Journal of Advanced Science and Computer Applications Bulletin of Pedagogical Research ALINIER: Journal of Artificial Intelligence & Applications Ilmu Komputer untuk Masyarakat Jurnal Fortech SinarFe7 Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Scientech Research and Development Reflection Journal Jurnal Inovasi Teknologi dan Edukasi Teknik Lentera: Multidisciplinary Studies Bulletin of Social Informatics Theory and Application Jurnal INFOTEL ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Jurnal ilmiah teknologi informasi Asia Lentera: Multidisciplinary Studies Jurnal FORTECH Academia Open Jurnal JEETech
Siti Sendari
Universitas Negeri Malang
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Analisis Kritis terhadap Kebijakan dan Praktik Etika pada ChatGPT Purwanto, Devi Dwi; Wibawa, Aji Prasetya; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 5 No. 2 (2025): Desember
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/xv4mqp17

Abstract

Perkembangan pesat dalam teknologi Generative AI berbasis teks melalui model seperti ChatGPT membawa dampak yang signifikan di berbagai sektor. Namun, penerapan teknologi ini juga memunculkan tantangan etika yang mendalam, khususnya terkait dengan transparansi, keadilan, akuntabilitas, dan pengelolaan bias algoritmik. Artikel ini mengkaji secara kritis penerapan kebijakan etika dalam desain dan pengembangan ChatGPT, dengan fokus pada prinsip-prinsip etika fundamental seperti keadilan, non-diskriminasi, dan transparansi. Metode analisis yang digunakan dalam artikel ini adala Ex Post Facto, dimana analisis dilakukan setelah peristiwa atau kebijakan diterapkan, guna menilai dampaknya terhadap pengelolaan bias algoritmik dan risiko disinformasi. Pendekatan etika normative juga diterapkan untuk mengevaluasi sejau mana kebijakan diterapkan oleh OpenAI sejalan dengan nilai-nilai keadilan. Penelitian ini juga mengidentifikasi tantangan besar yang dihadapi oleh pengembang dalam memastikan bahwa Generative AI dapat digunakan secara adil dan bertanggung jawab. Selain itu, artikel ini memberikan rekomendasi untuk meningkatkan transparansi dan akuntabilitas dalam penggunaan AI, guna menciptakan ekosistem yang lebih inklusif dan dapat dipertanggungjawabkan. Keunikan artikel ini terletak pada analisis mendalam terhadap kebijakan etika yang diterapkan oleh OpenAI, serta focus pada prinsip-prinsip etika fundamental dalam konteks pengembangan Generative AI, yang belum banyak dibahas dalam penelitian sebelumnya. Kesimpulannya, keberhasilan AI yang etis bergantung pada penerapan kebijakan yang komprehensif dan sistematis, yang tidak hanya efisien secara teknis tetapi juga menghormati nilai-nilai sosial dan hak-hak individu. A critical analysis of Ethical Policies and Practices at ChatGPT The rapid advancement of text-based Generative AI technologies through models such as ChatGPT has had a significant impact across various sectors. However, the adoption of this technology also raises profound ethical challenges, particularly with regard to transparency, fairness, accountability, and the management of algorithmic bias. This article critically examines the implementation of ethical policies in the design and development of ChatGPT, with a focus on fundamental ethical principles such as fairness, non-discrimination, and transparency. The analytical method employed in this study is an ex post facto approach, in which analysis is conducted after the implementation of events or policies to assess their impact on the management of algorithmic bias and the risks of misinformation. A normative ethical approach is also applied to evaluate the extent to which OpenAI’s policies align with principles of justice. This study identifies major challenges faced by developers in ensuring that Generative AI is used fairly and responsibly. In addition, the article offers recommendations to enhance transparency and accountability in AI deployment in order to foster a more inclusive and accountable ecosystem. The novelty of this article lies in its in-depth analysis of the ethical policies implemented by OpenAI and its focus on fundamental ethical principles in the context of Generative AI development, an area that has received limited attention in prior research. In conclusion, the success of ethical AI depends on the implementation of comprehensive and systematic policies that are not only technically efficient but also respectful of social values and individual rights.
Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency Riska, Suastika Yulia; Widiyaningtyas, Triyanna; Elmunsyah, Hakkun; Sendari, Siti
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.1227

Abstract

The swift advancements in Artificial Intelligence and Machine Learning have rendered datasets essential; nonetheless, their heightened utilization has engendered intricate ethical dilemmas that are frequently neglected. This study seeks to delineate and highlight ethical concerns associated with the collection of primary data and the reutilization of secondary datasets in computer science research. We employed a Systematic Literature Review (SLR) methodology in accordance with the PRISMA 2020 guidelines, examining 72 publications sourced from five esteemed academic databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar) published from 2021 to 2025. The study results indicate that ethical difficulties emerge uniformly in both primary and secondary datasets. Primary datasets primarily face challenges related to privacy threats, anonymization, and Informed Consent, whereas secondary datasets are more susceptible to licensing infringements, dataset repurposing, and insufficient preparation transparency. The three domains that predominantly encountered these challenges were Machine Learning, Computer Vision, and Natural Language Processing. Moreover, practices of data manipulation, including cherry-picking and concealed preparation, were identified as detrimental to scientific integrity. This study's findings underscore the need for enhanced ethical standards for datasets and greater transparency in preparation documentation to ensure the repeatability of data-driven research.
Automated PPE Compliance Verification Using YOLOv11l Spatial Logic: Verifikasi Kepatuhan APD Otomatis Menggunakan YOLOv11l dan Logika Spasial Effendi, Muhammad Minhaj; Sendari, Siti
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13886

Abstract

General Background: Monitoring personal protective equipment (PPE) usage is a critical component of occupational health and safety (OHS) in construction, yet manual inspection remains inconsistent and prone to error. Specific Background: Recent advances in computer vision, particularly YOLO-based object detection, have improved PPE detection accuracy in complex environments. Knowledge Gap: However, existing approaches primarily detect PPE presence without verifying its correct usage or associating it with individual workers, leading to inaccurate compliance interpretation. Aims: This study develops an automated PPE compliance verification system using YOLOv11l combined with spatial association logic to assess PPE completeness and anatomical correctness at the individual worker level. Results: The system was trained on 2,788 construction images and achieved high performance with mAP@50 of 0.979, precision of 0.976, recall of 0.954, and peak F1-score of 0.97, while demonstrating accurate classification across PPE categories including helmets, vests, and shoes. Novelty: The integration of zone-based spatial verification enables validation of PPE placement within anatomically defined regions, addressing the limitation of detection-only systems. Implications: This approach supports objective, continuous, and reliable safety auditing in construction environments, offering a scalable alternative to manual OHS monitoring. Highlights• Multi-class detection identifies workers and safety equipment with high accuracy• Region-based validation distinguishes proper gear usage from misplacement• System classifies compliance status through structured decision logic KeywordsAutomated PPE Verification; Construction Safety; Deep Learning; Spatial Association Logic; YOLOv11l
Optimalisasi Energi Pada Lift Berdasarkan Gerak Vertikal pada Lift Menggunakan Hybrid Naive Bayes Adika Prana Ihsanuddin; Siti Sendari; Ilham Ari Elbaith Zaeni; M. Afnan Habibi; Danang Arengga Wibowo
Jurnal JEETech Vol. 6 No. 2 (2025): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i2.6203

Abstract

Penelitian ini bertujuan untuk mengoptimalkan penggunaan energi pada sistem lift berdasarkan gerak vertikal menggunakan algoritma Hybrid Naive Bayes. Proses optimalisasi didasarkan pada pengumpulan data dilakukan di Gedung B11 Fakultas Teknik Universitas Negeri Malang selama periode waktu tertentu, dalam upaya mengurangi konsumsi energi pada gedung bertingkat, efisiensi energi lift menjadi salah satu fokus utama. Dengan memanfaatkan data penggunaan lift yang meliputi pola pergerakan vertikal, waktu operasional, serta beban muatan, penelitian ini melakukan klasifikasi dan prediksi efisiensi energi. Algoritma Hybrid Naive Bayes dipilih karena kemampuannya dalam menangani ketidakpastian data serta keandalannya dalam klasifikasi, terutama saat dikombinasikan dengan metode optimisasi lainnya. Hasil prediksi efisiensi energi yang akurat juga memungkinkan manajemen gedung untuk menerapkan strategi operasional yang lebih hemat energi dan ramah lingkungan. Dengan demikian, penelitian ini diharapkan memberikan kontribusi signifikan dalam pengelolaan energi yang lebih efisien pada sistem lift di gedunggedung tinggi.
Federated Ensemble Learning with SHAP–LIME Interpretability for Smart Home Energy Prediction Rahma Puspitasari; Siti Sendari; Muhammad Arif Hermawan; Joshua Andrian; Ira Kumala Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2665

Abstract

The increased adoption of IoT-based Smart Home systems in Indonesia has resulted in a growing volume of device-level energy data, opening up opportunities for the development of predictive models to support efficient household electricity consumption. However, challenges related to accuracy, interpretability, and data privacy remain a major concern, especially when data is distributed across multiple devices. This study evaluates the performance of four tree-based ensemble models, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, in centralized learning and federated learning scenarios using the Indonesia Smart Home Dataset. After undergoing feature preprocessing and refinement, including the removal of Sofa Pressure and Bed Pressure due to high noise, each model was trained and evaluated using MAE, MSE, and RMSE metrics. Federated learning was implemented through the Federated Averaging (FedAvg) algorithm to maintain data privacy without the need to transfer raw data between devices. The results show that LightGBM consistently provides the best performance in both scenarios and demonstrates resilience to data fragmentation and heterogeneity. Although there was a slight increase in error in federated learning, the error values remained within an acceptable range. SHAP and LIME analyses revealed that high-power devices such as air conditioners, water pumps, rice cookers, lights, and refrigerators had the greatest contribution.
Intelligent Weighing Machine untuk Meningkatkan Keakuratan Berat Produk Bubuk Herbal Instan Sujito; Siti Sendari; Anik Nur Handayani; Langlang Gumilar; Imam Tree Utomo; Dhiyaurrahman Fakhruddin
ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Vol. 1 No. 2 (2023): ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Publisher : UPT Publikasi dan Penerbitan Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/abdiunisap.v1i2.112

Abstract

Syarimpon merupakan usaha yang didirikan oleh Afiani Fadiana pada tahun 2017 yang berlokasi di Perumahan Persada Bhayangkara No.G3, Pangetan, Kecamatan Singosari, Kabupaten Malang. yang bergerak dalam bidang produksi minuman herbal instan dan sudah mengembangkan minuman herbal instan dengan kemasan yang menarik dan sudah memiliki berbagai perizinan mulai dari NIB ( Nomor Induk Berusaha), PIRT (Produk Industri Rumah Tangga), Perizinan penetapan produk halal dan produk sudah HAKI (Hak Kekayaan Intelektual) namun Syarimpon ini mengalami permasalahan mengenai proses penimbangan berat produk masih menggunakan cara manual dan memakan waktu pada saat proses penimbangan dan kurangnya keakuratan dari timbangan yang digunakan. Dengan adanya program pengabdian ini diharapkan mampu mengatasi masalah dari mitra dengan mentransfer teknologi Intelligent Weighing Machine guna meningkatkan keakuratan berat produk dengan cerdas dan lebih efisien. Tujuan dari program pengabdian kepada masyarakat ini menghasilkan Intelligent Weighing Machine yang diharapkan mampu mengurangi waktu dalam proses penimbangan dan membantu mengatasi permasalahan selama proses produksi mereka dan dapat mempertahankan kualitas dari minuman herbal instan yang mereka produksi. Hasil dari program PKM ini melakukan pengembangan Intelligent Weighing Machine alat ini didesain untuk memberikan kemudahan untuk pengguna sehingga mempercepat proses penimbangan dan meningkatkan keakuratan berat bersih produk sehingga dapat mengurangi waktu yang digunakan untuk menimbang serta memastikan berat bersih produk sesuai standar yang telah disesuaikan oleh UMKM Syarimpon dan memberikan dampak positif untuk penjualan dan produksi mereka.
Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine Franky Melky; Siti Sendari; Ilham Ari Elbaith
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26261

Abstract

During this time, weight point testing is still done manually using a jack until now it has begun to be replaced with hydraulic equipment namely Lift Table Hydraulic (LTH) which is a portable table with a hydraulic system equipped with sensors (Loadcell and LVDT), powerpack control panel, powerpack, relay module and solenoid valve to adjust the table height. This portable table is one component of the heavy point measurement equipment system used for mining and plantation vehicles such as tractors, buses, trucks which are required to have a safe structure in heavy road conditions with rough or uneven surfaces with slopes up to an angle of 15 ° to 20 °. This emphasized research contributes to more accurate testing. Based on these problems, this research was conducted using Support Vector Machine (SVM) for the optimization of heavy point position measurement. The objects used are minibuses with 1 and 19 passengers and buses with 29 and 36 passengers on the proportion of datasets (training: testing) of 80% and 20% using linier kernel. From the experimental results, the accuracy in the condition of 1 passenger is 94.7%; minibus 19 passengers 98%; bus 29 passengers 98.1% and bus 36 passengers 97.4%. The highest accuracy obtains on 29 passengers minibus. 
Integrasi Embedding Multiformat untuk Representasi Semantik Big Data Smart City: Analisis Etika Penelitian, AI Ethics, dan Tantangan Publikasi Ilmiah di Era Teknologi Lanjut Sumanti, Endang Sri; Prasetya, Didik Dwi; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 6 No. 2 (2026): June
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/rj.v6i2.4193

Abstract

Penelitian ini bertujuan menganalisis manfaat representasi semantik dan memetakan risiko etika pada pipeline embedding multimodal dalam pengolahan Big Data Smart City. Penelitian menggunakan desain sintesis literatur yang dipadukan dengan studi kasus konseptual, analisis hermeneutik teknologi, dan pemetaan risiko berbasis pipeline. Analisis mencakup lima komponen utama, yaitu Text Encoder, Visual Encoder, Cross-Modal Alignment, Fusion Layer, dan Semantic Output Layer. Hasil penelitian menunjukkan bahwa integrasi data tekstual dan visual dapat memperkaya konteks semantik, memperkuat hubungan informasi antarmodalitas, serta mempertahankan konsistensi representasi pada dokumen kebijakan yang kompleks. Namun, manfaat tersebut disertai lima risiko utama, yaitu bias representasional, privasi dan indirect disclosure, dual-use, surveillance dan profiling otomatis, serta asimetri kekuasaan informasi. Fusion Layer teridentifikasi sebagai komponen dengan risiko paling tinggi karena menggabungkan bias dan potensi penyalahgunaan dari beberapa modalitas, sedangkan Cross-Modal Alignment menunjukkan mekanisme mitigasi yang masih terbatas. Integrasi prinsip autonomy, beneficence, justice, dan consent dengan fairness, accountability, transparency, explainability, serta manajemen risiko menghasilkan kerangka evaluasi etika yang dapat diterapkan sepanjang siklus hidup sistem. Penelitian merekomendasikan penerapan ethical checkpoints, audit bias, dokumentasi dataset dan model, logging, pembatasan tujuan penggunaan, serta human oversight. Karena berbasis studi kasus konseptual, kerangka ini masih memerlukan validasi ahli dan pengujian empiris menggunakan dataset Smart City aktual. Ethical Risk Mapping in Multimodal Embedding Pipelines for Semantic Representation of Smart City Big Data: A Literature Synthesis and Conceptual Case Study This study aims to analyze the benefits of semantic representation and map ethical risks within multimodal embedding pipelines used to process Smart City Big Data. The study employed a literature synthesis design combined with a conceptual case study, technological hermeneutic analysis, and pipeline-based risk mapping. The analysis covered five main components: the Text Encoder, Visual Encoder, Cross-Modal Alignment, Fusion Layer, and Semantic Output Layer. The findings indicate that integrating textual and visual data can enrich semantic context, strengthen cross-modal information relationships, and maintain representational consistency in complex policy documents. However, these benefits are accompanied by five major risks: representational bias, privacy and indirect disclosure, dual use, automated surveillance and profiling, and information-power asymmetry. The Fusion Layer was identified as the component with the highest risk because it combines bias and the potential misuse of information from multiple modalities, whereas Cross-Modal Alignment still has limited mitigation mechanisms. Integrating the principles of autonomy, beneficence, justice, and consent with fairness, accountability, transparency, explainability, and risk management produced an ethical evaluation framework that can be applied throughout the system lifecycle. The study recommends implementing ethical checkpoints, bias audits, dataset and model documentation, logging, purpose limitation, and human oversight. Because the study is based on a conceptual case scenario, the proposed framework still requires expert validation and empirical testing using actual Smart City datasets.
Groundedness-Aware Retrieval untuk Reduksi Halusinasi pada Chatbot Dokumen Pemerintah di Era Big Data Rumamby, Frendy Rocky; Prasetya, Didik Dw; Elmunsyah, Hakkun; Sendari, Siti
Reflection Journal Vol. 6 No. 2 (2026): June
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/e83cp606

Abstract

Model Bahasa Besar (LLM) makin banyak dipakai untuk chatbot layanan publik karena mampu menyintesis informasi dari korpus dokumen yang besar. Namun, kecenderungan halusinasi pada LLM berisiko menghasilkan informasi administratif yang tampak meyakinkan tetapi tidak bersumber dari dokumen resmi. Penelitian ini mengusulkan kerangka Groundedness-Aware Retrieval (GAR) yang menggabungkan hybrid retrieval (BM25 + embedding), verifikasi groundedness multi-lapis, decoding sadar ketidakpastian, dan penelusuran bukti (evidence traceability) agar jawaban dapat diaudit. Pengujian pada 350 kueri administratif realistis menunjukkan GAR meningkatkan groundedness menjadi 0,91, menaikkan presisi faktual menjadi 94,8%, dan menurunkan tingkat halusinasi menjadi 3,5% dibanding LLM dasar dan RAG standar. Keunikan GAR dibanding RAG konvensional terletak pada lapisan verifikasi groundedness dan mekanisme penolakan/penandaan ketidakpastian yang secara eksplisit mencegah klaim tanpa bukti. Groundedness-Aware Retrieval to Reduce Hallucinations in Government Document Chatbots in the Big Data Era Large Language Models (LLMs) are increasingly adopted in public-service chatbots, yet they remain vulnerable to hallucinations that can surface as authoritative-looking but unsupported administrative claims. We propose a Groundedness-Aware Retrieval (GAR) framework that combines hybrid retrieval (BM25 + dense embeddings), multi-layer groundedness verification, uncertainty-aware decoding, and evidence traceability for auditability. On 350 realistic administrative queries, GAR outperforms a baseline LLM and standard RAG, achieving a groundedness score of 0.91, factual precision of 94.8%, and a hallucination rate of 3.5%. Compared with conventional RAG, GAR is distinctive in its explicit groundedness-verification layer and refusal/uncertainty tagging that prevents evidence-free generation.
Tantangan Etika dalam Penelitian Kualitas Daya Listrik Berbasis Data Intensif: Suatu Tinjauan Literatur Ana Nuril Achadiyah; Arif Nur Afandi; Hakkun Elmunsyah; Siti Sendari
RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Vol. 9 No. 1 (2026): RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer)
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/resistor.9.1.15-24

Abstract

Digitalisasi sistem tenaga listrik dan integrasi metode berbasis kecerdasan buatan telah mendorong penelitian Kualitas Daya Listrik (Power Quality/PQ) bergeser dari sekadar pengukuran teknis menuju pemanfaatan data operasional beresolusi tinggi yang sarat implikasi etika. Artikel ini menyajikan tinjauan literatur terstruktur terhadap publikasi tahun 2025 yang memanfaatkan dataset terkait PQ, dengan fokus pada identifikasi bentuk data, konteks penerapan, serta risiko etis yang menyertainya. Sumber literatur diperoleh melalui penelusuran basis data ScienceDirect menggunakan kombinasi kata kunci yang mengaitkan “ethic”, “power quality”, dan “electrical research”, kemudian disaring berdasarkan kriteria inklusi yang menekankan kualitas jurnal, relevansi teknis, dan keterkaitan langsung dengan isu etika pengelolaan data.​ Hasil telaah menunjukkan bahwa tantangan etika dalam riset PQ terkonfigurasi dalam tiga klaster utama, yaitu privasi dan anonimitas data konsumsi serta mobilitas energi, keamanan dan kerahasiaan data teknis yang merepresentasikan kondisi infrastruktur kritis, serta bias metodologis dan representasi ketika data memuat dimensi sosial, geografis, dan ekonomi. Berbagai dataset resolusi tinggi mulai dari deret waktu beban jaringan tegangan rendah, sinyal kondisi mesin listrik dan generator, hingga data lintasan kendaraan berpotensi membuka peluang re-identification, pemetaan kerentanan sistem, maupun generalisasi yang tidak adil terhadap kelompok atau wilayah tertentu. Artikel ini menekankan urgensi pengembangan kerangka etika khusus untuk penelitian PQ, yang mengintegrasikan prinsip privacy by design, keamanan siber, dan keadilan data dengan teknik teknis seperti differential privacy, federated learning, serta protokol klasifikasi sensitivitas dan pembatasan tujuan penggunaan data. Temuan ini diharapkan dapat menjadi pijakan bagi perumusan pedoman etika yang lebih operasional bagi komunitas riset PQ di era sistem tenaga cerdas berbasis data intensif.
Co-Authors A.N. Afandi Abdul Wafi Abdur Rohman Achmad Jefri Achmad Jefri Achmad Jefri Adika Prana Ihsanuddin Afnan Habibi, M. Agung Bella Putra Utama Agung Endro Nugroho Agus Rahma Dani, Ayunda Aji Prasetya Wibawa Alief Fajar Syahputra Amalia Nurutami Amalia Prameswari Alvina Ana Nuril Achadiyah Andi Khoirudin Andis Wijaya Anik Nur Handayani Anisatul Qomariyah Arengga Wibowo, Danang Arengga, Danang Argeshwara, Dityo Kreshna Arifin, Samsul Aripriharta - Arnista Vindriyanti Ashar, Muhammad Ashrofil Muzaki Ayunda Agus Rahma Dani Bagaskoro, Muhammad Cahyo Baliyah Ahmad Fathoni Benny Agung Prasetyo Billah, Egi Nursari Burhanudin Yusuf Abdullah Ar Ramadhan Cahyaning Wulandari Cahyaning Danang Arengga Wibowo Dava Desti Yanti Dendi Mukti Putranto Desti Yanti, Dava Devi Dwi Purwanto Dhiyaurrahman Fakhruddin Dian Candra Lestari Didik Dwi Prasetya Dwi Mukti Asmoro Sari Dwi Puri Fatmawati Dyah Lestari Effendi, Muhammad Minhaj Eko Noerhayati Eli Hendrik Sanjaya Elista Kartika Sari Faiz Syaikhoni Aziz Fajar Syahputra, Alief Fatma Cahyaningrum Fitri, Shofiana Franky Melky Giri Wahyu Wiriasto Guyub Raharjo Hakiki, A.Riyan Rahman Hakkun Elmunsyah Hanny Prasetya Hariyadi Haq, Sigit Prasetyo Harits Ar Rosyid Hariyadi, Hanny Prasetya Hary Suswanto Heru Wahyu Herwanto Hidiyah, Tabita May Hsien-I Lin I Made Wirawan Ilham Ari Elbaith Ilham Ari Elbaith Zaini Imam Tree Utomo Ira Kumala Sari Ira Kumalasari Irham Fadlika Irmawanto, Rudi Irvan, Mhd James Aditama Januar Arief Muhammad Joshua Andrian Joumil Aidil Saifuddin Kamil Faqih Kartika Sari, Elista Khoiruddin Asfanie Kotaro Hirasawa Kumalasari, Ira Langlang Gumilar Lestari, Dian Candra Listyo Yudha Irawan M. Afnan Habibi M. Bagus Arifin Made Radikia Prasanta Mahfud Jiiono Mahfud Jiono Mario Leo Nardo Melta Dhemahestri Misik Rahayu Oktaningsih Moch. Burhanuddin Alfarobbi Mochamad Farhan Ali Irfani Mochammad Haidar Ridho Mochtar, Norrima Moh. Zainul Falah Mohammad Yussril Asri Mokh Sholihul Hadi Mokhammad Nasrulloh Mokhtar , Norrima Binti Muhamad Syamsu Iqbal Muhammad Aditya Firnanda Muhammad Arif Hermawan Muhammad Fajar Saifuddin Muhammad Hanif Abdur Razaq Muhammad Tahfidlul Azmi Muhammad Yoga Pranata Mukti Putranto, Dendi Muladi Mustika, Soraya N. Muzayana Muzayana Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Ni’am, Faj’run Nobri Wicaksono Nur Halim Nurutami, Amalia Nuzul Zaeni Eki Ramadhanu Prana Ihsanuddin, Adika Prasetya Widiharso Prasetya Widiharso Prasetya Widiharso Prasetya, Didik Dw Prastiwi, Mellinia Regina Putri, Nanda Regita Rahma Puspitasari Ria Rahmawati Rina Dewi Indahsari Rindi Santika Agustin Ristanto Aji Prakoso Rizki Jumadil Putra Rizky Asilia Puspita Sari Rosmin, Norzanah Rumamby, Frendy Rocky Samsul Arifin Setumin , Samsul Setumin, Samsul Shofiana Fitri Shrestha, Rajendra Prasad Soraya Norma Mustika Soraya, Fenthy Soraya, Fenthy Suastika Yulia Riska Sumanti, Endang Sri Supardjan A. Margono Susilo, Suhiro Wongso Syaad Patmanthara Syabani, Muhiban Syafaat, Mokhammad Syafiq Ubaidillah Syamsul Arifin Syamsul Bachri Triyanna Widiyaningtyas Wahyu Sakti Gunawan Irianto Wahyu Tri Handoko Waridno, Aji J. T. Wibowo, Danang Arengga Wibowo, Dian S. Wibowo, Fauzy Satrio Wildan Iswahyudi Yogi Dwi Mahandi Yudhi Christianto Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zulkarnain, Aldo Z. A.