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All Journal International Journal of Electrical and Computer Engineering Jurnal SPEKTRUM Techne : Jurnal Ilmiah Elektroteknika Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Majalah Ilmiah Teknologi Elektro Jurnal SPEKTRUM
Ida Bagus Gede Manuaba
Department Of Electrical Engineering, Universitas Udayana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Literatur Review Tantangan dan Teknologi dalam Pengembangan Advance Metering Infrastructure (AMI) I Made Agus Artha Putra; Ida Bagus Gede Manuaba
Majalah Ilmiah Teknologi Elektro Vol 24 No 1 (2025): ( Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.205.v24i01.P01

Abstract

The fundamental challenges related to the inability of traditional metering infrastructure to provide accurate and fast data and the lack of visibility to manage electricity usage information have driven the development of smart metering solutions. Smart metering, which is part of the smart grid architecture, has evolved over the years along with the needs of the electric power system infrastructure that requires efficient energy management initiatives. Advanced Metering Infrastructure (AMI) is one of the technologies being developed as a smart metering infrastructure. AMI consists of systems and networks, which are responsible for collecting and analyzing data received from smart meters. In addition, AMI also manages various electricity-related applications and services based on data collected from smart meters. The implementation of AMI has been proven to provide various positive results for both energy service providers and consumers. AMI is able to increase the accuracy of energy consumption recording by up to ±0.5% and reduce billing errors by up to 95%. Therefore, AMI plays an important role in the smooth functioning of the smart grid. In developing AMI technology, of course, there are challenges. Therefore, this paper provides an overview of smart metering technology, its design requirements, protocols and challenges, and policy issues.
Integrasi Data Science dan AI untuk Optimalisasi Layanan Pemerintahan: Literatur Review Kadek Dwi Mahardika Adnyana; I Made Oka Widyantara; NMAED Wirastuti; Ida Bagus Gede Manuaba
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia’s digital government agenda establishes a policy backbone for data-driven and AI-enabled public services through the national Electronic-Based Government System (SPBE) and the One Data policy, while the Personal Data Protection Law (PDP) frames privacy-by-design obligations for public institutions. Building on international guidance (OECD’s G7 Toolkit; World Bank’s GovTech Maturity Index), this review synthesizes how Data Science (DS)—via reliable feature engineering and descriptive–predictive analytics—can be aligned with Artificial Intelligence (AI) for automation and decision support under public-sector accountability requirements. We identify recurrent enablers (interoperable data architecture, data governance, civil service capabilities, and MLOps) and barriers (data silos, legacy constraints, skills gaps, and explainability/ethics demands), and propose evaluation indicators that link model performance to service performance: service latency reduction, service quality, model fairness, and explainability. The contribution is a systems view that connects SPBE/Satu Data/PDP compliance to DS–AI operations across the lifecycle (governance ? pipeline/feature store ? training/validation ? deployment ? MLOps & audit), and a graduate-level research agenda on causal impact and federated collaboration across agencies.
Pemanfaatan Local Large Language Model dalam Software Effort Estimation Menggunakan Metode LOOCV Diyan Sueka Buana Putra; Dewa Made Wiharta; Widyadi Setiawan; Ida Bagus Gede Manuaba
Majalah Ilmiah Teknologi Elektro Vol 25 No 1 (2026): (Januari - Juni) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2026.v25.01.p13

Abstract

 Software Effort Estimation (SEE) in the early stages of a project is generally confronted with minimal technical specifications and high ambiguity in requirement documents. Large Language Models (LLMs) offer the potential to automate information extraction from unstructured natural language text. This exploratory study evaluates the standalone performance of a local LLM (Phi-3 Mini architecture) in predicting man-days effort using a within-company historical dataset from a local digital agency. The experiment was conducted using the Leave-One-Out Cross-Validation (LOOCV) method to ensure objective evaluation within a limited data population. Model optimization utilized Chain-of-Thought (CoT) prompt engineering combined with a methodological strategy of few-shot selection based on boundary distribution (Min, Median, Max) to serve as cognitive anchors. Qualitative analysis proves that the LLM possesses high semantic intelligence in evaluating relative project complexity. However, quantitative evaluation using MMRE, PRED(25), and Coefficient of Determination (R-squared) indicates fundamental limitations in standalone mathematical regression. The model achieved an MMRE of 0.6963 and a PRED(25) of 0.25, performing below the Mean Baseline (MMRE 0.44) with a negative R-squared of -2.038. These results confirm that while LLMs are proficient in understanding context, they struggle with precise numerical interpolation. Consequently, this study recommends a hybrid estimation framework for future research, positioning the LLM purely as a qualitative feature extractor for conventional Machine Learning algorithms to produce robust and precise estimation accuracy.
Co-Authors A.A Ngurah Amrita Abdurrahman Sayudi Anak Agung Gede Maharta Pemayun Asep Andang Ayu Rahmawati Devanka Arya Levin Dewa Ayu Sri Santiari Dewa Made Wiharta Diyan Sueka Buana Putra Dwi Suryadewi Gede Patrianaya Margayasa Wirsuyana Gede Patrianaya Margayasa Wirsuyana I Dewa Nyoman Anom Manuaba I G.N. Ayrthon Senapati I Gede Bayu Suarsa I Gede Ketut Ari Wirawan I Gusti Ngurah Gede Agung Suniantara I Made Agus Artha Putra I Made Anggi Wahyudi Artha I Made Mataram I Made Oka Widyantara I Made Suartika I N Satya Kumara I Nyoman Wira Mastika I Putu Riasa I Wayan Eka Krisna Putra Indra Sukadiana Putra Jonatan Sinurat Kadek Dwi Mahardika Adnyana Lie Jasa Linawati . Linawati Linawati Made Dika Nugraha Made Sudarma Ngurah Narendra Dananjaya NMAED Wirastuti Putu Arya Mertasana Rukmi Sari Hartati Sylvi Oktavia Ginting Tjok Gede Indra Partha Widyadi Setiawan