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All Journal Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Renewable Energy and Smart Device Computer Architecture and Signal Processing
Dwi Atmodjo WP
Perbanas Institute
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Design and Performance Evaluation of Energy Efficient Heterogeneous Microprocessor Architectures for Real Time Signal Processing in Edge IoT Systems Dani Sasmoko; Widya Aryani; Dwi Atmodjo WP
Computer Architecture and Signal Processing Vol. 1 No. 1 (2026): March: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i1.37

Abstract

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.
Peningkatan Literasi Digital Warga Binaan Melalui Pengenalan Website di Lapas Kelas II A Salemba Winny Purbaratri; Dwi Atmodjo WP; M. Isnin Faried; M Syaiful Fajri; Raka Fahlevi; Kinkin Damai Sakinah; Adrian Sakha Shabia
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 7, No 2 (2026)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v7i2.7777

Abstract

Kesenjangan literasi digital masih menjadi tantangan bagi warga binaan lembaga pemasyarakatan, padahal kemampuan memahami dan memanfaatkan teknologi informasi merupakan bekal penting untuk reintegrasi sosial dan kemandirian ekonomi pasca-pembinaan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital dasar melalui pengenalan konsep website bagi warga binaan Lapas Kelas II A Salemba sebagai mitra pelaksanaan. Sasaran kegiatan adalah 30 warga binaan yang dipilih bersama pihak pembinaan Lapas. Metode pelaksanaan menggunakan pendekatan participatory community-based learning melalui empat sesi terstruktur: pengenalan literasi digital dan fungsi website, pemahaman struktur website, simulasi pembuatan website sederhana berbasis CMS offline (WordPress), serta diskusi reflektif. Evaluasi efektivitas dilakukan menggunakan desain one-group pretest–posttest dengan instrumen kuesioner, observasi, dan wawancara. Hasil menunjukkan peningkatan rata-rata pemahaman peserta dari nilai pretest 45,3 menjadi 82,1 pada posttest, dengan nilai N-Gain sebesar 0,81 (kategori tinggi), serta korelasi positif yang kuat (r = 0,72) antara keaktifan peserta dan peningkatan pemahaman. Secara kualitatif, peserta melaporkan meningkatnya rasa percaya diri, motivasi belajar teknologi, dan pemahaman potensi website untuk aktivitas produktif setelah bebas. Kegiatan ini dinilai berhasil dan direkomendasikan untuk dilanjutkan sebagai program pelatihan lanjutan pembuatan website produktif bagi warga binaan
Hybrid Rule-Based and Anomaly Detection Model for Wholesale Sales Risk Classification Dwi Atmodjo WP; Winny Purbaratri; Lely Priska D Tampubolon; Nani Krisnawaty Tachjar; Deden Prayitno; Budi Indiarto; M Iman Wahyudi
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1184

Abstract

Large-scale wholesale transaction systems face increasing risks from suspicious purchasing patterns, abnormal customer behavior, and operational inconsistencies, while conventional rule-based methods may fail to identify previously unseen patterns. This study develops a decision-level hybrid risk classification model that combines expert-derived business rules with post-hoc anomaly decisions from Isolation Forest and DBSCAN. The modules are orchestrated in Apache Airflow and executed through a scheduled daily DAG for batch transaction monitoring. The model was evaluated retrospectively using 12,476 wholesale transactions recorded over 12 months in a single company. The business rules and ground-truth labels were elicited from the same expert pool; therefore, the separation between model development and evaluation was implemented at the data level through independent training, validation, and test subsets. The Hybrid Rule + Isolation Forest configuration achieved the highest accuracy at 87.5%, compared with 74.3% for the authors' rule-based baseline. The resulting 13.1-percentage-point gain should be interpreted as an internal ablation result rather than a comparison with a state-of-the-art external model. For operational efficiency, the automated workflow processed a batch of 1,000 transactions in 42 seconds, compared with approximately 3 hours of manual processing. These findings suggest that combining interpretable business rules with anomaly detection at the decision level can improve risk classification while retaining operational transparency. However, because the evaluation used data from only one wholesale company and shared expert sources for rules and labels, validation across independent companies and expert groups is required before broader generalization.
Co-Authors Adrian Sakha Shabia Budi Indiarto Dani Sasmoko Deden Prayitno Kinkin Damai Sakinah Lely Priska D Tampubolon M Syaiful Fajri M. Iman Wahyudi M. Isnin Faried Nani Krisnawaty Tachjar Purbaratri, Winny Raka Fahlevi Widya Aryani Winny Purbaratri