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Mutia Fadhilla
Universitas Islam Riau
Computer Science & IT Electrical & Electronics Engineering

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Design of the 'Abdimas' Marketplace System: A Digital Platform for PKM Collaboration (Version 0.0) M Rizki Fadhilah; Rizky Wandri; Mutia Fadhilla; Dwi Fiqri Qurniawan; Ause Labellapansa; Hendra Gunawan
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5545

Abstract

Community Service (Pengabdian kepada Masyarakat or PkM) is one of the three core responsibilities (Tridharma) of higher education in Indonesia, alongside education and research. However, many lecturers especially those new to academia face difficulties in identifying suitable community partners. This study addresses that issue through the design of a digital platform called “Abdimas”, intended to function as a marketplace system for matching lecturers with PkM partners. Applying the Design Thinking methodology, focusing on the conceptual and architectural design of the “Abdimas” platform, this study implemented a user-centered design approach. Data from interviews with lecturers, partners, and university administrators were analyzed using thematic analysis to identify core requirements. Based on these findings, the system was designed to support lecturer registration, partner discovery, and need-based matching. Senior lecturers are also supported in exploring new, underserved areas, while partners can publicly express their needs to attract suitable academic collaborators. University administrators can monitor the distribution of PkM activities over time to ensure equity and effectiveness. Unlike existing administrative platforms that often function as one-way reporting tools, the “Abdimas” marketplace introduces a bidirectional matching mechanism that allows partners to actively broadcast their specific community needs, bridging the information gap for lecturers. The system design includes use case diagrams, UI/UX prototypes, an Entity Relationship Diagram (ERD), and blackbox test scenarios to validate functionality. Although still in the design phase (Version 0.0), “Abdimas” has the potential to scale beyond academic users by supporting Corporate Social Responsibility (CSR) initiatives and facilitating student-level community services. This research contributes a structured and scalable system design to improve collaboration and outreach in community service programs within higher education.
Edge-based Face Recognition Pipeline Optimization for Class Attendance System Mutia Fadhilla; Rizky Wandri; Des Suryani; Yudhi Arta
SISTEMASI Vol 15, No 9 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i9.6918

Abstract

Manual attendance recording remains inefficient and may enable proxy attendance, while cloud-based facial recognition introduces network dependency, latency, and biometric-data privacy concerns. Edge processing offers a local alternative, but limited computing resources require pipeline optimization. This study conducted a descriptive single-run evaluation of five optimization strategies on a Raspberry Pi 5: frame downscaling, static region of interest (ROI), prototype caching, sample interval tuning, and face detector comparison. The pipeline used FaceNet embeddings, cosine-similarity-based open-set recognition, and track-based temporal confirmation. Evaluation involved three prerecorded 1920 × 1080 classroom videos totaling 16 min 23 s, an enrollment database of 18 students, and 53 student-video attendance instances. Frame downscaling at 0.75 reduced runtime from 1,274.83 s to 947.72 s, equivalent to 25.66%, while confirmed attendance instances decreased slightly from 47 to 46. Effective sampled-frame throughput increased by 34.62%, and average RAM usage decreased by 6.86%, although CPU utilization and peak temperature increased. Prototype caching reduced preparation time but had limited effect on end-to-end runtime, whereas static ROI, sampling intervals above 5 s, and YuNet did not improve overall pipeline performance. Frame downscaling was therefore the most promising strategy in the evaluated setup. However, repeated trials and independent classroom-video testing are required before generalizing the results or considering the pipeline deployment-ready.
Co-Authors Ause Labellapansa, Ause Des Suryani Dwi Fiqri Qurniawan Hendra Gunawan M Rizki Fadhilah Rizky Wandri Yudhi Arta