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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Informatika CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science Dewa Ruci : Jurnal Pengkajian dan Penciptaan Seni ARSY : Jurnal Aplikasi Riset kepada Masyarakat Journal of Informatics Management and Information Technology Mejuajua SAINSTEK The Indonesian Journal of Computer Science Jurnal Informatika
Rizky Wandri
Universitas Islam Riau
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Other

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Implementation of a Face Recognition API for an Automated Web-based Attendance System using Extreme Programming Rizky Wandri; Mutia Fadhillah; Dwi Fiqri Qurniawan; Eka Deddy Saputra
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6388

Abstract

Manual attendance recording is still vulnerable to data manipulation, entry delays, and high reliance on manual input. This research focuses on the implementation and integration of the Facial Recognition REST API into a web-based attendance information system to automate attendance recording. The novelty of this research lies in the integration of REST API-based facial recognition services with a three-layer architecture consisting of a camera device, a Raspberry Pi 5-based edge server, and a Laravel-based application server, developed using the Extreme Programming (XP) method, resulting in a modular, responsive, and easily scalable attendance system compared to previous approaches. The system was developed in four sprints over one month with an average speed of 15.5 story points per sprint, based on twelve user stories defined in the planning stage. In the facial recognition module, the identification process utilizes a combination of Haar Cascade for facial position detection, FaceNet for facial embedding formation, and Cosine Similarity for similarity measurement against a reference database consisting of 18 students with 6 reference photos per student. System testing was conducted using three class videos with a resolution of 1920×1080 pixels with an average duration of 5 minutes and 28 seconds. Evaluation of the accuracy of the facial recognition model was part of a collaborative study reviewed separately; this paper focuses on the system's integration performance and functionality. Functional testing using black box methods across 20 scenarios demonstrated that all modules—authentication, master data management, facial registration, facial recognition-based attendance, reporting, and error handling—performed according to specifications, with a consistent system response time of 2–4 seconds. The results demonstrate that the system reduces reliance on manual input, improves record-keeping efficiency, and provides real-time attendance data, and is ready for further development for broader adoption in educational settings.
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.
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods Nisa Pirsingki; Rizky Wandri
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12256

Abstract

Social media facilitates user communication, both in positive, negative and neutral aspects. Tiktok is a popular platform that allows users to stay up to date on the latest news, including the major conflict between Palestine and Israel. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. The objective of this study is to evaluate public sentiment regarding the news of Palestinian refugees en route to Rafah. To achieve this purpose, we will examine 2982 comments on TikTok relating to the hashtag #SaveRafah, which will be the data to be trained. Prior to classification, the data will undergo a preprocessing process and TF-IDF weighting. The two classification methods will be compared to ascertain the most accurate approach. Because the data at the labeling stage has a larger percentage of positive data 90.7%, this study will employ the technique SMOTE to address class imbalance in the data set. The results showed that the Naive Bayes Multinomial method with the application of SMOTE produced an accuracy of 85.43%, a precision of 86.22%, a recall of 85.43%, and an f1-score of 85.53%. Meanwhile, the Decision Tree C4.5 method with the application of SMOTE produced an accuracy of 94.23%, a precision of 94.58%, a recall of 94.23%, and an f1-score of 94.22%. Based on the evaluation results, the best method for sentiment analysis of the hashtag #SaveRafah is Decision Tree C4.5.
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 ANDRI KURNIAWAN Arbi Haza Nasution, Arbi Haza Ause Labellapansa, Ause Bima Kristian Pranoto Daulay, Suandi Des Suryani Des Suryani Dwi Fiqri Qurniawan Eka Deddy Saputra Fadhilah, M Rizki Fadhilla, Mutia Fhelia Zahwa Fiqri Qurniawan, Dwi Fitriani Hafiza Oktasia Nasution Hanafiah, Anggi Hendra Gunawan Jerika Mardafora M Rizki Fadhilah M. Rizki Fadhilah Masnur Masnur Mohamad Yatim, Maizatul Hayati Muhammad Akbar Almuttaqin Mutia Fadhilla Mutia Fadhilla Mutia Fadhillah Nisa Pirsingki Nur, Nuriman M Oktasia Nasution, Hafiza Oktaviaani, Rizka Pandini, Pandini Pangestu, Firly Bagdja Pirsingki, Nisa Poppy Camenia Jamil Rachmat Setiawan, Panji Ranggi Ade Febrian Rizdqi Akbar Ramadhan Rizka Oktaviaani Sapitri Sapitri, Sapitri Sari, Putri Indah Setiawan, Panji Rachmat Syefriani Syefriani syefriani Syefriani, Syefriani Therecya Silitonga Yudhi Arta Yudhi Arta Yudhi Arta, Yudhi Zaini Ali Zulfhan Azmal