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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL EDUCATION AND DEVELOPMENT Zonasi: Jurnal Sistem Informasi JURSIMA (Jurnal Sistem Informasi dan Manajemen) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Pendidikan dan Teknologi Indonesia Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Mejuajua Jurnal Pengabdian Pada Masyarakat METHABDI JURSIMA SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) Jurnal Sistem Informasi dan Manajemen Riau Jurnal Teknik Informatika Journal of Soft Computing Exploration Big Data Analytics and Data Science
Bakti, Imam Rangga
Universitas Pasir Pengaraian
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Physics Public Health Social Sciences Other

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Sistem Penilaian Esai Otomatis Berbasis Kecerdasan Buatan Menggunakan Pendekatan Text Mining dan Cosine Similarity Bakti, Imam Rangga; Supriyanto, Asep; Riki Mustafa, Satria
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.4571

Abstract

Evaluasi pembelajaran merupakan komponen penting untuk mengukur tingkat pemahaman siswa terhadap materi yang telah diajarkan. Salah satu bentuk evaluasi yang efektif adalah soal esai, karena mampu mengukur kemampuan berpikir dan pemahaman siswa secara mendalam. Namun, proses penilaian esai secara manual memerlukan waktu yang lama dan berpotensi menimbulkan subjektivitas. Penelitian ini bertujuan untuk membangun sistem penilaian esai otomatis berbasis e-learning menggunakan metode text mining dengan algoritma cosine similarity dan stemming Nazief dan Adriani. Metode yang digunakan dalam penelitian ini adalah metode pengembangan sistem Waterfall yang meliputi tahap analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Proses pengolahan data dilakukan melalui tahapan case folding, tokenizing, filtering, stemming, pembobotan TF-IDF, serta perhitungan cosine similarity untuk menentukan tingkat kemiripan antara jawaban siswa dan jawaban acuan. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu melakukan penilaian esai secara otomatis dengan tingkat kemiripan yang dapat dijadikan dasar dalam pemberian nilai. Sistem ini dapat membantu guru dalam mempercepat proses penilaian, meningkatkan efisiensi, serta mengurangi subjektivitas dalam penilaian. Dengan demikian, sistem penilaian esai otomatis ini dapat menjadi solusi yang efektif dalam mendukung proses evaluasi pembelajaran berbasis teknologi.
A comparative analysis of five textual similarity methods for automatic short answer grading Bakti, Imam Rangga; Jati, Handaru; Nurkhamid, Nurkhamid; Bunda, Yola Permata
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.11

Abstract

This study investigates the application of text mining techniques in Automatic Short Answer Grading (ASAG) by comparing five textual similarity methods: Cosine Similarity, Jaccard Similarity, Dice’s Coefficient, Overlap Coefficient, and Matching Coefficient. The dataset consists of five definition-based questions answered by 25 students in a Human–Computer Interaction course. The data were preprocessed using case folding, tokenization, stop word removal, and stemming. The results show that Cosine Similarity achieved the highest similarity score of 67.00%, followed by Overlap Coefficient (66.67%) and Dice’s Coefficient (63.16%), while Jaccard Similarity and Matching Coefficient produced lower scores of 46.15%. These findings indicate that vector-based similarity methods are more effective in handling variations in sentence structure and keyword usage compared to set-based approaches, particularly for definition-based short answers. This study provides a comparative evaluation of multiple lexical similarity methods within a unified experimental setting, offering practical insights for selecting appropriate techniques in ASAG applications.
Designing Robust Data Quality Governance Strategies for Distributed Software Systems : Integrating Real Time Monitoring and Automated Anomaly Detection Imam Rangga Bakti; Yola Permata Bunda; Mohammad Muhsin
Big Data Analytics and Data Science Vol. 1 No. 1 (2026): March: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i1.21

Abstract

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.
Co-Authors Afriansyah Afriansyah Afriansyah Ahmad Sumantri - Ainur Ramadhani, Muhammad Alifiansyah, Eldi Alwi Hasibuan, Achmad Anwar Syaputra, Sofyan Asep Supriyanto Basorudin Budhi Santri Kusuma Budi Yanto, Budi Bunda, Yola Permata Cut Try Utari Cut Try Utari Deni Oktavia, Deni Dian Rahayu Eddis Syahputra Pane, Eddis Syahputra Elyani Elyani Erni Rouza, Erni Fariza Habibi Fasha, Ulya hajatina hajatina Handaru Jati HAPOSAN HUTAHAEAN Hardianto, Roki Haryadi, Octadino Heriyaldho, Yuri Iskandar Zulkarnain, Wisnu Khamo Waruwu Kurniawan Sinaga Luth Fimawahib Maulia, Sindi Melani, Tasya Mohammad Muhsin Muhammad Fadlan Siregar Muhammad Fadlan Siregar Muhammad Rusdi Nasution, Yandi Novendra, Rizki Nurkhamid, Nurkhamid Nurul Afni Octadino Haryadi Rohimah Rohimah Romi Nasution, Muhammad Satria Riki Mustafa, Satria Riki SAUDAH SAUDAH Sindi Maulia Sofyan Anwar Syahputra Sulaiman, Abidin Suprianto Supriyanto, Asep Syahputra Pane, Eddis Tri Astuti, Missi Turnandes, Yogo Ulum, Zahrul Ulya, Siti Uswatun Hasanah Wirdahchoiriah