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Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30318904     EISSN : 30318912     DOI : 10.61132
Core Subject : Science,
Computer Science & IT
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika memuat naskah hasil-hasil penelitian di bidang Sistem Informasi dan Teknik Informatika
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 297 Documents
 26   27   28   29   30 
Analisis dan Evaluasi Website Fakultas Sains dan Teknologi Sebagai Sistem Informasi Layanan Akademik Mellyana Rafizah
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1687

Abstract

A faculty website serves as vital infrastructure in supporting the digital transformation of higher education institutions. However, many academic portals currently remain static and limited to one-way information dissemination. This study aims to analyze and evaluate the quality of the Faculty of Science and Technology website as an academic service information system. A comprehensive system evaluation was conducted by integrating the WebQual 4.0 model and the PIECES Framework to assess quality across usability, information quality, service interaction, performance, economy, control, efficiency, and service domains. The research adopted a descriptive quantitative approach, with data gathered via direct observation, literature reviews, and structured questionnaires using a 1–5 Likert scale distributed to platform users. The empirical results demonstrate that the website functions adequately as a basic academic information provider. Nonetheless, significant deficiencies were identified regarding inconsistent content updates, a lack of two-way interactive service features, and limited data access security controls. Consequently, this study formulates structural optimization recommendations, including responsive user interface and experience (UI/UX) redesign, strengthening internal access control management, and full academic system integration to cultivate a transparent and adaptive digital service ecosystem tailored to the needs of the modern academic community.
Analisis Pengukuran Kualitas Aplikasi Pendataan Warga Menggunakan Metode Six Sigma pada Perumahan Griya Sejahtera Palembang Monika Sima; Tata Sutabri
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1690

Abstract

The rapid advancement of digital technology has encouraged various sectors, including government and housing management, to adopt application-based systems for data collection and administration. The Citizen Data Collection Application at Griya Sejahtera Housing, Palembang, aims to facilitate population data management. However, its quality and performance need to be measured systematically to ensure it meets user expectations. This study applies the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) framework to measure and analyze the quality of this application. The research employs a quantitative approach by collecting user complaint data, defect identification, and questionnaire analysis. The results reveal that the application's sigma level is at 2.18, indicating relatively low quality with a DPMO (Defects Per Million Opportunities) value of 279,069. The highest defect types found include data entry errors, slow application response, and unsynchronized synchronization. Proposed improvements include enhanced feature testing, user interface simplification, and periodic evaluation. This study contributes to efforts to improve the quality of application-based public services and offers practical recommendations for housing managers and application developers.
Perancangan Antarmuka Pengguna Sistem Informasi Ekstrakurikuler Berbasis Web Menggunakan Balsamiq Mockups Sani Yuliani; Sulidar Fitri; Sarmidi Sarmidi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1697

Abstract

The development of information technology encourages schools to utilize web-based information systems to support the management of extracurricular activities. However, many schools still use manual systems so that conveying information, registering members and managing extracurricular data is less effective. This research aims to design a user interface for a web-based extracurricular information system using the User-Centered Design (USD) method and Balsamiq Mockups as a medium for creating system display wireframes. The research method used includes the user needs analysis stage through observation and interviews, making wireframes, making prototypes and evaluating interface designs based on usability principles.  The results of the research are user interface designs that include login pages, dashboards, extracurricular registration, member data, attendance, student achievements and membership reports. The evaluation results of 32 respondents obtained a score of 1,448 out of a maximum score of 1,600 which is in the very good category. The implications of this research show that the interface design is expected to help users and help schools understand the flow of the system and increase the ease of use of the web-based extracurricular information system, speed up access to information, and support extracurricular data documentation in a more structured manner before the system is implemented.
Implementasi Naive Bayes untuk Memprediksi Prestasi Belajar Siswa MTs Fathurrahman Padang Tualang Alfin Noval Permana; Juwita Adinda; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1698

Abstract

Student learning achievement is an important indicator in evaluating the success of the learning process in madrasah. This study aims to implement the Naive Bayes algorithm in predicting the learning achievement of Grade VII, VIII, and IX students at MTs Fathurrahman Padang Tualang. The research data uses 77 students from three grade levels; 38 students (49.35%) are classified as Achieving and 39 students (50.65%) as Underachieving. Model evaluation using the Hold-Out Split Data method (80% training, 20% testing) achieved Accuracy of 93.75%, Precision of 100.00%, and Recall of 87.50%, confirming the model's high reliability. The UAS variable is the strongest predictor with a mean difference of 13.59 points between classes (μAchieving = 80.92 vs μUnderachieving = 67.33). This research proves that Naive Bayes is an effective and efficient classification algorithm for predicting student learning achievement across grade levels in madrasah tsanawiyah. The proposed model can support educators in identifying students with potential academic difficulties, enabling early intervention and more targeted learning strategies. Furthermore, the implementation of predictive analytics provides valuable insights for improving academic management and supporting data-driven decision-making in educational institutions.
Prediksi Tingkat Pemahaman Siswa Smp Swasta Tenera Berdasarkan Aktivitas Belajar Menggunakan Naive Bayes M. Dimas Prayoga; Mona Ayunda; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1699

Abstract

This study aims to develop a prediction model for determining the comprehension level of students at SMP Swasta Tenera using the Naive Bayes algorithm based on academic learning activities. The prediction model utilizes student learning data, including subject grades and attendance records, as the main variables to classify students’ comprehension levels. The data used in this study were collected from 90 students consisting of Grade VII (28 students), Grade VIII (23 students), and Grade IX (39 students), covering performance data from 10 subjects. The research method applies a quantitative approach with data processing and classification analysis using the Naive Bayes algorithm. The evaluation results show that the developed model achieved an accuracy level of 83.33%, with a precision value of 78.12%, recall of 98.04%, and an F1-score of 86.96%. The prediction results indicate that Grade VII students have the lowest comprehension level at 42.9%, followed by Grade VIII at 60.9% and Grade IX at 64.1%. This research demonstrates that machine learning-based prediction systems can support educational decision-making by identifying students who require learning assistance and enabling schools to implement faster, more targeted, and effective academic interventions.  
Optimasi Keuntungan Penjualan Produk Warung Sembako Anggara Menggunakan Linear Programming Shandy Pratama; Gilang Anggara; M. Andre Ansyah; Ismar Hidayat
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1707

Abstract

Optimalisasi keuntungan merupakan isu manajerial penting bagi usaha mikro ritel karena modal, kapasitas penyimpanan, dan permintaan produk terbatas. Studi ini bertujuan untuk merumuskan model pemrograman linier untuk menentukan alokasi pembelian optimal dari beberapa kategori produk di Warung Sembako Anggara. Penelitian ini menggunakan pendekatan studi kasus deskriptif kuantitatif berdasarkan catatan modal dan keuntungan dari enam kategori produk: barang kebutuhan pokok, makanan ringan anak-anak, beras, bahan bakar ritel, voucher data, dan minuman kemasan. Setiap kategori dikonversi ke dalam unit pembelian yang relevan, seperti kemasan, bungkus, kilogram, liter, unit voucher, dan karton minuman. Fungsi tujuan memaksimalkan total keuntungan, sedangkan kendala terdiri dari modal yang tersedia dan batas pembelian minimum-maksimum untuk setiap kategori. Hasil menunjukkan bahwa alokasi awal menggunakan modal sebesar Rp 13.051.000 dan menghasilkan keuntungan sebesar Rp 2.701.500. Alokasi pemrograman linier menggunakan modal sebesar Rp 13.050.167 dan menghasilkan perkiraan keuntungan sebesar Rp 2.857.958. Oleh karena itu, keuntungan meningkat sebesar Rp 156.458 atau 5,79% dibandingkan dengan kondisi awal. Alokasi optimal meningkatkan barang kebutuhan pokok, bahan bakar ritel, dan voucher data, sementara mengurangi produk pelengkap berprioritas rendah dalam batas yang wajar.
Klasifikasi Sentimen Pengguna Terhadap Pembayaran Digital Menggunakan Algoritma Naive Bayes Nayla Syafiah Asmi; -, Sinta Amellyya Sari; Roberto Kaban
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1713

Abstract

Perkembangan teknologi finansial (fintech) di Indonesia telah mendorong meningkatnya penggunaan aplikasi pembayaran digital, salah satunya DANA. Banyaknya ulasan pengguna pada Google Play Store menghasilkan data tekstual yang dapat dimanfaatkan untuk mengetahui persepsi pengguna terhadap kualitas layanan aplikasi. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi DANA menggunakan algoritma Naïve Bayes. Dataset yang digunakan berasal dari Kaggle dengan jumlah 50.000 ulasan yang telah dikategorikan ke dalam tiga kelas sentimen, yaitu positif, negatif, dan netral. Tahapan penelitian meliputi pengumpulan data, preprocessing teks (case folding, cleaning, tokenizing, stopword removal, dan stemming), pembobotan kata menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF), proses klasifikasi menggunakan algoritma Multinomial Naïve Bayes, serta evaluasi model menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model memperoleh nilai accuracy sebesar 79,26%. Kelas sentimen positif memiliki performa terbaik dengan nilai precision sebesar 0,85, recall 0,94, dan F1-score 0,89, sedangkan kelas sentimen netral memiliki nilai recall terendah sebesar 0,19 akibat ketidakseimbangan distribusi data. Berdasarkan hasil tersebut, algoritma Naïve Bayes mampu memberikan kinerja yang cukup baik dalam mengklasifikasikan sentimen ulasan pengguna aplikasi DANA dan dapat dimanfaatkan sebagai salah satu metode analisis opini pengguna untuk mendukung peningkatan kualitas layanan aplikasi pembayaran digital.
Analisis Keamanan Sistem Biometrik terhadap Ancaman Deepfake : Studi Kasus Lonjakan 3.000% Fraud Incidents Periode 2023-2024 Osewa Pallyama Sultan Khadir; Asep Saeppani; Esa Firmansyah; Beben Sutara
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1318

Abstract

The rapid advancement of deepfake technology powered by Generative AI has created a critical security threat to biometric authentication systems, particularly face recognition and voice recognition. Global reports from 2023–2024 indicate a dramatic 3,000% increase in deepfake-enabled identity fraud, emphasizing the urgent need for stronger biometric security measures. This study aims to analyze key vulnerabilities in biometric systems against deepfake attacks, evaluate the effectiveness of existing detection techniques, and develop a layered biometric security framework as a mitigation solution. The research employs a Systematic Literature Review (SLR) of scientific publications and industry reports from 2016–2025, complemented by Lite Expert Validation to assess the feasibility of the proposed framework. The findings reveal that most biometric systems remain vulnerable to advanced face swap, voice cloning, and liveness detection bypass attacks. Detection methods based on frequency–spatial analysis and multi-modal deepfake detection are identified as the most effective, although they require strong operational integration. This study introduces the Biometric Deepfake Security Framework, which incorporates technical, procedural, and adaptive security controls to enhance biometric resilience against digital identity manipulation. The proposed framework is expected to provide practical guidance for organizations relying on biometric authentication to strengthen protection against evolving deepfake threats.
Strategi Adaptasi Pemasaran Digital Produk Olahan Peternakan di Era Industri 4.0: Sebuah Tinjauan Literatur Sistematis (Systematic Literature Review) Rais Mughni Salam; Asep Saeppani; Fadhil Irfan
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1323

Abstract

Digital transformation in the era of Industry 4.0 has forced the livestock sector, particularly processed product SMEs (UMKM), to adapt their marketing strategies. Conventional marketing methods are increasingly limited by geographical boundaries and high operational costs. This study aims to identify and analyze the most effective digital marketing adaptation strategies for processed livestock products using a Systematic Literature Review (SLR) approach. Data were collected from Google Scholar (2020–2025) using the PRISMA method. The results indicate that the most dominant strategies are the utilization of Social Commerce (TikTok & Instagram) for brand awareness and Marketplaces (Shopee/Tokopedia) for transaction efficiency. Furthermore, successful adaptation requires a transition from "selling" to "educating" consumers about product quality. This study provides a strategic map for livestock SMEs to increase sales volume and market reach through digital technology. The findings also reveal that structural barriers, such as limited digital literacy among older farmers and inadequate cold-chain logistics for perishable products, remain significant obstacles to full-scale adoption. These insights offer practical guidance for SME actors and policymakers seeking to accelerate the digital transformation of the livestock processing industry.
Implementasi Otentikasi Berbasis Risiko dan Deteksi Penipuan pada Platform E-Niaga SecureShop Satria Tegar Bimantara; Damar Damar; Rahadian Ronggo Kusumo
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 4 (2026): Juli: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i4.1619

Abstract

The rapid growth of the e-commerce ecosystem has introduced complex cybersecurity threats, particularly account takeovers and transaction fraud. Traditional static authentication and fragmented security modules are no longer sufficient to mitigate these dynamic risks. This research aims to design, implement, and evaluate an integrated security architecture that combines adaptive Risk-Based Authentication with a real-time Order Risk Engine. Utilizing an experimental approach within the Secure Software Development Life Cycle framework, a server-side rendered prototype was developed and subjected to synthetic anomaly injections in an isolated local testbed. The system evaluates operational contexts, such as unfamiliar IP addresses, bruteforce attempts, and abnormal order velocities, using a deterministic scoring mechanism to trigger automated interventions ranging from multi-factor authentication challenges to absolute access blocks. The empirical findings demonstrate that the proposed end-to-end risk scoring engine achieved a zero percent false-positive rate for legitimate users while successfully mitigating all simulated critical threats and account takeover attempts. Furthermore, the integration of stateless session management maintained an exceptionally low computational latency, ensuring a seamless user experience. These results imply that a unified risk-scoring model provides a highly effective, autonomous, and scalable blueprint for securing modern e-commerce platforms against multi-layered exploitations.

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