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Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Published by PT. Lentera Ilmu Publisher
ISSN : 30312698     EISSN : 30312698     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) adalah jurnal nasional berbahasa Indonesia yang dikelola oleh Lentera Ilmu Publisher. Jurnal ini memuat hasil penelitian dengan topik-topik penelitian yang berasal dalam cakupan rumpun sistem informasi seperti perancangan sistem informasi, analisis sistem informasi, tata kelola IT, sistem pengambilan keputusan (SPK) dan teknik informatika meliputi rekayasa perangkat lunak, kecerdasan buatan, machine learning serta bidang-bidang lainnya yang terkait ke dalam rumpun ilmu tersebut. Jurnal ini diterbitkan 2 kali dalam 1 tahun yakni pada bulan Februari, dan Agustus dengan periode penerimaan artikel sepanjang tahun. Artikel yang masuk ke jurnal ini akan di-review oleh mitra bestari sebelum diterbitkan. Proses review artikel dilakukan secara double blind review yang mana mitra bestari tidak mengetahui siapa penulis artikel tersebut dan juga sebaliknya penulis tidak mengetahui mitra bestari yang mereview artikel tersebut. Jurnal JAFOTIK merupakan jurnal akses terbuka (open access) sehingga seluruh artikel yang diterbitkan oleh jurnal ini dapat diakses kapan saja dan di mana saja oleh siapa saja tanpa dipungut biaya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 2 (2025): JAFOTIK - August" : 5 Documents clear
Enhancing OSS E-Government Testing via CEG Method Gilang, Raka; Sanmorino, A
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.76

Abstract

The rapid growth of e-government systems has created new opportunities to improve public service delivery, transparency, and efficiency. One prominent initiative in this area is Indonesia’s Online Single Submission (OSS) system, which centralizes licensing and regulatory processes into a digital platform. However, as usage expands, the OSS system faces recurring issues such as functional errors, incomplete test coverage, and inconsistent results that affect user trust and service reliability. To address these challenges, this study applies the Cause-Effect Graphing (CEG) method as a structured testing approach. By translating functional requirements into graphical models, the method enables systematic test case generation that covers both common and complex scenarios. The results show that CEG-based testing achieved higher test coverage (~90%), improved error detection, and reduced redundancy compared to traditional methods. These outcomes demonstrate the potential of CEG to enhance OSS quality and reliability while strengthening confidence in e-government services.
Integrated Information System for Livestock Health Data Management Ramadhan, Sayzajaya
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.77

Abstract

The growing demand for efficient livestock management highlights the importance of reliable health data systems that support decision-making and disease prevention. However, many existing approaches still rely on fragmented and manual processes, leading to inaccuracies, delays, and limited collaboration among stakeholders such as farmers, veterinarians, and government officers. This study proposes an Integrated Information System for Livestock Health Data Management designed to centralize health records, streamline data entry, and enhance reporting accuracy. The system employs structured data modeling through class diagrams, flowcharts, and interface designs to capture farmer reports, veterinary diagnoses, and administrative validations in a coordinated workflow. By integrating these processes, the solution ensures seamless data flow, reduces redundancy, and fosters collaboration across roles. The results demonstrate improved data consistency, faster response times, and stronger support for monitoring livestock health. This approach provides a practical and replicable framework to modernize livestock health management systems.
Bridging Educational Inequalities with Future AI in Advancing SDG 4 Anwar, Ican
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.82

Abstract

Educational inequality remains a persistent challenge in many developing contexts, where limited resources, large class sizes, and high dropout rates prevent students from achieving their full potential. This study aims to explore how future applications of Artificial Intelligence (AI) can bridge these gaps and support the achievement of Sustainable Development Goal 4 (SDG 4) on quality education. The research adopts a mixed-methods approach, combining case study analysis of AI-driven initiatives with scenario-based calculations of potential benefits in time, cost, and student reach. By examining areas such as AI tutoring, automated grading, predictive dropout interventions, and personalized learning, the study highlights both the opportunities and limitations of AI in education. The contribution of this work lies in proposing a practical framework that illustrates how AI can reduce disparities, optimize resource use, and enhance inclusivity, ultimately offering a pathway toward more equitable and sustainable education systems.
The Design of a Smart Livestock Monitoring System Firdaus, Ananda Ade
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.83

Abstract

The livestock industry faces challenges in maintaining animal health, productivity, and welfare due to reliance on manual observation and delayed detection of health issues. This study aims to design a smart livestock monitoring system that leverages modern technologies to support farmers with timely, data-driven decision-making. The proposed system integrates sensor-based data collection, wireless communication, and a centralized platform for processing and visualization. Through this approach, key parameters such as animal health, activity patterns, and environmental conditions are monitored in real time. The results demonstrate that the system minimizes manual workload, reduces the risk of unnoticed illnesses, and enhances overall resource utilization. The main contribution of this study is the development of an affordable, scalable, and user-friendly system that empowers farmers, including those in small- and medium-scale operations, to adopt digital solutions for sustainable livestock management.
Understanding and Mitigating AI-Generated Hoax Information Zahra, Y
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.84

Abstract

The rapid advancement of artificial intelligence has enabled the creation of highly convincing hoax information, posing serious challenges to information integrity and public trust. This study aims to understand the characteristics of AI-generated hoaxes and propose effective strategies to detect and mitigate their impact. A mixed-method framework was adopted, combining content analysis of AI-generated texts to identify patterns, vulnerabilities, and intervention points, with machine learning techniques for detection and social analysis to capture human and policy dimensions. Validation of the framework demonstrated improved detection accuracy, reduced misinformation reach, and stronger user resilience when supported by transparency measures and digital literacy efforts. The study contributes by offering a structured detection–response cycle that integrates technical, social, and policy approaches. This framework provides governments, organizations, and individuals with practical tools to anticipate and respond to the risks of AI-driven misinformation, ultimately strengthening digital resilience.

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