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JOINCS (Journal of Informatics, Network, and Computer Science)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : -     EISSN : 25415123     DOI : https://doi.org/10.21070/joincs
Core Subject : Science,
Computer Science & IT
JOINCS publishes original research papers in computer science and related subjects in system science, with consideration to the relevant mathematical theory. Applications or technical reports oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. JOINCS also welcomes research contributions on the traditional subjects such as : Theory of automata, algorithms and its complexity. But not limited to contemporary subjects such as: • Big Data • Internet of thing (IoT) • Parallel & distributed computing • Computer networks and its security • Neural networks • Computational learning theory • Database theory & practice • Computer modelling of complex systems • Decentralized Systems • Information Management in the Enterprise Context • Database related technical solutions for Information Quality • Information Quality in the context of Computer Science and Information Technology • Game Techology • Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 85 Documents
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Design and Construction of a Long Range (LoRa) Based Rat Pest Monitoring Information System Model on Agricultural Land: Rancang Bangun Model Sistem Informasi Monitoring Hama Tikus Pada Lahan Pertanian Berbasis Long Range (LoRa) Vina Oktaviani; Baso Maruddani; Muhammad Rohidh Alfayidh
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i2.1695

Abstract

Penelitian ini menyajikan perancangan dan implementasi model sistem informasi monitoring hama pertanian berbasis Long Range (LoRa) yang dikembangkan untuk area persawahan terpencil dengan keterbatasan jaringan internet. Serangan hama tikus berkontribusi besar terhadap penurunan produktivitas padi sehingga diperlukan sistem monitoring otomatis yang mampu mendeteksi pergerakan hama dan mengirimkan informasi secara real-time. Sistem yang dikembangkan mengintegrasikan sensor Passive Infrared (PIR) untuk deteksi gerakan, ESP32-CAM untuk akuisisi citra, aktuator ultrasonik untuk pengusiran hama, serta modul LoRa sebagai media transmisi jarak jauh. Data yang diterima diproses dan divisualisasikan melalui dashboard sistem informasi pertanian berbasis web yang menampilkan notifikasi deteksi, citra hama, serta histori monitoring. Hasil pengujian menunjukkan bahwa sensor PIR mampu mendeteksi pergerakan hingga jarak 3 meter dengan tegangan stabil. Komunikasi LoRa dapat beroperasi hingga jarak 300 meter dengan kehilangan paket minimal, sedangkan dashboard sistem informasi berhasil menampilkan aliran data secara real-time dan menyimpan rekam jejak monitoring secara terstruktur. Sistem ini dinilai sesuai diterapkan pada lingkungan pertanian dan mendukung praktik smart farming.
Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems: Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems Ahmed Gheni Dawood; Ekhlas Muthanna Turki
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1699

Abstract

Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based and regulated environment with even stricter requirements from regulations like GDPR, HIPAA, CCPA and future laws on data sovereignty. This paper analyzed FL in depth. It described foundational concepts, architectural approaches, algorithmic approaches, real-world and practical applications and challenges in distributed systems. Key issues such as communication overhead, data heterogeneity, security risks, fairness, scalability, energy efficiency and compliance with regulations were also discussed and analyses were provided on any underpinning implications on FL performance. Seven tables provide comprehensive overviews of the algorithms, datasets, metrics of performance and applications, while nine figures in unique styles visualize trends, comparisons and data analytics to aid readability. Applications were provided in healthcare, IoT, financial sectors, smart cities and autonomous systems which lend evidence to the promise of FL as a revolutionary technology for privacy-respecting related analytics. Future directions for integrating FL highlights potential synergies with emergent technology such as quantum computing, blockchain, edge artificial intelligence and federated generative models, with supported rationales and inferences when necessary. This work provides a comprehensive and definitive reference point to enhance the scope and level of enquiry for researchers and practitioners who are trying to advance the development of distributed machine learning in sensitive situations to ultimately support the emergence of secure, scalable, ethical, and privacy-preserving analytics, which can drive future paradigm shifts
Development of an Automated Attendance System Based on Facial Recognition Using Convolutional Neural Networks (CNN) for Kaca Super Jaya MSME: Pengembangan Sistem Kehadiran Otomatis Menggunakan Pengenalan Wajah Menggunakan Convolutional Neural Network (CNN) terhadap UMKM Kaca Super Jaya Syaeful Anas Aklani; Jetset; Suwarno Suwarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1692

Abstract

Attendance management is a critical component of human resource administration, yet conventional methods such as manual sign-in sheets and card-based systems are often inefficient, error-prone, and vulnerable to manipulation. This study aims to design and implement an automatic attendance system based on face recognition using Convolutional Neural Networks (CNN) for UMKM Kaca Super Jaya. The proposed system replaces manual attendance by enabling real-time, contactless, and automated attendance recording through facial identification. An applied research approach with qualitative methods was employed, involving system development, direct observation, and structured interviews with users. The CNN model was trained using facial image datasets under various conditions, including different lighting levels, facial expressions, and viewing angles, to improve robustness and accuracy. The system architecture integrates a camera as input, a CNN-based face recognition model, a backend server, and a web-based dashboard for attendance monitoring and reporting. Experimental results show that the system achieved an average face recognition accuracy of 96%, demonstrating reliable performance even under suboptimal lighting and non-frontal face angles. The implementation significantly reduced attendance processing time, minimized human error, and lowered the potential for fraudulent practices such as proxy attendance. These findings indicate that CNN-based face recognition is an effective and practical solution for enhancing attendance management efficiency and accuracy in small and medium enterprises.
Comparison of Naive Bayes and KNN for Honey-Mumford Learning Style Classification in Interpersonal Skill: Komparasi Naive Bayes dan KNN untuk Klasifikasi Gaya Belajar Honey-Mumford pada Interpersonal Skill Hari Moerti; Hamzah Setiawan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Developing soft skills competence, particularly interpersonal abilities, often presents a challenge for Informatics students accustomed to technical and structured thinking patterns. The mismatch between teaching methods and student learning preferences can hinder the absorption of non-technical material. This study aims to classify student learning style profiles in the Interpersonal Skill course using a Machine Learning approach based on the Honey-Mumford model (Activist, Reflector, Theorist, Pragmatist). The research methodology employs Educational Data Mining techniques by comparing the performance of Naive Bayes and K-Nearest Neighbor (KNN) algorithms in predicting learning styles based on academic history data and behavioral questionnaires. Experimental results indicate that the Naive Bayes algorithm outperforms KNN in recognizing student characteristic patterns, achieving an accuracy rate of 93.33%. These findings suggest that engineering students possess heterogeneous learning styles; therefore, adaptive and varied teaching strategies are essential to optimize the comprehension of soft skills materia.
Website-Based Digitalization of the Expertise System (SiPAKAR) for Engineering Faculty Lecturers to Support SDGs 8 and 9: Digitalisasi Sistem Kepakaran (SiPAKAR) Dosen Fakultas Teknik Berbasis Website untuk Mendukung SDGs 8 dan 9 Yeni Yulianti; Nur Riska; Ahmad Lubi; Shilmi Arifah; Ali Idrus; Sri Sundari
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i2.1694

Abstract

Study This aim develop system the expertise of the lecturers of the Faculty of Engineering, Universitas Negeri Jakarta (SiPAKAR) is web- based for make things easier search and mapping skill lecturer based on field knowledge, publications, and experience research. Development system use Research and Development (R&D) method with the Waterfall model and algorithm TextRank in keyword extraction​ publication. Research results show that system capable integrate data from Google Scholar, SISTER, and ORCID automatic. Data analysis using statistics descriptive for evaluate level validity and satisfaction TKT users in research This is at level 4-6 which is development system expertise in environment limited, including expert data processing lecturer from internal source. Validation test by experts produce level 'Very Adequate' eligibility (89%), and response users show satisfaction by 85%. SiPAKAR expected support transparency academic, collaboration research, and achievement of SDGs No. 8 (Decent Work and Growth) Economy) and No. 9 (Industry, Innovation, and Infrastructure). Although not yet perfect and still face obstacles, systems This is step strategic For strengthen the link between lecturers, expertise, and collaboration industry-academic. So that the impact more wide achieved, necessary supported by policies, incentives, technology and systematic monitoring.

Page 9 of 9 | Total Record : 85

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