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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 77 Documents
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Rancang Bangun Game 3D Pembelajaran Nama, Jenis, dan Perkembangbiakan Hewan dan Tumbuhan: Design and Construction of a 3D Game for Learning Names, Types, and Reproduction of Animals and Plants kurmidi, shidqul ibnu zaim
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1647

Abstract

Abstrak. Dalam pelajaran Biologi atau IPA di Sekolah Dasar, terdapat materi yang membahas tentang Hewan dan Tumbuhan, yang mencakup banyak gambar yang berkaitan dengan nama, jenis, dan perkembangan hewan dan tumbuhan. Materi seperti ini sering membuat siswa merasa bosan, sehingga mengurangi minat belajar mereka. Hasil wawancara awal dengan guru kelas IV di SDN Kutisari 1 menunjukkan kesulitan dalam mengajar topik-topik tersebut, yang menyebabkan kurangnya antusiasme siswa. Untuk mengatasi hal ini, sebuah permainan edukasi dirancang untuk meningkatkan minat dan pemahaman siswa. Penelitian ini bertujuan merancang game edukasi menggunakan Unity Engine dengan metode MDLC agar pembelajaran tentang hewan dan tumbuhan menjadi lebih menarik. Permainan yang selesai diharapkan dapat membantu siswa kelas IV memahami materi lebih efektif. Kata kunci: Laptop, Komputer Desktop, Hewan dan Tumbuhan, MDLC, Game, Unity3d
Development of a Web-Based Canteen Delivery Application Using QR-Code and SAW Method at Untag Surabaya: Pembuatan Aplikasi Pesan Antar dengan Penerapan QR-Code di Kantin Untag Surabaya Berbasis Web Menggunakan Metode Simple Additive Weighting Galang Kurniawan, Reza; Koesdijarto, Roenadi
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1648

Abstract

The system developed is a web-based delivery application that uses QR-Codes to indicate the order location containing a link to the ordering website and the order's pickup point. This research aims to develop the application for the cafeteria at Universitas 17 Agustus 1945 Surabaya, recommending popular foods that are often bought utilizing the Simple Additive Weighting (SAW) approach based on parameters like price, taste, service, and portion size. The results show that the application functions according to specifications, with all features operating correctly and being responsive to user input. Testing with 30 respondents using the UMUX Lite method resulted in an average score of 80.497, indicating that the application falls within the acceptable category. This application is expected to be well-implemented in the Untag Surabaya cafeteria, providing convenience and efficiency in ordering and transactions, and contributing to the development of web and mobile-based information systems using the SAW method and QR-Code technology.
Website Design For Estimating Time And Cost Of Replating And Painting Ship Repair at PT.X: Desain Website Estimasi Waktu dan Biaya Pelapisan Ulang dan Pengecatan Perbaikan Kapal di PT.X HERDIANTI, WINDA; Betty, ariani; prasetyawati, dian; azafa, bima
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1667

Abstract

PT. X has been experiencing problems in estimating time and cost data in the replating and painting process on ships. This obstacle is due to the fact that the surveyor owner has difficulty finding data manually which is piled up in the office. Therefore, the researcher designed a website to estimate the time and cost in the process of replating and painting the ship. In this website, the surveyor owner needs to fill the main dimension of the ship data and work. According to the work that has been done, with a comparison of the history of previous work stored on the website, it will speed up the estimation calculation process. The design of this website has been tested on PT. X and get the great result
Exploring the Integration of Artificial Intelligence and IoT in Smart Farming: A Systematic Review: Menjelajahi Integrasi Kecerdasan Buatan dengan IoT dalam Pertanian Cerdas: Tinjauan Sistematis Rosadi, Aswin; Hadi, Mokh Sholihul
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1668

Abstract

Abstract – This study examines the integrationof artificial intelligence (AI) and the Internet ofThings (IoT) in smart farming through asystematic literature review. This researchfocuses on the application of AI, the AIoTarchitecture, the datasets used, and theproblems solved by this technology. The mainproblems faced are the complexity oftechnology integration and the limitations ofinfrastructure in implementation in the field.The purpose of the research is to provide acomprehensive understanding of theadvancements and challenges of AIoTtechnology in the agricultural sector. Themethod used follows the guidance ofKitchenham (2007) by reviewing the latestrelevant literature. The results show that AIoThas great potential in improving the efficiencyand sustainability of the agricultural sectorthrough efficient data management and datadrivendecision-making. However, the successof the implementation of this technology ishighly dependent on the availability of qualitydatasets and the adaptability of the technologyat scale. This research provides practicalrecommendations for the development andapplication of AIoT in various smartagriculture scenarios in the future.
Comparison of Data Mining Model Performance in Heart Disease Detection with Feature Selection Application: Perbandingan Kinerja Model Data Mining Dalam Deteksi Penyakit Jantung Dengan Penerapan Feature Selection Wahyudin, Widya Cholid; Sutikno, Tole; Umar, Rusydi; Ridwan, Ahmad
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1669

Abstract

Penyakit jantung merupakan penyebab utama kematian di seluruh dunia, sehingga deteksi dini sangat penting untuk meningkatkan harapan hidup pasien. Dengan kemajuan teknologi data mining dan machine learning, prediksi penyakit jantung dapat dilakukan lebih akurat. Penelitian ini membandingkan kinerja prediksi model Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM) dalam mendeteksi penyakit jantung menggunakan UCI Heart Disease Dataset. Teknik feature selection—Filter Method, Wrapper Method (RFE), dan Embedded Method—diterapkan untuk meningkatkan akurasi prediksi dan mengurangi kompleksitas model. Hasil eksperimen menunjukkan bahwa SVM mencapai akurasi tertinggi sebesar 91,2%, diikuti Random Forest dengan 90,7%. Penggunaan feature selection terbukti meningkatkan kinerja model secara signifikan dengan mengurangi dimensi data dan menghindari overfitting. Temuan ini menunjukkan efektivitas SVM dan Random Forest dalam pengembangan sistem prediksi penyakit jantung yang efisien di lingkungan klinis. Kata kunci: Data Mining, Prediksi Penyakit Jantung, Feature Selection, Support Vector Machine
A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset: A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset Hatem Khorsheed, Farah; Abbas Abed , Enas; Hassan Mohammed , Zainab; Badr Khudhair Alwan , Walaa
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.v9i1.1688

Abstract

The rapid evolution of cyber-attacks—particularly zero-day intrusions and ransomware—has intensified the need for intelligent and resilient detection systems capable of handling imbalanced, high-dimensional network traffic. This research proposes a robust hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced anomaly detection using the UGRansome dataset, a realistic benchmark designed for ransomware and zero-day behavior analysis. The methodology integrates advanced preprocessing, including categorical encoding, feature normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance. The hybrid architecture leverages CNN layers for spatial feature extraction and LSTM layers for modeling temporal dependencies, enabling improved detection of emerging and stealthy threats. Experimental results demonstrate superior performance compared to standalone deep learning baselines, achieving 97.89% accuracy, 0.999 macro AUC, and strong detection capability across minority classes. Confusion matrix visualizations and classification metrics confirm the model’s robustness and generalization. The findings highlight the potential of hybrid deep learning models for proactive cybersecurity defense and establish a foundation for future intelligent intrusion detection systems
Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems: Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems Gheni Dawood, Ahmed; Muthanna Turki, Ekhlas
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

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