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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 79 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
Ensemble Deep Learning Strategy for Handling Imbalanced Credit Card Fraud Data: Strategi Pembelajaran Mendalam Ensemble untuk Menangani Data Penipuan Kartu Kredit yang Tidak Seimbang Hassan Mohammed, Zainab; Hatem Khorsheed, Farah; Jabbar Ahmed, Ghazwan
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.v8i2.1670

Abstract

Credit card fraud remains a major challenge in the financial sector due to its dynamic nature and highly imbalanced transaction data. This study presents a robust deep ensemble learning approach that integrates spatial, sequential, and temporal learning capabilities. A series of preprocessing steps were applied, including feature normalization, class-label separation, and class rebalancing using SMOTE. The model architecture combines convolutional, recurrent, and long short-term memory layers to capture diverse fraud patterns. These components are merged and passed through dense and dropout layers for optimal binary classification. The datasets used are generated from real-world credit card transactions, ensuring practical relevance. On the test set, the proposed model achieved 99.7% accuracy, 99.6% precision, 99.9% recall, and 99.8% F1-score. The training and validation loss curves showed smooth convergence without any overfitting, confirming model stability. To ensure reliability, 3-fold stratified cross-validation was performed on the balanced dataset. The average metrics across folds included 99.76% accuracy, 99.70% precision, 99.85% recall, and 99.77% F1-score. These results underscore the generalization capability and consistent prediction performance of the model. Comparative analysis showed that the group model outperformed individual CNN, RNN, and LSTM architectures. The hybrid strategy benefits from the spatial extraction of CNN, sequence modeling of RNN, and memory retention of LSTM. By integrating these strengths, the model effectively detects subtle and complex fraud patterns. This approach provides a scalable and reliable solution for real-time fraud detection in imbalanced credit card datasets.
Designing an Assistive Tool for Visually Impaired People Based on Object Detection Technique: Merancang Alat Bantu Bagi Penyandang Disabilitas Visual Berbasis Teknik Deteksi Objek Jabbar Ahmed, Ghazwan; Hatem Khorsheed, Farah; Kadhim Zaidan, Fadhil
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.v8i2.1672

Abstract

Visually impaired individuals often face significant challenges in navigating their environments due to limited access to visual information. To address this issue, we propose an assistive tool designed to operate on a PC. The focus of this research is on developing an efficient, lightweight object detection system to ensure real-time performance while maintaining compatibility with low-resource setups. The proposed system enhances the autonomy and accessibility of visually impaired individuals by providing audio descriptions of their surroundings through the processing of live-streaming video. The core of the system is an object detection module based on the state-of-the-art YOLO7 model, designed to identify multiple objects in real-time within the user's environment. The system processes video frames captured by a camera, identifies objects, and delivers the results as audio descriptions using the pyttsx3 text-to-speech library, ensuring offline functionality and robust performance. The system demonstrates satisfactory results, achieving inference speeds ranging from 0.12 to 1.14 seconds for object detection, as evaluated through quantitative metrics and subjective assessments. In conclusion, the proposed tool effectively aids visually impaired individuals by providing accurate and timely audio descriptions, thereby promoting greater independence and accessibility.
Comparative Study of Convolutional Neural Network Architectures for Automated Classification of Leukemia in Blood Smear Images: Studi Perbandingan Arsitektur Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Otomatis Leukemia pada Citra Apusan Darah Raid Hameed, Marwa
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.v8i2.1677

Abstract

. Microscopic analysis of peripheral blood smears remains a critical and complex step in leukemia diagnosis, which could greatly benefit from automation using deep learning. In this paper, we compare three different deep learning models for automated classification of leukemia cells: a simple CNN, a ResNet, and a hybrid vision transformer. The Kaggle leukemia image dataset, which includes 15,135 blood smear images, was used. The blood smear images were preprocessed using denoising, normalization, upscaling, and upscaling. Training was performed on high-performance GPUs and evaluated on multiple complex metrics such as F-score, precision, recall, and accuracy. The expected outcomes include identifying the most robust and accurate deep learning model for leukemia classification, providing insights into the strengths and weaknesses of different leukemia subtypes, and demonstrating strategies and the effectiveness of image distortion handling. The results showed that ViT Hybrid models outperformed CNN and ResNet, achieving 89% of accuracy, 88% of precision, 90% of recall, and 89% of F-score.This suggests that hybrid structures hold great promise for improving computer-aided diagnosis in hematology. These findings are expected to contribute significantly to the field of medical image analysis, offering an accurate and scalable diagnostic tool with immediate clinical application.
On-Time Student Graduation Prediction Modeling: A Comparative Analysis of Naive Bayes Algorithm and Other Data Mining Classifications: Pemodelan Prediksi Kelulusan Mahasiswa Tepat Waktu: Analisis Komparatif Algoritma Naive Bayes Dan Klasifikasi Data Mining Lainnya Achmad Ridwan; Sutikno, Tole; Riyadi, Imam; Cholid Wahyudin, Widya
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.v8i2.1679

Abstract

Predicting the on-time graduation of university students is a crucial task in higher education institutions, enabling proactive support and improving institutional effectiveness. This paper presents a comparative analysis of several machine learning algorithms for predicting on-time graduation, with a specific focus on challenging the performance of the Naive Bayes (NB) algorithm. Although often used as a baseline model, the effectiveness of NB in the complex domain of educational data is frequently debated. We compare NB with MultinomialNB and Decision Tree (DT), both widely favored in recent literature. Using a public dataset containing students' academic records, we follow the CRISP-DM methodology, incorporating feature selection and SMOTE to address class imbalance. The models are evaluated using accuracy, precision, recall, and F1-score metrics. Our results show that while Decision Tree achieves the highest accuracy, Naive Bayes offers an appealing balance of performance, computational efficiency, and interpretability, making it a strong candidate for implementation in early warning systems at universities. This study provides empirical evidence on the role of Naive Bayes in the current landscape of educational data mining. The classification results show an accuracy of 0.82 for Naive Bayes, 0.81 for MultinomialNB, and 0.85 for Decision Tree.

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