cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hindarto
Contact Email
joincs@umsida.ac.id
Phone
+6282336441637
Journal Mail Official
joincs@umsida.ac.id
Editorial Address
https://joincs.umsida.ac.id/index.php/joincs/about/editorialTeam
Location
Kab. sidoarjo,
Jawa timur
INDONESIA
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 7 Documents
 1 
Search results for , issue "Vol. 8 No. 2 (2025): November" : 7 Documents clear
Ensemble Deep Learning Strategy for Handling Imbalanced Credit Card Fraud Data: Strategi Pembelajaran Mendalam Ensemble untuk Menangani Data Penipuan Kartu Kredit yang Tidak Seimbang Zainab Hassan Mohammed; Farah Hatem Khorsheed; Ghazwan Jabbar Ahmed
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 Ghazwan Jabbar Ahmed; Farah Hatem Khorsheed; Fadhil Kadhim Zaidan
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 Marwa Raid Hameed
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; Tole Sutikno; Imam Riyadi; Widya Cholid Wahyudin
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.
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.
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 1 of 1 | Total Record : 7

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 1 (2026): April Vol. 8 No. 2 (2025): November Vol. 8 No. 1 (2025): April Vol. 7 No. 2 (2024): November Vol. 7 No. 1 (2024): April Vol. 6 No. 2 (2023): November Vol. 6 No. 1 (2023): April Vol. 5 No. 2 (2022): November Vol 5 No 1 (2022): April Vol. 4 No. 2 (2021): November Vol 4 No 1 (2021): April Vol. 3 No. 2 (2020): November Vol 3 No 2 (2020): November Vol 3 No 1 (2020): April Vol 2 No 2 (2019): November Vol 2 No 1 (2019): April Vol. 2 No. 1 (2019): April Vol 1 No 1 (2017): April More Issue