Journal of Intelligent Systems for Community Development (JISCoDe)
Journal of Intelligent Systems for Community Development (JISCoDe, J. Intell. Syst. Community Dev.) is an interdisciplinary scholarly journal published by the Indonesian Artificial Neural Network Society. It aims to advance research, innovation, and practical applications of intelligent systems in addressing real-world challenges within communities. The journal serves as a platform for academics, researchers, practitioners, and policymakers to explore how emerging technologies can contribute to sustainable, inclusive, and data-driven community development. JISCoDe focuses on integrating artificial intelligence, machine learning, data analytics, and intelligent information systems across domains such as governance, public services, urban development, environmental sustainability, and social innovation. The journal emphasizes solutions that are not only technically robust but also ethically grounded, socially responsible, and contextually relevant—particularly in developing and transitional societies. The journal welcomes original research articles, review papers, case studies, and policy reports that demonstrate the impact of intelligent systems on improving quality of life, enhancing decision-making, and fostering resilient communities. JISCoDe encourages contributions that bridge the gap between technology and society, including but not limited to: Intelligent systems for public policy and governance Smart cities and digital communities AI for social good and humanitarian applications Data-driven decision-making and analytics Sustainable and inclusive technological innovations Human-centered and ethical AI systems All submissions undergo a rigorous peer-review process to ensure high academic quality, originality, and relevance. The journal is committed to promoting open knowledge, interdisciplinary collaboration, and impactful research that supports community development at local, national, and global levels.
Articles
6 Documents
<![CDATA[Portable Solar Power Generation Technology for Strengthening Field Medical Services During Flood Disaster Situations ]]>
<![CDATA[Dickyansyah Dickyansyah]]>;
<![CDATA[Inna Ekawati]]>;
<![CDATA[Annisa Firasanti]]>;
<![CDATA[Setyo Supratno]]>;
<![CDATA[Muhammad Ilyas Sikki]]>;
<![CDATA[MuhammadAmin Bakri]]>;
<![CDATA[Retno Nugroho Whidhiasih]]>;
<![CDATA[Malikus Sumadyo]]>;
<![CDATA[Sugeng Sugeng]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 1 (2026): 2026: JISCoDe Volume 1 Issue 1 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[Flood disasters frequently disrupt health services due to limited access to electricity in affected areas. This problem hinders the operation of field medical services, particularly for lighting, the operation of basic medical equipment, and emergency communication. This community service activity aims to develop and implement portable Solar Power Generation (SPG) technology as an independent electricity source to support field medical services during flood disaster situations. The implementation method was carried out through a multidisciplinary collaborative approach involving lecturers from different study programs at the Faculty of Engineering, Universitas Islam 45 Bekasi, in cooperation with the Bekasi Branch of the Indonesian General Practitioners Association (PDUI) as a partner. The activity stages consisted of planning, design and assembly of the portable SPG unit, functional testing, implementation, mentoring, and activity evaluation. The results show that the portable SPG unit was successfully developed in a compact, easily portable form capable of providing a stable electricity supply to support field medical services. The partner gave a positive response to the use of the device, considering it practical, safe, and effective for use in disaster emergency conditions. This activity demonstrates that the application of portable SPG-based renewable energy technology can serve as an appropriate solution to support humanitarian services and strengthen the readiness of health services in disaster-prone areas.]]>
<![CDATA[Affordable Housing, Urban Analytics, and Inclusive Infrastructure Planning ]]>
<![CDATA[Herlawati Herlawati]]>;
<![CDATA[rahmadya trias handayanto]]>;
<![CDATA[Teddy Mantoro]]>;
<![CDATA[Media Anugerah Ayu]]>;
<![CDATA[Nitin Kumar Tripathi]]>;
<![CDATA[Pramod Kumar]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 1 (2026): 2026: JISCoDe Volume 1 Issue 1 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[Affordable housing, particularly in developing countries such as Indonesia, has become a major government priority through the Ministry of Housing and Residential Areas of the Republic of Indonesia. Adequate housing does not function in isolation but must be supported by complementary infrastructure such as commercial areas, roads, transportation networks, healthcare facilities, and other public service centers. Housing development should therefore be inclusive by adopting a mixed-use development approach and avoiding overly rigid zoning systems, since each land-use category is interconnected with and dependent on other land-use types. To address this challenge, urban analytics is required. This study applies land-use optimization based on four key objectives: compactness, compatibility, dependency, and suitability. By implementing clustering techniques, several potential central nodes were identified as suitable hubs for specific land-use types, including commercial centers, vertical residential districts, and industrial zones.]]>
<![CDATA[Road Damage Segmentation Using U-Net and FCN Based on Digital Imagery ]]>
<![CDATA[Sultan Ahmad Rizki Badani]]>;
<![CDATA[Herlawati Herlawati]]>;
<![CDATA[Sugiyatno Sugiyatno]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 1 (2026): 2026: JISCoDe Volume 1 Issue 1 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[Road damage is an infrastructure problem that affects public safety and transportation mobility. Manual road damage identification is considered less effective because it is time-consuming and subjective. This study aimed to implement semantic segmentation using the U-Net and Fully Convolutional Network (FCN) architectures for road damage detection based on digital images. The research used the Cross Industry Standard Process for Data Mining (CRISP-DM) method with datasets consisting of secondary PotholeMix data and primary data collected in West Bekasi. Model training was conducted using 256×256 pixel images with evaluation metrics including Accuracy, Dice Score Coefficient, Intersection over Union (IoU), and Loss. The results showed that the U-Net model achieved better performance than FCN with an Accuracy of 0.9652, Dice Score Coefficient of 0.9596, IoU of 0.9267, and Loss of 0.0674. Furthermore, the model was successfully implemented into a Flutter-based mobile application for automatic road damage identification and monitoring.]]>
<![CDATA[Image-Based Facial Skin Undertone Classification Using ResNet50 and MobileNetV2 ]]>
<![CDATA[Afina Putri Dzulqiyana]]>;
<![CDATA[Herlawati Herlawati]]>;
<![CDATA[Andy Achmad Hendharsetiawan]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 1 (2026): 2026: JISCoDe Volume 1 Issue 1 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[This study aims to develop an automatic facial skin undertone classification system based on deep learning using facial images. The problem stems from the manual undertone identification process, which is subjective and potentially inconsistent when determining skin color categories. The method uses a Convolutional Neural Network (CNN) and compares two architectures, ResNet50 and MobileNetV2, for classifying warm, cool, and neutral skin undertones. The dataset combines secondary data from the Hugging Face platform and primary data obtained through direct facial image capture. The study follows the CRISP-DM process, including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. ResNet50 achieved an accuracy of 89.3% and a weighted F1-score of 0.893, whereas MobileNetV2 achieved an accuracy of 68.3% and a weighted F1-score of 0.684. Thus, ResNet50 demonstrated better and more stable performance for facial skin undertone classification.]]>
<![CDATA[Food and Beverage Sales Prediction Using Linear Regression and Random Forest Regression at Ayam Serayu Restaurant Bekasi ]]>
<![CDATA[Nabila Ramadhani Sari]]>;
<![CDATA[Herlawati Herlawati]]>;
<![CDATA[Prima Dina Atika]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 1 (2026): 2026: JISCoDe Volume 1 Issue 1 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[The large number of sales transactions at Ayam Serayu Restaurant Bekasi creates challenges in managing and analyzing sales data. Manual processes make it difficult to predict future sales, affecting inventory management and decision-making. Therefore, an accurate prediction method is needed. This study applies Linear Regression and Random Forest Regression to predict sales based on historical data. The research stages included data collection, preprocessing, modeling, and evaluation using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Random Forest Regression provides better accuracy than Linear Regression. The resulting model is expected to improve operational efficiency and support decision-making at Ayam Serayu Restaurant Bekasi.]]>
<![CDATA[Detection and Classification of Indonesian Batik Motifs Using YOLOv11 ]]>
<![CDATA[Mochamad Galih Pradipta]]>;
<![CDATA[Herlawati Herlawati]]>;
<![CDATA[Rakhmat Purnomo]]>
<![CDATA[ Journal of Intelligent Systems for Community Development Vol. 1 No. 2 (2026): 2026: JISCoDe Volume 1 Issue 2 Year 2026 ]]>
Publisher : <![CDATA[Indonesian Artificial Neural Network Society ]]>
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<![CDATA[The complexity of asymmetric visual patterns and overlapping ornaments in batik motifs presents challenges for automatic identification in modern applications. The limitations of conventional classification methods have created a need for computer vision techniques capable of accurate object localization. This study aimed to detect and classify various Indonesian batik motifs in real time using the YOLOv11 model. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM), consisting of six structured phases, from business understanding to system deployment. The study focused on major variations of batik motifs originating from different regions of Indonesia, with model performance evaluated using the mean Average Precision (mAP) metric. The results demonstrated that: (1) the YOLOv11 model achieved an mAP50 of 77%; (2) data augmentation effectively reduced the risk of model overfitting; and (3) the trained detection model was successfully integrated into a web-based platform, enabling users to perform real-time image testing.]]>
