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Ramdani Dwi Pamuji
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Journal of Computer Science and Information Technology
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 3090787X     DOI : https://doi.org/10.70716/jocsit
Core Subject : Science,
Computer Science & IT
Journal of Computer Science and Information Technology (JOCSIT) is a scientific journal in computers that contains research results and literature studies, managed by Lembaga Publikasi Ilmiah Nusantara. JOCSIT journal provides a platform for researchers, academics, professionals, practitioners and students to embed and share knowledge in the form of empirical and theoretical research papers, case studies, literature reviews and book reviews related to computer science and information technology research, and or related to it with a range of themes such as Biomedical Application Computer Network and Architecture, Data Mining, E-Business, E-Commerce, E-Government E-Learning, Embedded Systems, Environmental Systems, Fuzzy Logics, Genetic Algorithms, Geographic Information System, High-Performance Computing, Human-Computer Interaction, Image Processing, Internet of Things (IoT), Computer Vision, Information Security, Information Retrieval, Modeling System and Control, Mobile Technology, Neural Networks, Pattern Recognition, Remote Sensing, Robotics, Signal Processing, Smart Home, Smart Sensor Networks. This journal will process all receipts of the script in a double-anonymized review by Bestari partners.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
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Design and Development of a Mobile-Based E-Tourism Application for Promoting Tourism Destinations in Lombok Irwan Prasetya; Mei Lin Chen; Ahmad Fauzi
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2026): Journal of Computer Science and Information Technology, June 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i2.552

Abstract

The advancement of mobile technology has driven digital transformation in the tourism sector, including Lombok Island, which has high tourism potential but still faces limitations in information access and integrated services. This study aims to design and develop a mobile-based e-tourism application to enhance the promotion and accessibility of tourist destinations in Lombok. The research method employs a software engineering approach using the waterfall model, including requirement analysis, system design, implementation, and testing. Data were collected through literature studies and observation of user needs. The results indicate that the developed application provides features such as GPS-based location search, tourism recommendations, multimedia information, and integration with local services. System testing shows good usability and potential to increase tourist visits. In conclusion, mobile-based e-tourism applications can serve as an effective solution to support digital and sustainable tourism promotion in Lombok.
Design and Development of an IoT-Based Smart Farming System for Corn Plant Monitoring in Sumbawa Bayu Pratama; Satria Wijaya; Faiza Irfan
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2026): Journal of Computer Science and Information Technology, June 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i2.557

Abstract

The development of Internet of Things (IoT) technology has provided significant opportunities to improve agricultural efficiency, particularly in corn farming in Sumbawa, which faces challenges related to climate variability and irrigation management. This study aims to design and implement an IoT-based smart farming system capable of monitoring environmental conditions in real-time. The research employs a prototyping approach by integrating soil moisture, temperature, and pH sensors connected to a cloud-based platform. The results indicate that the system improves water usage efficiency by up to 25% and achieves sensor accuracy of approximately 95%. Additionally, the system enables farmers to make data-driven decisions more effectively. Therefore, the implementation of IoT in corn farming in Sumbawa has proven to enhance productivity and resource efficiency.
Early Warning System for Forest and Land Fires in Sumatra Using Computer Vision and Remote Sensing Data Satria Dwi Nanda; Nur Aisyah Ahmad
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2026): Journal of Computer Science and Information Technology, June 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i2.592

Abstract

Forest and land fires in Sumatra pose significant environmental, health, and economic challenges. This study aims to develop an early warning system by integrating computer vision and remote sensing data to improve real-time fire detection accuracy. The method utilizes image processing techniques based on YOLO and CNN algorithms, combined with MODIS and Landsat satellite data for hotspot monitoring. Data analysis is conducted using machine learning approaches and multi-source data integration. The results indicate that the proposed system achieves detection accuracy above 90% and provides earlier warnings compared to conventional methods. The integration of drone imagery and satellite data proves effective in detecting fires at an early stage. This research contributes to the advancement of intelligent fire monitoring systems to support disaster mitigation efforts in Indonesia.
Development of a Web-Based Geographic Information System for Mapping MSME Locations Aditya Dwi Ramadhan; Nabila Iskandar; Muhammad Aiman Hakim
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2026): Journal of Computer Science and Information Technology, June 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i2.608

Abstract

The advancement of information technology has encouraged the utilization of web-based Geographic Information Systems (GIS) to support the management and promotion of Micro, Small, and Medium Enterprises (MSMEs). This study aims to develop a web-based GIS capable of mapping MSME locations interactively, informatively, and accessibly. The research method adopts the waterfall software development model, including requirement analysis, system design, implementation, and testing. Data were collected through observation, interviews, and literature review. The system was developed using web technologies such as PHP, Laravel framework, and digital map integration with Leaflet and Mapbox. The results indicate that the system effectively presents spatial and non-spatial MSME information, equipped with search features, category filters, and navigation tools. System testing shows a high success rate and positive user responses. Therefore, this system can improve accessibility of MSME information and support location-based decision-making.
Prediksi Kebutuhan Rawat Inap Pasien Berdasarkan Data Hematologi Menggunakan Random Forest dengan Hyperparameter Optimization dan SHAP Explainability Milla Akbarany Baktiar Putri Putri; Hana Titania Sastrian; Muhammad Erlangga Kurniawan; Andri Fauzan Adziima; Kartika Maulida Hindrayani; Muhammad Zulhaj Aliansyah
Journal of Computer Science and Information Technology Vol. 2 No. 2 (2026): Journal of Computer Science and Information Technology, June 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i2.650

Abstract

Determining whether a patient requires inpatient or outpatient care is one of the most critical clinical decisions in healthcare facility management. Complete blood count (CBC) parameters provide routine and rapidly available physiological indicators, making them strong candidates for machine learning-based clinical decision support systems. This study aims to develop a prediction model for patient hospitalization needs using the Random Forest algorithm optimized through RandomizedSearchCV, combined with SHAP (SHapley Additive exPlanations) for model interpretability. The dataset consists of 4,412 patient records from an Indonesian private hospital available on Kaggle, containing nine hematological parameters and two demographic attributes. Feature engineering was applied by constructing three derived variables: leucocyte-thrombocyte ratio (leu_throm_ratio), hemoglobin-hematocrit ratio (hb_hct_ratio), and thrombocyte-age interaction (platelet_age). Two models were compared: a baseline Random Forest with manually configured hyperparameters and an optimized model using RandomizedSearchCV with Stratified 5-Fold Cross Validation. The baseline model achieved accuracy of .7633 and ROC AUC of .8097, while the optimized model yielded accuracy of .7644 and ROC AUC of .8131 on the test set. Feature importance analysis identified thrombocyte (.1655) and leu_throm_ratio (.1525) as the dominant predictors, together contributing over 31% of the model's predictive information. SHAP analysis confirmed that low thrombocyte values and high leucocyte values were the primary drivers of inpatient predictions, consistent with clinical indicators of infection and systemic inflammation. These findings demonstrate that Random Forest with clinically grounded feature engineering and SHAP interpretability provides a transparent and reasonably accurate approach for supporting hospital admission triage decisions.

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