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All Journal Jurnal Ilmu Komputer dan Informasi Charity : Jurnal Pengabdian Masyarakat Narra J Jurnal Nusantara Berbakti Jurnal Sistem Informasi
Ari Wibisono Ari Wibisono
Fakultas Ilmu Komputer, Universitas Indonesia
Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications Wibisono, Ari; Naufal Rahmadika, Rafif
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1539

Abstract

The Earthquake Early Warning System (EEWS) plays a pivotal role in mitigating structural damage and minimizing casualties by issuing alerts prior to the arrival of destructive seismic waves (S-waves), through the detection of the earlier and faster P-waves. The operational effectiveness of EEWS depends not only on the accuracy of its predictive algorithms but also on the efficiency of the underlying data storage and management infrastructure. This study presents a comparative evaluation of three data storage approaches i.e. MongoDB, MongoDB with sharding, and InfluxDB, as well as the MiniSEED (mseed) binary format, with a focus on their performance in managing real-time seismic prediction data. Benchmarking was conducted based on two key metrics: Input/Output Operations Per Second (IOPS) and data throughput. The results indicate that both MongoDB and InfluxDB offer strong performance in high-ingestion scenarios, with MongoDB demonstrating higher IOPS, while InfluxDB exhibits better scalability and consistency as data volume increases. Conversely, the mseed format achieves exceptionally high throughput due to its flat-file structure but lacks the responsiveness and query capabilities required for real-time analytics. These findings suggest that MongoDB and InfluxDB are well-suited for integration into scalable EEWS infrastructures, offering a balance between performance and flexibility. Future work will extend this evaluation to larger-scale datasets and alternative architectures such as data lake systems to improve disaster response readiness.
Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.
Pembangunan Screenhouse Sebagai Upaya Peningkatan Produktivitas Tanaman Di Dusun Ngadilegi Utara, Kecamatan Pandaan, Kabupaten Pasuruan Ari Wibisono; Muhammad Farhan Firmansyah; Purnomo Edi Sasongko
Jurnal Nusantara Berbakti Vol. 2 No. 1 (2024): Januari : Jurnal Nusantara Berbakti
Publisher : Universitas Kristen Indonesia Toraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jnb.v2i1.321

Abstract

Ngadilegi Utara Hamlet is one of the hamlets of Plintahan Village, Pandaan District, which has great potential in the agricultural sector. However, global climate change has caused problems that Ngadilegi Utara farmers, especially the Madulegi Farmer Group, must face in cultivating crops. The impact of global climate change can trigger the emergence of plant pest organisms (OPT) which can affect the achievement of quality to meet market demand (consumers). Meanwhile, farmers in Ngadilegi Utara Hamlet still carry out crop cultivation activities on open land which is very potential for pest and disease attacks, and can be disrupted by environmental stress. Cultivation innovation using a screenhouse is one solution to overcome the problem in order to increase plant productivity. The screenhouse was built with a size of 6 x 8 meters using a hollow and galvanized C frame. The roof uses UV plastic with a thickness of 200 microns, the walls use insect net with a density of 50 mesh, and the base is covered using tarpaulin. The construction of the screenhouse in Ngadilegi Utara Hamlet is considered to reduce the attack of plant pest organisms (OPT) and environmental stress factors, so that farmer groups in Ngadilegi Utara Hamlet can increase crop productivity well.
Co-Authors Abdul Haris Abdul Haris Afifi, Rahmi Ahmad Arinaldi Andreas Febrian Anggraini Iriani Arry Yanuar Baruna, Bagus DI. Benny Hardjono Chahyati, Dina David Bayu Ananda David Bayu Ananda David Bayu Ananda Dwi Marhaendro Jati Purnomo Dwi Purnomo Dwi Teguh Priyantini Falerisya, Maharani Grafika Jati Guarddin, Gladhi Hanif Arief Wisesa hanif wisesa hanif wisesa hanif wisesa Heru Suhartanto Krisnadhi, Adila Machmud R Alhamidi Machmud Roby Alhamidi Machmud Roby Alhamidi Muhammad Farhan Firmansyah Muhammad Iqbal Tawakal Muhammad Iqbal Tawakal muhammad soleh muhammad soleh muhammad soleh Nandipinto, Finny Nareswari, Dewi Naufal Rahmadika, Rafif Novian Habibie Petrus Mursanto Petrus Mursanto Pratama, Dhita P. Purnomo Edi Sasongko Puteri Khatya Fahira Qurrotin A’yunina Qurrotin A’yunina Qurrotin A’yunina Rachmad Akbar Rachmad Akbar Rachmad Akbar Rahmawati, Irda N. Rumende, Cleopas M. Salsabila, Raissa Satria Bagus Wicaksono Septiyanti, Wita Sini, Ivan R. Sumarsih C Purbarani Sumarsih C Purbarani Sumarsih C Purbarani Telly Kamelia Wicaksono, Ceva Wisnu Jatmiko Yohanes Gultom Yunus, Reyhan E. Zulkarnaien, Benny