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NInuk Wiliani
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Journal of Informatics and Advanced Computing
Published by Universitas Pancasila
ISSN : -     EISSN : 27220346     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Informatics and Advanced Computing is a leading scientific publication platform that presents the latest and innovative research in the field of informatics and computing. This journal highlights the latest developments, practical applications, and significant impacts of computing technology across various disciplines. We invite researchers, academics, and practitioners to share their findings that contribute to the advancement of science and technology. The Journal of Informatics and Advanced Computing is committed to publishing research that is relevant to real-world challenges. This journal presents innovative computational-based solutions for problems faced by society, industry, and government. We strive to be the primary reference for practitioners who want to apply the latest technology in their work.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing" : 4 Documents clear
IMPLEMENTASI COMPUTER VISION UNTUK DETEKSI SAMPAH LAUT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Hanifa, Faiqa; Maspiyanti, Febri
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/sch9tm40

Abstract

Marine debris has become a serious and growing threat to marine ecosystems, human health, and maritime activities and economies. Various manual monitoring efforts that has been carried out so far are often limited in terms of spatial coverage, efficiency, and resource effectiveness. With the rapid advancement of remote sensing technology and the integration of artificial intelligence, marine debris monitoring can now be automated through computer vision approaches. This study develops a computer vision-based system for marine debris detection using Sentinel-2 satellite imagery and Convolutional Neural Network (CNN) to adhere to the blue economy framework. The proposed approach applies semantic segmentation using the U-Net architecture. The primary dataset used in this study is provides multispectral imagery with a spatial resolution of 10 meters and annotations for four main classes: marine debris, organic material, seawater, and other objects. The imagery enhanced using spectral indices such as the Floating Debris Index (FDI) and the Normalized Difference Vegetation Index (NDVI) helps distinguish spectral characteristics between debris and non-debris classes more clearly. Model performance is evaluated using metrics including accuracy, precision, recall, Intersection-over-Union (IoU), and F1-Score. The best model achieved scores of 0.95, 0.81, 0.67, and 0.77 for each respective metric, demonstrating U-Net's effectiveness in detecting marine debris. The final system is deployed through an interactive Streamlit interface, allowing users to upload satellite imagery, view segmentation results, visualize spectral indices, and preview bounding boxes that highlight detected debris locations. This approach is expected to serve as an effective and adaptive tool to support sustainable marine environmental policies and decision-making.
KAJIAN KEAMANAN CCTV BERBASIS INTERNET OF THINGS "MENGIDENTIFIKASI MASALAH DAN SOLUSI PERLINDUNGAN" Tanuwidjaya, Michael Keyva Valiant; Valiant, Michael; Wiliani, Ninuk
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/gtrsgn29

Abstract

Perkembangan teknologi khususnya pada Internet of Things (IoT) yang menjadi awal adopsi perangkat seperti CCTV IoT dalam sistem keamanan rumah. Tetapi perangkat tersebut rentan terhadap ancaman siber seperti Man-in-the-Middle, Denial of Serivices, dan Brute force. Dari lemahnya autentikasi, enkripsi data yang kurang baik, serta penggunaan kredensial default. Penelitian bertujuan mengidentifikasi potensi kerentanan dan mengeksploitasi nya dengan metode serangan pada perangkat CCTV IoT dan memberikan solusi pendekatan ekperimental. Pengujian ini dilakukan pada dua perangkat CCTV IoT yang dipasang pada ruang lingkup rumah, dengan menggunakan beberapa tools open-sources seperti Nmap, Bettercap, dan Metasploit pada sistem operasi ubuntu linux. Hasil dari pengujian ini menunjukkan perangkat memiliki kerentanan keamanan pada konfigurasi jaringan dan autentikasi, serta rentan terkena serangan DoS dan Sniffing data. Solusi yang bisa diberikan, penggunaan firewall, sistem deteksi intrusi, dan pembaruan firmware, serta penguatan kredensial.
Analisis Efektivitas Sistem Monitoring Berbasis Telegram Terhadap Peningkatan Keandalan Operasi Control Rod Drive Mechanism (CRDM) iman sugiharto; Sugiharto, Iman; Suryono, Tulis Jojok Suryono; Suryanto , Suryanto; Watmah, Sri Watmah
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/c8kh8v68

Abstract

Analisis Efektivitas Sistem Monitoring Berbasis Telegram terhadap Peningkatan Keandalan Operasi Control Rod Drive Mechanism (CRDM). Keselamatan operasi reaktor nuklir sangat tergantung pada keandalan Control Rod Drive Mechanism (CRDM) dalam mengatur reaktivitas inti. Sistem pemantauan konvensional sering lambat dalam mendeteksi gangguan, sehingga meningkatkan risiko kerusakan dan memperpanjang waktu pemulihan. Penelitian ini menganalisis efektivitas sistem monitoring cerdas berbasis Internet of Things (IoT), menggunakan modul ESP8266 yang terintegrasi dengan Telegram Bot, untuk memberikan notifikasi kondisi operasi secara real-time kepada operator. Evaluasi dilakukan melalui simulasi skenario normal dan darurat (scram), dengan fokus pada kecepatan deteksi gangguan, respons sistem, dan efisiensi pemulihan operasi CRDM. Hasil analisis menunjukkan bahwa sistem monitoring berbasis Telegram mampu menurunkan Mean Time To Repair (MTTR) hingga 57% dan memastikan notifikasi diterima operator dalam hitungan detik. Temuan ini menegaskan bahwa penerapan IoT dan Telegram Bot meningkatkan efektivitas pemantauan, memperkuat keandalan operasi CRDM, dan menjadi langkah strategis dalam manajemen risiko di lingkungan berisiko tinggi seperti reaktor nuklir.
Sistem Pendukung Keputusan Penentuan “Employee of The Month” di RSUP Rivai Abdullah Menggunakan Metode SAW dan WP Ananda, Marcel Antoneo; Kiagus Muhammad Alamsyah; Ibnu Choldun; Dien Novita
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/tr7yz415

Abstract

Penilaian kinerja pegawai secara objektif dan terstruktur merupakan hal penting dalam meningkatkan kualitas sumber daya manusia, terutama di lingkungan instansi pelayanan publik seperti RSUP Rivai Abdullah. Dalam proses pemilihan “Employee of the Month”, dibutuhkan sistem yang mampu mengakomodasi berbagai kriteria penilaian secara adil dan efisien. Penelitian ini mengembangkan sistem pendukung keputusan berbasis web dengan menerapkan metode Simple Additive Weighting (SAW) dan Weighted Product (WP). Sistem dirancang untuk mengevaluasi kinerja pegawai berdasarkan enam kriteria, yaitu absensi, jumlah pekerjaan yang diselesaikan, jumlah pelanggaran, kerjasama tim, tanggung jawab, dan masa kerja. Hasil penelitian menunjukkan bahwa sistem yang dibangun dapat menghasilkan peringkat pegawai secara tepat, transparan, dan membantu pihak manajemen dalam mengambil keputusan secara lebih efektif. Penggunaan metode SAW dan WP secara bersamaan memberikan akurasi yang lebih baik dalam proses penilaian.

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