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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING The Indonesian Journal of Computer Science
Karimatun Nisa’
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : the indonesian journal of computer science

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Analisa Performansi Komunikasi Lora (Long Range) pada Sistem Monitoring Buoy di Laut Sa'adah, Nihayatus; Aries Pratiarso; Faridatun Nadziroh; Nailul Muna; Karimatun Nisa’; I Gede Puja Astawa; Tri Budi Santoso; Sultan Syahputra Yulianto
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4462

Abstract

LoRa (Long Range) is a leading technology in Low Power Wide Area Networks (LPWAN), ideal for Internet of Things (IoT) applications. Designed for long-range communication with low power consumption, LoRa is used in monitoring navigation buoys, critical aids in marine waters. An IoT-based monitoring system is essential for maintaining buoy functionality. LoRaWAN, with a range of 15 kilometers under line of sight (LoS) conditions, is employed for IoT connectivity in this system. In this study, 915 MHz LoRa communication was used in buoy monitoring, with performance evaluated based on signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Measurements showed an average RSSI greater than -120 dB and an average SNR greater than -20 dB, indicating LoRa's suitability as a communication network for buoy monitoring systems.
Implementasi Algoritma EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite untuk Sistem Deteksi Gulma Nailul Muna; Norma Ningsih; Nanang Syahroni; Abd. Malik Syamlan; Vina Larasati; Karimatun Nisa’
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3723

Abstract

Gulma merupakan tumbuhan yang tumbuh disekitar tanaman dan dapat merugikan tanaman yang dibudidayakan. Pengendalian gulma menjadi faktor penting yang dapat mempengaruhi produktivitas tanaman. Pengendalian gulma dapat ditanggulangi dengan melakukan penyemprotan pestisida pada gulma. Cakupan penyemprotan yang tepat sasaran dapat dilakukan untuk mencegah timbulnya masalah limbah. Sistem pertanian cerdas sangat dibutuhkan untuk mengatasi permasalahan tersebut, seperti deteksi gulma yang memanfaatkan teknik deep learning. Pada penelitian ini membangun sistem deteksi gulma yamg mengimplementasikan EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite. Dataset yang digunakan berjumlah 941 citra gulma yang kemudian dilakukan pelabelan untuk data latih dan data uji. Sistem menunjukkan kinerja yang baik untuk mendeteksi gulma dengan accuracy berturut-turut dari EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite mencapai 95,69% dan 99,138%. Hasil tersebut menunjukkan EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite dapat mendukung dalam pengendalian gulma.
Co-Authors Abd. Malik Syamlan Ahmad Baihaqi Adi Putro Aries Pratiarso Faridatun Nadziroh I Gede Puja Astawa Nailul Muna Nanang Syahroni NIHAYATUS SAADAH Ningsih, Norma Sa'adah, Nihayatus Sultan Syahputra Yulianto Tri Budi Santoso Vina Larasati