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All Journal Journal of Applied Data Sciences Journal of Telecommunication, Electronics and Control Engineering (JTECE)
Ni'amah, Khoirun
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisa Kuantitatif Kualitas Jaringan 5G Non-Stand Alone berdasarkan Pengukuran Empiris di Pacitan Darmanto, Darmanto; Larasati, Solichah; Ni'amah, Khoirun
Journal of Telecommunication Electronics and Control Engineering (JTECE) Vol 7 No 2 (2025): Journal of Telecommunication, Electronics, and Control Engineering (JTECE)
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jtece.v7i2.1931

Abstract

Penelitian melakukan kajian terkait uji layak operasi jaringan 5G pada frekuensi 2,3 GHz dari salah satu operator seluler di Indonesia. Evaluasi performa jaringan 5G Non-Stand Alone (NSA) diimplementasikan pada dua lokasi di cluster 1 wilayah Pacitan, Jawa Timur yaitu site ZMDU_0406 dan ZMDU_0407. Metode yang digunakan meliputi perancangan skenario pengukuran, pengumpulan data, dan analisa data lapangan berdasarkan pada parameter throughput, Secondary Synchronization Reference Signal Received Power (SS-RSRP), dan Synchronization Signal-to-Noise and Interference Ratio (SS-SINR). Pengujian dilakukan menggunakan dua skenario traffic yaitu FTP Server dan UDP Server pada perangkat IDTrium—ATEL 5G CPE (SDX62) dan aplikasi iperf3. Hasil simulasi berdasarkan cakupan menunjukan bahwa 99% area memiliki kualitas sinyal yang baik yang ditunjukan dengan nilai SS-RSRP > 80 dBm dan SS-SINR >20 dB pada kedua site. Hasil pengukuran FTP Server menunjukan nilai rata-rata throughput sebesar 82 Mbps untuk site ZMDU_0406 dan 107 Mbps untuk site ZMDU_0407. Sementara itu, pengukuran UDP, site ZMDU_0406 memiliki rata-rata throughput 123 Mbps dan site ZMDU_0407 memiliki nilai rata-rata throughput 157 Mbps. Berdasarkan penelitian berikut dapat dikatakan bahwa infrastuktur 5G Non-Stand Alone yang diterapkan di wilayah Cluster 1 Pacitan memiliki performa yang baik untuk aplikasi real time.
Spatial Analysis of Ensemble Learning Models for Agricultural Drought Early Warning Sudianto, Sudianto; Ni'amah, Khoirun; Dewi, Atika Ratna; Ramadhan, Afan; Aprilia, Jeti; Tiyaswening, Arsita Wiwit; Anataya, Syalaisha Nisrina
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1108

Abstract

Drought poses a serious threat to rice production and local food security, triggered by climate anomalies such as El Niño. This study aims to evaluate and compare the performance of Ensemble Learning Models in classifying drought levels and analyze its correlation with periods of climate anomalies. This study uses Landsat 9 image data in the simulation period from June 2024 to July 2025, which is processed with HSV-based pan-sharpening and spectral index extraction (NDVI, NDWI, NDDI, EVI, LST). The modeling process applied undersampling to address class imbalance and hyperparameter tuning optimization using Optuna. The models compared included Random Forest, LightGBM, AdaBoost, XGBoost, and Gradient Boosting. The results showed that Gradient Boosting excelled with a train accuracy of 96,85% in original dataset with split dataset 70:30, whereas rise to 98.98% after tuning. Spatial validation was conducted in other rice field plots, however its steadfastly on research area with same treatment. The classification map shows the dominance of the moderate category, which temporally coincides with the period of rainfall decline associated with El Niño, although a direct causal relationship requires further investigation. These findings confirm that remote sensing combined with machine learning is effective for drought monitoring, with the caveat that the application of undersampling and limited spatial validation that is, confined solely to the research area; needs to be considered in the interpretation of results.
Co-Authors Anataya, Syalaisha Nisrina Anggun Fitrian Isnawati Aprilia, Jeti Atika Ratna Dewi Darmanto Darmanto Harahap, Fatur Rahman Ramadhan, Afan Solichah Larasati Sudianto, Sudianto Tiyaswening, Arsita Wiwit