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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIV : International Journal on Informatics Visualization Journal of Information Systems and Informatics Indonesian Journal for Social Responsibility Abdi Psikonomi Almufi Jurnal Pengabdian kepada Masyarakat Journal of Informatics and Communication Technology (JICT) Teknologika Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Journal of Informatics and Communication Technology (JICT)
Vivi Monita
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Comparative Analysis of Daily and Weekly Heavy Rain Prediction Using LSTM and Cloud Data Monita, Vivi; Sevirda Raniprima; Nanang Cahyadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30374

Abstract

Indonesia's distinct geographic and climatic features make forecasting the weather there tricky. Due to its location at the equator and between two enormous oceans, the nation endures erratic weather patterns. Despite technical developments, the Meteorology, Climatology, and Geophysics Agency (BMKG) require assistance with precise forecasting. This research seeks to increase prediction accuracy using the Long Short-Term Memory (LSTM) algorithm, a deep learning technique appropriate for time series data processing. The research focuses on cloud data sets to improve the prediction of heavy rain. The potential of LSTM in weather forecasting has been demonstrated in earlier research, focusing on identifying rain at particular intervals. This research compares daily and weekly heavy rain prediction models using Python.  Results reveal that the weekly model outperforms the daily model, achieving 85% accuracy compared to 80%. These findings highlight the effectiveness of LSTM in addressing the limitations of existing methods, offering a foundation for more reliable weather forecasting tailored to Indonesia’s conditions.
Smart Monitoring Anak Usia Dini: Pemantauan Kembang Tumbuh dan Status Gizi Berbasis Aplikasi Android di TKIT Al-Munar Fu’adah, R Yunendah Nur; Monita, Vivi; Rahmaniar, Thalita Dewi
Almufi Jurnal Pengabdian Kepada Masyarakat Vol 5 No 2: Desember (2025)
Publisher : Yayasan Almubarak Fil Ilmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63821/ajpkm.v5i2.583

Abstract

Permasalahan gizi dan keterlambatan kembang tumbuh anak usia dini masih menjadi tantangan dalam upaya peningkatan kualitas kesehatan anak di Indonesia. Salah satu kendala yang dihadapi adalah belum optimalnya pemantauan pertumbuhan anak secara rutin serta keterlibatan orang tua dalam pencatatan perkembangan anak. Kegiatan pengabdian kepada masyarakat ini bertujuan mengimplementasikan konsep smart monitoring anak usia dini melalui pemanfaatan aplikasi Android untuk pemantauan kembang tumbuh dan status gizi anak di TKIT Al-Munar, Kota Bandung. Metode pelaksanaan kegiatan meliputi pengumpulan data awal melalui observasi dan wawancara, studi literatur dan penyusunan materi, sosialisasi dan pelatihan penggunaan aplikasi kepada guru dan orang tua, implementasi aplikasi dalam pemantauan rutin, serta evaluasi kegiatan melalui kuesioner dan diskusi. Aplikasi Android yang digunakan dilengkapi dengan fitur pencatatan data pertumbuhan dan perhitungan status gizi berdasarkan indikator antropometri sesuai standar World Health Organization (WHO), sehingga memungkinkan pemantauan pertumbuhan anak secara mandiri dan berkelanjutan. Peran mitra difokuskan pada pendampingan orang tua dan integrasi penggunaan aplikasi dalam aktivitas pemantauan di lingkungan sekolah. Hasil kegiatan pengabdian kepada masyarakat menunjukkan peningkatan pemahaman dan partisipasi guru serta orang tua dalam pemantauan kembang tumbuh dan status gizi anak. Implementasi aplikasi ini mempermudah pencatatan dan pelaporan data pertumbuhan anak, serta berpotensi mendukung upaya pencegahan dini masalah gizi pada anak usia dini.
Comparative Quality of Services and Resource Utilization Analysis of Free5GC and Open5GS in Resource-Constrained Private 5G Networks Naufal Hanan Lutfianto; Budi Prasetya; Vivi Monita
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1513

Abstract

This research compares the performance of two widely used open-source 5G core (5GC) platforms, Free5GC and Open5GS, in a resource-constrained private network environment. While previous studies have mainly focused on feature comparison or large-scale deployments, performance under limited computational resources has received less attention, particularly for small-scale enterprise use cases. In this work, both platforms are integrated with UERANSIM to emulate end-to-end 5G communication and evaluated under dynamic user equipment (UE) scaling. Each 5GC instance and simulator component is allocated one CPU core and 2 GB of memory. Performance is assessed using key Quality of Service (QoS) metrics, including throughput, latency, packet loss, and resource utilization (CPU and memory), under both TCP and UDP traffic. The results show that Open5GS consistently provides better performance than Free5GC. It achieves up to 10.58 Mbps throughput compared to 9.22 Mbps and maintains lower latency around 0.72–0.73 ms, while Free5GC reaches up to 1.20 ms as the number of UEs increases. In addition, Free5GC reaches high CPU utilization earlier under increasing load. These differences are mainly related to its microservice-based architecture, which introduces additional processing overhead.
Co-Authors Anggiratih, Endang Budi Prasetya Fu’adah, R Yunendah Nur Hanan Lutfianto, Naufal Indrarini Dyah Irawati Musiafa, Zayid Nanang Cahyadi Naufal Hanan Lutfianto Niswah, Nailah Khairun Putri Rahmawati Rahmadi Rahmadi Rahmah, Githa Desyana Nur Rahmaniar, Thalita Dewi Reza Ramadani Firman Rizka Rachmawati ROHMAT TULLOH RR. Erna Sadiarti Budiningtyas Seshariana Rahma Melati Sevierda Raniprima Sevirda Raniprima Sherfina Salsabila Wendanto, Wisnu Wisnu Wendanto Yudiansyah Yudiansyah