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All Journal INTI Nusa Mandiri PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL)
Agustina Rachmawardani
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Computer Science & IT Electrical & Electronics Engineering Energy Physics Other

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RANCANG BANGUN LANDSLIDE EARLY WARNING SYSTEM (LEWS) BERBASIS WIRELESS SENSOR NETWORK MENGGUNAKAN ESP32 DAN LORAWAN Bayu Satrio; Agustina Rachmawardani; Agustya Adi Martha; Dwi Indra Prasetyo
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA10

Abstract

Tanah longsor merupakan bencana geologis yang berdampak signifikan terhadap keselamatan manusia dan infrastruktur, sehingga diperlukan sistem peringatan dini yang andal. Penelitian ini mengembangkan prototipe Landslide Early Warning System (LEWS) berbasis Wireless Sensor Network (WSN) untuk memantau parameter tanah secara real-time menggunakan tiga node sensor. Masing-masing node dilengkapi dengan sensor akselerometer MPU9250 dan sensor kelembapan tanah kapasitif. Komunikasi antar node dan base station menggunakan protokol LoRaWAN, sementara pengiriman data ke server dilakukan melalui WiFi dengan protokol MQTT. Data ditampilkan melalui antarmuka web berbasis Laravel dan dikirim sebagai notifikasi ke Telegram. Pengujian dilakukan melalui dua simulasi. Pertama, uji komunikasi LoRa menunjukkan jangkauan hingga 100 meter dengan nilai RSSI antara −46 dBm hingga −74 dBm. Kedua, simulasi deteksi longsor menunjukkan lonjakan Peak Ground Acceleration (PGA) mencapai 0.4–0.5 g secara serempak di ketiga node, sebelum kembali ke nilai normal < 0.1 g. Pengamatan kelembapan tanah menunjukkan kestabilan data antara 45%–55. Hasil membuktikan bahwa sistem WSN-LEWS mampu melakukan pemantauan dan transmisi data secara andal, serta memberikan deteksi dini terhadap indikasi longsor melalui perubahan percepatan dan kelembapan tanah.
ANALISIS SENTIMEN INFORMASI GEMPA BUMI BMKG PADA APLIKASI X MENGGUNAKAN SVM DAN RANDOM FOREST Arfany Dhimas Muftareza; Reza Okta Pratama; I Dewa Gede Loka Maheswara; Giarno; Agustina Rachmawardani
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8550

Abstract

This study analyzes public sentiment towards the dissemination of earthquake information by BMKG through the X application with modeling using the Support Vector Machine (SVM) and Random Forest (RF) algorithms. Data were collected from 2,711 tweets mentioning @infoBMKG using tweet-harvest, then processed through the stages of case folding, cleaning, tokenization, slang normalization, stopword removal, and stemming. Automatic sentiment labeling was performed using a hybrid approach of VADER and InSet Lexicon. Feature representation used TF-IDF (Term Frequency–Inverse Document Frequency) with 1,000 features and data distribution 80% train and 20% validation. The results show that RF achieved an accuracy of 81.92% and SVM 81.17%, with almost identical Macro F1 (RF: 0.7445; SVM: 0.7443). Neutral sentiment indicates informative tweets without emotional content (61.75%), negative sentiment represents the public's emotional response that is not solely intended as a form of negative assessment of BMKG (24.71%), and positive sentiment is an expression of appreciation, gratitude, and hope for the delivery of information (13.54%). SVM excels in cross-validation stability (std ±0.0574) and negative sentiment recall (0.71), making it more suitable for real-time disaster communication monitoring.
Co-Authors Agustya Adi Martha Arfany Dhimas Muftareza Arno, Giarno Bayu satrio Dwi Indra Prasetyo I Dewa Gede Loka Maheswara Reza Okta Pratama