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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pendidikan Vokasional Teknik Elektronika (JVoTE) Paulus Civil Engineering Journal
Ratri Andinisari
Institut Teknologi Nasional Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Transportation Other

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Identifikasi Kawasan Rawan Bencana Gerakan Tanah Dengan Menggunakan GIS-MCDA dan AHP di Kecamatan Tirtoyudo, Kabupaten Malang, Jawa Timur Ratri Andinisari; Krishna Himawan Subiyanto; Ardiyanto Maksimilianus Gai; Kinta Ramayanti; Rahmadina Fitria Ristanti; Estri Pamungkasih; Diyan Eko Wahyudie; Iftita Yustitia Arini; Dewa Oka Prabawa
Paulus Civil Engineering Journal Vol. 8 No. 1 (2026): Paulus Civil Engineering Journal, Maret 2026
Publisher : Universitas Kristen Indonesia Paulus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52722/cz23jr53

Abstract

Penelitian ini bertujuan untuk memetakan kerawanan bencana gerakan tanah di Kecamatan Tirtoyudo, Kabupaten Malang, dengan menggunakan pendekatan GIS berbasis MCDA-AHP. Parameter yang digunakan meliputi kelerengan, curah hujan, geologi, jenis tanah, dan tutupan lahan. Setiap parameter dianalisis secara spasial dan diklasifikasi berdasarkan tingkat pengaruhnya terhadap potensi gerakan tanah, kemudian diintegrasikan melalui metode weighted overlay untuk menghasilkan peta zonasi kerawanan. Hasil penelitian menunjukkan bahwa wilayah Kecamatan Tirtoyudo didominasi oleh zona kerawanan sedang dengan luasan sekitar 908,06 ha, zona kerawanan rendah seluas 89,06 ha, dan zona kerawanan tinggi seluas 6,68 ha. Distribusi kerawanan tersebut menunjukkan bahwa sebagian besar wilayah penelitian berada pada tingkat kerentanan menengah. Di sisi lain, zona kerawanan tinggi bencana gerakan tanah di wilayah ini terkonsentrasi pada lereng curam, area dengan jenis batuan yang lemah, serta tutupan lahan yang rendah. Hasil penelitian ini menunjukkan bahwa pendekatan MCDA berbasis GIS efektif dalam mengidentifikasi pola kerawanan bencana gerakan tanah secara spasial dan dapat digunakan sebagai dasar perencanaan mitigasi bencana serta penataan ruang wilayah yang adaptif di wilayah Kecamatan Tirtoyudo.
Pemilihan Dosen Pembimbing Berdasarkan Judul Skripsi Mahasiswa menggunakan Metode Cosine Similarity Ratri Andinisari; Fathur Riski; Karina Auliasari
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The process of selecting a thesis advisor that aligns the thesis topic with the advisor's area of expertise will greatly assist in the thesis preparation process for students. However, in reality, the process of determining the thesis advisor is often not based on the advisor's area of expertise. The process of determining the thesis advisor is often done manually and inefficiently, which can lead to a mismatch between the advisor's area of expertise and the student's thesis topic. From this problem, this research developed a recommendation model for selecting thesis supervisors using the Cosine Similarity method, utilizing thesis title data and the list of lecturers' areas of expertise. The process begins with preprocessing the text data of thesis titles, then calculating word weights using TF-IDF and measuring similarity to finally match the students' thesis titles with the lecturers' areas of expertise, which are calculated using the Cosine Similarity algorithm. From the results of testing the process of matching research topics from student thesis titles with the expertise and research of lecturers, it was produced accurately, as indicated by a precision value of 81% and a recall of 90%. This result also shows that the relevance level of the recommendations is very high. The F1 score obtained, which is 85%, indicates that the modeling has a good balance between precision and sensitivity (recall).
Two-Stage Adaptive Normalization Based on Exponential Moving Average for DC Offset and Variance Non-Stationarity Compensation in Real-Time Volcanic Seismic Signal Preconditioning Radimas Putra Muhammad Davi Labib; Ratri Andinisari; Mochammad Rifki Ulil Albaab; Ferty Lanisa Putri; Fernanda Sucitra Murti; Agam Nizar Dwi Nur Fahmi
JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Vol. 8 No. 2 (2025): Vol 8 No 2 (2025): JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Volu
Publisher : PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA FAKULTAS TEKNIK UNIVERSITAS NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jvote.v8i2.71692

Abstract

Volcanic seismic signals output by digitizers as raw counts carry the accumulated imperfections of the entire acquisition chain—specifically, significant DC offset and large-scale variability—thereby violating the stationarity assumption underpinning downstream estimation blocks. This study proposes a two-stage adaptive normalization architecture based on the Exponential Moving Average (EMA) to serve as a statistical pre-conditioning block prior to the Kalman Filter. The first stage removes the DC offset using a recursive mean estimator—analytically equivalent to a first-order high-pass filter—while the second stage performs Z-Score standardization based on a running estimate of the standard deviation. Testing was conducted on a 386.48-second vertical-channel recording from the Leker (LKR) station at Mount Semeru (sampled at 100 Hz; 38,649 samples), characterized by a DC offset of −7,292.4 counts and a variance dynamic ratio of 10.42. With an adaptation constant of 0.005, the effective cutoff frequency of 0.0796 Hz lies approximately eighteen times below the 1.42 Hz fundamental frequency of the harmonic tremor, resulting in a passband attenuation of only −0.035 dB. Results demonstrate that the estimator locks onto the bias with a discrepancy of 7.72 counts (0.070% of the signal's standard deviation), reduces the dynamic ratio to 1.52, and yields an output where 99.989% of samples fall within the ±3 range. The Normalized Innovation Squared test confirms that the static covariance parameters of the Kalman Filter remain consistent with the normalized input, yielding a ratio of 1.17 compared to 17.10 for the raw input—an improvement factor of 14.6. The algorithm requires 8 bytes of state memory and 0.0104% processor utilization on an ARM Cortex-M4F at 48 MHz, whereas static normalization of the same recording requires 151.0 kB and cannot be implemented on that platform.
Co-Authors Agam Nizar Dwi Nur Fahmi Ardiyanto Maksimilianus Gai Dewa Oka Prabawa Diyan Eko Wahyudie Estri Pamungkasih Fathur Riski Fernanda Sucitra Murti Ferty Lanisa Putri Iftita Yustitia Arini Karina Auliasari Kinta Ramayanti Krishna Himawan Subiyanto Mochammad Rifki Ulil Albaab Radimas Putra Muhammad Davi Labib Rahmadina Fitria Ristanti