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All Journal Jurnal Teknologi Agro-Industri Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal)
Suhendra
Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Chemistry Education Physics

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Integrasi Data Geolistrik (VES) dengan Verifikasi Data Litologi Cutting Bor untuk Menganalisis Lapisan Akuifer Lulu Setiawati; Agung Wijaya; Valennita; Suhendra; Benny Bayu Prabowo; Rudianto Girsang; Didi Ardiansyah
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 7 No. 2 (2026): May
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v7i2.1733

Abstract

The increasing demand for clean water in Selebar Subdistrict, Sumur Dewa Village, Bengkulu City has prompted research to identify and map aquifer zones more accurately. This study aims to analyze the presence and depth of aquifers using the Vertical Electrical Sounding (VES) geophysical method, which is then verified using borehole lithological data. VES data collection was conducted at three sounding points (n=3) using a Schlumberger configuration with an AB/2 electrode spacing of 250 m. The acquired geophysical data were processed using IPI2win software with an error value (RMS error) >5%, resulting in a one-dimensional (1D) subsurface model that was further developed into a two-dimensional (2D) cross-section. The results indicate that the main aquifer zone is located at a depth of 51–75 meters, characterized by low resistivity values (<50 Ωm), indicating a water-saturated layer identified as the groundwater zone. This is consistent with drilling data, which show indications of water loss at the same depth interval, thereby reinforcing the assumption of groundwater presence in that aquifer layer. Based on these results, it can be concluded that the integration of the VES geoelectric method, verified with borehole cross-section data, is effective in characterizing subsurface structures and mapping aquifer zones more accurately.
Identifikasi Potensi Akuifer Menggunakan Metode Geolistrik Resistivitas Konfigurasi Wenner di Kelurahan Betungan, Kota Bengkulu Felysia Laurend; Suhendra; Efmadani
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 7 No. 3 (2026): August (Inpres)
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v7i3.2082

Abstract

Groundwater is an important water resource for meeting community water demands. Information regarding subsurface characteristics and aquifer occurrence is essential for assessing groundwater potential in a given area. This study aims to identify subsurface layer characteristics and determine zones with potential as aquifers in Raflesia Asri Housing Estate, Betungan Village, Selebar District, Bengkulu City. The research employed the two-dimensional (2D) electrical resistivity method using the Wenner configuration. The measured data were processed using Res2DINV software to obtain a subsurface resistivity distribution model. The inversion results indicate resistivity values ranging from 1.52 Ωm to 317 Ωm, with an investigation depth of approximately 43 m. Low-resistivity zones (1.52–15.0 Ωm) are interpreted as clay and silt layers, whereas moderate-resistivity zones (15.0–69.0 Ωm) are interpreted as clayey sand to water-saturated sand, which are considered potential aquifer layers. High-resistivity zones (>69.0–317 Ωm) are interpreted as gravelly sand and weathered rock. The dominance of moderate-resistivity zones in the inverted resistivity sections indicates the presence of potential aquifers that may serve as groundwater resources in the study area.
Evaluasi Kelayakan Penggunaan Model Machine Learning pada Klasifikasi Perilaku Ayam di Bawah Kondisi Suhu dan Kelembapan yang Bervariasi Firmansyah Firmansyah; Suhendra; Arina Fatharani; Yusuf Irfan
Jurnal Teknologi Agro-Industri Vol. 13 No. 1 (2026): Jurnal Teknologi Agro-Industri
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jtai.v13i1.256

Abstract

Penelitian ini mengkaji penerapan model pembelajaran mesin untuk mengklasifikasikan perilaku ayam pada kondisi suhu dan kelembapan yang bervariasi, dengan tujuan mengoptimalkan kesejahteraan dan produktivitas unggas melalui pemantauan otomatis. Analisis difokuskan pada perilaku utama seperti Beristirahat, Makan/Minum, dan Aktivitas Fisik, serta meneliti bagaimana fluktuasi lingkungan memengaruhi respons fisiologis ayam, khususnya dalam termoregulasi. Selama periode pengamatan selama 21 hari, data dikumpulkan dari enam ayam menggunakan sensor suhu dan kelembapan, disertai dengan rekaman video. Sebanyak lima model pembelajaran mesin diuji, yaitu Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), dan metode ensemble, untuk mengidentifikasi model yang paling efektif di antara lima model yang digunakan. Model Random Forest menunjukkan kinerja terbaik dengan akurasi sebesar 98,65%, membuktikan kemampuannya dalam membedakan berbagai aktivitas dengan efektif. Selain itu, temuan penelitian ini menekankan pentingnya mengintegrasikan data lingkungan secara real-time dan meningkatkan teknik ekstraksi fitur untuk meningkatkan keandalan klasifikasi. Wawasan dari penelitian ini berpotensi memberikan kontribusi terhadap pengembangan sistem pemantauan cerdas adaptif, meskipun masih memerlukan validasi lebih lanjut dengan jumlah sampel yang lebih besar, sekaligus menjadi landasan awal yang berpotensi mendukung kemajuan di masa depan dalam manajemen unggas komersial dengan memungkinkan intervensi kesejahteraan yang tepat waktu dan mendukung operasi yang efisien serta berorientasi pada kesejahteraan.
Co-Authors Agung Wijaya Arina Fatharani Benny Bayu Prabowo Didi Ardiansyah Efmadani Felysia Laurend Firmansyah Firmansyah Lulu Setiawati Rudianto Girsang Valennita Yusuf Irfan