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All Journal Jurnal Teknik BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of the Civil Engineering Forum J-Sil (Jurnal Teknik Sipil dan Lingkungan)
Olii, Rizky Selly Nazarina
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Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning Olii, Muhammad Ramdhan; Nento, Sartan; Doda, Nurhayati; Olii, Rizky Selly Nazarina; Djafar, Haris; Pakaya, Ririn
Journal of the Civil Engineering Forum Vol. 11 No. 2 (May 2025)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.19581

Abstract

Soil erosion presents substantial environmental and economic challenges, especially in areas prone to land degradation. This study assesses the use of Machine Learning (ML) methods—Support Vector Machines (SVM) and Generalized Linear Models (GLM)—to model Soil Erosion Susceptibility (SES) in the Saddang Watershed, Indonesia. It incorporates environmental, hydrological, and topographical factors to improve prediction accuracy. The approach includes multi-source geospatial data collection, erosion inventory mapping, and relevant factor selection. SVM and GLM were applied to classify SES, with performance evaluated using accuracy, AUC, and precision metrics. Results show SVM classified 40.59% of the area as moderately susceptible and 38.50% as low susceptibility. GLM identified 24.55% as very low and 38.59% as low susceptibility. Both models demonstrated high accuracy (SVM: 87.4%, GLM: 87.2%) and strong AUC values (SVM: 0.916, GLM: 0.939), though GLM showed better specificity and recall. Feature importance analysis highlights that GLM favors hydrological factors like river proximity and drainage density, while SVM balances across various environmental inputs. These findings affirm the value of ML-based geospatial modeling for SES assessment, supporting interventions such as reforestation and erosion control. SVM is suitable for localized planning, whereas GLM offers strategic-level insights. This research contributes to advancing environmental modeling by embedding domain knowledge into ML frameworks, and suggests future work integrate real-time remote sensing and more sophisticated models for broader SES prediction.
Integrasi Sosialisasi Dan Pendampingan Verifikasi Program BSPS Di Kelurahan Tomulabutao Selatan Ma'sum, Ratna Dwi; Palilati, Mifidyah Putri; Olii, Rizky Selly Nazarina; Djau, Rahman A; Ishak, Sahional; Talango, Novriyanti
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v6i2.12229

Abstract

Meningkatkan standar perumahan bagi masyarakat berpenghasilan rendah menjadi layak huni merupakan tujuan dari inisiatif program Bantuan Stimulan Perumahan Swadaya. Namun program ini sering kali terdapat hambatan dalam pelaksanaan program terutama dalam hal pemahaman masyarakat mengenai prosedur verifikasi penerima bantuan dan mekanisme program. Tujuan dari kegiatan pengabdian ini adalah untuk mengevaluasi seberapa efektif integrasi sosialisasi dan pendampingan kegiatan verifikasi yang dilakukan dalam mendukung keberhasilan pelaksanaan program Bantuan Stimulan Perumahan Swadaya di Kelurahan Tomulabutao Selatan. Temuan menunjukkan bahwa penggabungan kedua strategi ini dapat meningkatkan pemahaman masyarakat tentang tujuan dan manfaat program serta dapat meminimalisir kesalahan informasi administrasi dan meningkatkan kesesuaian data penerima bantuan. Selain itu, strategi ini juga meningkatkan hubungan masyarakat dengan pemerintah dan meningkatkan kepercayaan terhadap program Bantuan Stimulan Perumahan Swadaya melalui partisipasi masyarakat untuk mencapai tujuan program. meningkatkan kolaborasi pemangku kepentingan dan mengintensifkan sosialisasi dan pelatihan pendamping merupakan beberapa ide yang disarankan selain itu diharapkan metode ini akan menjadi solusi jangka panjang untuk mewujudkan skema perumahan berbasis pemberdayaan Masyarakat yang efektif.
Enhancing Compressive Strength of Self-Compacting Concrete (SCC) through Rice Husk Ash and Superplasticizer Incorporation Olii, Muhammad Ramdhan; Ali, Azhar Zukur Putra M.; Djau, Rahman Abdul; Doda, Nurhayati; Olii, Rizky Selly Nazarina
Jurnal Teknik Vol 23 No 1 (2025): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v23i1.611

Abstract

The increasing demand for sustainable construction materials has encouraged the utilization of alternative materials, such as rice husk ash (RHA), and the use of chemical admixtures like superplasticizers in self-compacting concrete (SCC). This study aims to evaluate the effects of RHA and superplasticizer incorporation on the fresh and hardened properties of SCC, focusing on flowability, segregation resistance, and compressive strength. The experimental program involved three SCC mixtures: normal concrete, and SCC with 4% and 8% RHA and superplasticizer by cement weight. Fresh concrete properties were assessed using the slump flow, V-Funnel, and L-Box tests, while compressive strength tests were conducted on cube specimens after 28 days of curing. The results indicated that the addition of 4% RHA and superplasticizer enhanced the compressive strength to 34.02 MPa and maintained flowability within the specified limits, with an average slump flow diameter of 675–697 mm, V-Funnel time of 7.35–8.72 seconds, and L-Box ratio of 0.84–0.85. However, the 8% RHA mixture exhibited a decline in compressive strength (28.51 MPa), highlighting the detrimental effects of excessive superplasticizers on particle cohesion. Furthermore, the use of RHA reduced concrete density, showcasing its potential for lightweight construction applications. These findings confirm that a 4% RHA and superplasticizer dosage optimizes SCC performance, supporting sustainable construction through resource-efficient and durable materials. The study underscores the need for precise mix designs and suggests broader applications of RHA and chemical admixtures in advancing green concrete technologies.
Compressive Strength Performance of Rice Husk Ash-Based Geopolymer Concrete with Fly Ash as a Secondary Material Olii, Muhammad Ramdhan; Saliko, Maxidin; Doda, Nurhayati; Nento, Sartan; Olii, Rizky Selly Nazarina
Jurnal Teknik Sipil dan Lingkungan Vol. 10 No. 2: October 2025
Publisher : Departemen Teknik Sipil dan Lingkungan IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jsil.10.2.259-266

Abstract

Concrete production heavily relies on cement, whose manufacturing significantly contributes to carbon emissions, necessitating alternative materials for sustainable construction. This study investigates the effect of varying compositions of rice husk ash (RHA) and fly ash on the compressive strength and workability of concrete. Five variations of RHA and fly ash ratios (80:20, 75:25, 70:30, 65:35, and 60:40) were tested to identify the optimal mixture. The results show that the 60:40 ratio produced the highest compressive strength of 16.66 MPa and a slump value of 9.5 cm, indicating enhanced workability and mechanical performance. This finding highlights the complementary roles of RHA, which contributes to pozzolanic activity, and fly ash, which enhances hydration and cementitious properties. Excessive RHA content, however, leads to reduced strength due to its lower reactivity. The exponential trend observed in the compressive strength characteristics (R² = 0.9081) confirms the nonlinear relationship between material composition and performance. This research aligns with previous studies demonstrating the benefits of using industrial by-products in concrete. The findings underscore the potential of combining RHA and fly ash as an eco-friendly solution for high-strength concrete, promoting waste utilization and sustainability in the construction industry. Future studies should explore long-term durability and scaling for industrial applications.
Co-Authors Ali, Azhar Zukur Putra M. Djau, Rahman A Doda, Nurhayati Haris Djafar, Haris Ishak, Sahional Ma'sum, Ratna Dwi Nento, Sartan Olii, Muhammad Ramdhan Palilati, Mifidyah Putri Rahman A. Djau Ririn Pakaya Saliko, Maxidin Talango, Novriyanti