Jurnal Ilmiah Giga

Evaluasi Nilai Signal to Noise Ratio (SNR) dengan Penerapan Aturan 10 kVp dan 15% kVp pada Objek Stepwedge

2025-01-18T12:14:43+07:00

Radiographic images are medical images obtained using ionizing radiation. The quality of radiographic images is influenced by many factors, including exposure factors and the presence of anode heel effect. Several strategies are used to improve the quality of radiographic images while still providing a reasonable dose, one of which is the kVp rule. This study discusses the analysis of Signal to Noise Ratio (SNR) values using different rules, the 10 kVp and 15% kVp rules. This study aims to obtain a combination of exposure factors that produce images with the highest SNR. The tube voltage used is 40 kV to 70 kV, varied according to the 10 kVp and 15% kVp rules, with a total of 4 variations of exposure factors for the 10 kVP rule and 5 variations for the 15% kVp rule. An aluminum stepwedge with 10 levels with a height of 2 mm each is used as an object. The stepwedge is placed horizontally, parallel to the examination table with a distance of 100 cm from the focus of the X-ray machine. The results of the study indicate that increasing the exposure factor can increase the SNR value. The higher the tube voltage and mAs, the SNR value will increase. The results of this study indicate that the use of 70 kV and 4 mAs is more recommended because it produces lower noise than the use of 70 kV and 2 mAs.

Comprehensive Review of Strengths and Weaknesses of Solar-Biomass Hybrid Power Systems

2025-07-05T15:11:52+07:00

Solar-biomass hybrid power systems (SBHPS) offer a promising solution to global energy and climate challenges by integrating two complementary renewable sources. This study presents a systematic literature review (SLR) based on 543 articles retrieved from Scopus and Web of Science (2014 to 2024), with 76 full texts screened, 26 meeting the inclusion criteria, and 18 scoring ≥4/5 in quality based on CASP and MMAT frameworks. The analysis identifies key advantages of SBHPS, including improved energy efficiency, supply reliability, greenhouse gas emission reduction, and local resource utilization. Conversely, barriers include high capital costs, resource dependency, technical complexity, and policy constraints. The novelty of this review lies in its integrated, multi dimensional synthesis across technical, economic, environmental, and social aspects, filling a gap left by earlier SBHPS reviews. The findings offer practical insights for energy planners, technology developers, and policymakers aiming to implement sustainable and regionally adapted hybrid systems

Rancang Bangun Alat Pendeteksi Sampah Logam dan Non Logam Secara Otomatis Berbasis Mikrokontroler ESP32

2025-05-26T11:04:16+07:00

Waste management still faces major challenges, particularly due to the use of conventional bins that mix metal and non-metal waste. This practice leads to the accumulation of mixed waste, negatively impacting environmental cleanliness and aesthetics, while also complicating the recycling process, which is crucial for ecosystem health and sustainability. Therefore, this study aims to design and develop an automatic metal and non-metal waste detection device based on the Internet of Things (IoT), utilizing the ESP32 microcontroller. The system integrates an ultrasonic sensor for detecting non-metal waste and monitoring volume, an inductive proximity sensor for identifying metal waste, and an infrared sensor to indicate when the bin is full. The device successfully classifies waste into two categories: metal and non-metal. The inductive proximity sensor effectively detects metal waste, while the ultrasonic sensor accurately identifies non-metal waste within a range of less than 15 cm. A servo motor directs metal waste to the right and non-metal waste to the left. Detected waste types are displayed on an LCD screen, and notifications are sent via the Blynk application. This system is expected to improve the efficiency and accuracy of waste management and support more effective environmental conservation efforts.

Analisis Kandungan Saponin Dalam Ekstrak Daun Sirsak (Annona muricata L) Untuk Potensi Biosurfaktan

2025-05-26T10:58:03+07:00

Domestic waste, especially that originating from the use of synthetic detergents, is a major cause of severe environmental pollution due to its content of chemicals that are difficult to degrade and potentially harmful to aquatic ecosystems. This study aims to analyze the saponin content in Annona muricata L leaf extract and evaluate its potential as a biosurfactant in environmentally friendly detergent applications. Extraction was carried out using the maceration method with variations in the sample: solvent ratio (1:4, 1:6, 1:8, 1:10, and 1:12) using 96% ethanol. Saponin content analysis was carried out using Thin Layer Chromatography (TLC), while biosurfactant characterization was tested through foam height, foam stability, and pH. The results showed that the highest saponin content was obtained at a ratio of 1:10 with a content of 2.79%. The foam height test showed inconsistent variations, with the highest values at ratios of 1:6 and 1:12 of 44 mm. The highest foam stability was obtained at a ratio of 1:4 of 4.2%. All samples had a pH in the range of 5.95 – 6.79, which meets the SNI 4075-1:2017 standard for liquid detergent (pH 5 – 10). Based on these results, soursop leaf extract has the potential as a natural biosurfactant for environmentally friendly detergent formulation.

Identifikasi Lapisan Akuifer Air Tanah menggunakan Metode Resistivitas di Dusun Puaringan, Desa Pelangan, Lombok Barat

2025-07-18T08:38:02+07:00

The aim of this study is to identify the groundwater aquifer layer using the resistivity method in Puaringan, Pelangan Village, West Lombok. The aquifer layer is a layer of rock that has the ability to store groundwater well. This layer also tends to have high porosity so that fluid/water is trapped in the layer. The resistivity method used is vertical electric sounding (VES). This method is effective in detecting the subsurface vertically. Data acquisition was carried out using two sounding points (S1 and S2). Sounding points S1 and S2 were processed using IP2WIN software to produce a picture of the subsurface rock layer. The results of the S1 point study showed that the groundwater aquifer layer was at a depth of 113-126 meters identified as a sandstone layer. The results of the S2 point study showed that the groundwater aquifer layer was at a depth of 105-198 meters identified as a sandstone layer.