Journal of Engineering and Scientific Research

Comparison of DBSCAN Algorithm and Resistivity Data Inversion, Case Study of Identification of Granite Distribution in ‘Zs’ Area

2025-06-20T09:05:46+00:00

The emergence of automatic data analysis techniques based on data mining algorithms can be applied in various fields including geophysics, thereby improving the quality of interpretation results. Wanner-Schlumberger configuration resistivity data were used in this study. Geoelectric data processing usually uses inversion methods, to determine the true resistivity distribution below the surface. This study proposes the use of the DBSCAN algorithm. Inversion comparison with linearization and clustering with the DBSCAN algorithm is carried out to identify granite dispersal in the 'ZS' area. The clustered cross-section has a clearer picture than the inversion cross-section. Based on the interpretation results on the resistivity cross-section that has an RMS error value of 0.68%, three ranges of resistivity values can be interpreted, namely low resistivity values < 100 ohms.m are indicated as soil, medium resistivity values of 100 – 500 ohms.m are indicated as granite gravel aquifers, and resistivity values > 500 ohms.m are indicated as pink basement granite. The cross-section of clustering results with input parameters eps 0.22 and mints 7 is interpreted. Namely, cluster 1 is an unsaturated granite gravel aquifer, cluster 2 is a saturated granite gravel aquifer, cluster 3 pink granite bedrock, cluster 4 soil and noise of 5.54%

Generation Expansion Planning: A Bibliometric Review

2025-04-11T12:40:01+00:00

Generation expansion planning is a prominent topic of energy-related scientific discourse. Due to operational constraints, the penetration of renewable energy sources, and uncertainty, there have been numerous changes in the planning of generation expansion. This article provides a bibliometric analysis of recent research on generational expansion planning. Relevant authors, sources, terms, disciplines, and conceptual analysis are used for bibliometric analysis. This bibliometric is based on a Scopus database data set for the years 2013 to 2023.

Cement Supply Chain Model of Palangka Raya Using System Dynamics Method

2025-04-11T12:43:25+00:00

One of the most strategic material resources in the execution of construction is cement. It is estimated that the demand for cement to support construction activities in Indonesia is approximately 78.66% of national cement consumption, with cement consumption in Indonesia increasing by 14.1% in the first quarter of 2021 to 18.19 million tons. The objective of this research is to understand how the dynamic supply chain model and cement inventory in Kota Palangka Raya will be from 2024 to 2028. In this research procedure, data analysis techniques use the system dynamic method with the Vensim program, utilizing data from interviews with distributors and retail stores scattered in Kota Palangka Raya and supporting research data. The results of the supply chain model for cement include three variables: stock, in, and out. The total inventory of Conch cement in 2024 is 1140555 zak, in 2025 is 1547654 zak, in 2026 is 6192387 zak, in 2027 is 1946944 zak, and in 2028 is 1918412 zak. The total inventory of Gresik cement in 2024 is 548287 zak, in 2025 is 1785559 zak, in 2026 is 1826060 zak, in 2027 is 1849395 zak, and in 2028 is 1838869 zak. The total inventory of Tiga Roda cement in 2024 is 42763390 zak, in 2025 is 250804 zak, in 2026 is 177705 zak, in 2027 is 321805 zak, and in 2028 is 183585 zak.

Design and Implementation of A Solar-Driven Spy Security Motion Detector

2025-06-21T03:37:15+00:00

This project designs and implements a solar-powered spy security system with motion detection capability. The system integrates a solar panel, charge controller, battery, PIR sensor, 555 timer IC, buzzer, WiFi module, and a spy camera. The solar panel recharges the battery, providing a sustainable power source. The PIR sensor detects motion, triggering the 555 timer IC to activate the buzzer and spy camera. The system captures images or records videos only when motion is detected, conserving energy and memory space. The spy camera connects to a smart device via WiFi, enabling remote viewing of captured footage. Testing demonstrated the system's effectiveness as a reliable and efficient security solution. This project addresses the limitations of traditional CCTV cameras, providing a more efficient and sustainable solution for home security. The design leverages principles of electrical and electronics engineering, including circuit design, electronics, and control systems. Future enhancements can include integrating Artificial Intelligence to improve security and reduce false alarms.

A Consensus-Based Feature Selection and Classifier Benchmarking for Network Anomaly Detection

2025-04-22T07:25:01+00:00

Efficient anomaly detection in network traffic is essential for securing modern digital infrastructures. This study presents a comprehensive comparative analysis of six feature selection methods—including Mutual Information, Recursive Feature Elimination (RFE), LASSO, Random Forest Importance, ANOVA, and Chi-square—and seven machine learning classifiers on the NF-UQ-NIDS-v2 dataset. Experimental results demonstrate that advanced feature selection methods, particularly Mutual Information and RFE, combined with ensemble classifiers such as Random Forest and XGBoost, achieve superior detection performance. A consensus analysis reveals that features like protocol type, packet length, and flow duration are consistently most informative for anomaly detection. These findings provide practical guidance for designing accurate and efficient intrusion detection systems in high-dimensional network environments.

Design of Long-Distance Shrimp Pond Monitoring Using 2.4 GHz IoT Digital Radio Line-of-Sight Transmission

2025-06-22T09:09:14+00:00

Water quality plays a vital role in shrimp farming, as suboptimal conditions can increase stress levels and disease susceptibility, potentially resulting in mass mortality. Real-time water quality monitoring enabled by Internet of Things (IoT) technology presents a promising solution to this issue. However, implementing IoT systems in shrimp ponds, typically situated in open, low-vegetation, and aquatic environments, poses technical challenges, particularly in maintaining line-of-sight (LOS) conditions between sensor nodes and gateway nodes. This study utilizes a linear path profiling method to evaluate LOS feasibility. A straight-line path is drawn between the transmitter and receiver to determine the minimum required antenna height for unobstructed signal propagation. For a 2.4 GHz WiFi-based transmission over a 1.27 km distance, ensuring 30% Fresnel zone clearance requires the sensor antenna to be elevated 1.5 meters above the water surface, while the gateway antenna must be positioned at a minimum height of 6.5 meters above ground level, assuming a ground elevation of 1.5 meters above mean sea level (AMSL). To achieve a minimum Received Signal Level (RSL) of -80 dBm, simulation results indicate that the system needs a transmit power of at least 12 dBm when using a gateway antenna with 8 dBi gain, or 8 dBm if the gateway antenna has 20 dBi gain, assuming the transmitter antenna gain is 2 dBi. The study also presents a simulation-based relationship between RSL and transmit power across different receiver antenna gains (2 dBi, 8 dBi, and 20 dBi), providing insights for optimizing IoT-based monitoring systems in aquaculture environments.