Journal La Multiapp

Evaluation of Slope Stability in Mining Areas Using the Morgenstern Price Method

Tue, 07 Oct 2025 14:19:50 +0700

Indonesia’s mining sector, particularly in areas like Morowali Regency, Central Sulawesi, faces significant geotechnical challenges due to its location in the Pacific Ring of Fire. One of the key concerns in open-pit mining operations is slope instability, which can lead to landslides, threaten worker safety, damage infrastructure, and disrupt production. This study evaluates the slope stability in the Sambalagi site of PT. Wosindo Berkat Abadi using the Morgenstern-Price method, a limit equilibrium approach known for its accuracy in heterogeneous slope conditions. Field data were collected, including slope geometry, geological conditions, material strength, and hydrogeological factors. The safety factor (FK) was calculated based on geotechnical parameters such as cohesion, internal friction angle, and unit weight of the slope materials primarily saprolite and limonite. The actual slope FK value at PIT D was found to be 0.974, below the standard requirement (≥1.3) set by the Ministerial Decree No. 1827K/30/MEM/2018. To improve stability, a revised slope design was proposed, including reducing slope angles to 35°, increasing bench widths to 2 meters, and decreasing slope height per bench to 4 meters. The simulation of this revised geometry showed that it could achieve the required FK value. The study contributes to safer and more efficient mine planning by demonstrating the importance of integrating detailed geotechnical analysis in slope design, especially in tropical high-rainfall mining regions.

Strengthening MSME Digital Transformation through Android-Based Geolocation Mapping: Implementation of Location-Based Services and the Haversine Formula

Thu, 09 Oct 2025 10:53:25 +0700

Information and Communication Technology (ICT) plays a strategic role in reducing socio-economic disparities and accelerating inclusive development, especially in rural areas. In Indonesia, Micro, Small, and Medium Enterprises (MSMEs) are the dominant sector, absorbing over 96% of the workforce and contributing around 60% to the national Gross Domestic Product (GDP). However, many MSME actors particularly in remote regions such as North Sulawesi still face challenges in accessing digital markets due to limited technology adoption and lack of business location exposure. This study aims to develop an Android-based application that integrates geolocation services through the implementation of Location-Based Services (LBS) and the Haversine formula. LBS is used to detect users' real-time locations via GPS, while the Haversine formula calculates the precise distance between users and MSME locations. The application is designed to send automatic notifications when a user is within a 1000-meter radius of a registered MSME location. This research adopts a software engineering approach through the design of context diagrams, system flowcharts, and the implementation of the Haversine algorithm using the Dart programming language. Testing results show that the distance difference between the application and Google Maps ranges only from 1.5 to 2.9 meters, and the distance calculated by the application matches 100% with online Haversine calculators. These findings indicate that the system has high accuracy and is feasible to be applied as a solution to improve MSME visibility, enhance community digital literacy, and strengthen the local economy through e-commerce.

Comparative Analysis of the Support Capacity and Settlement of Bored Pile Foundations Using Manual Methods and Allpile Software: Case Study

Thu, 09 Oct 2025 13:22:26 +0700

Foundations are structural elements that function to bear the load of a building and transfer it to the ground to a certain depth. Foundations must be designed so that the transferred load does not exceed the bearing capacity of the soil, as this can cause excessive settlement and lead to structural collapse. The purpose of this study is to determine the comparison of bearing capacity and settlement of bored pile foundations in the Poltekkes Kemenkes Mataram building using two approaches: the manual method and the Allpile software. The analysis was conducted based on secondary data obtained from the Detailed Engineering Design (DED), results of the Standard Penetration Test (SPT), and laboratory soil test results. The results of the manual Meyerhof method yielded an ultimate bearing capacity of 13,127.79 kN and a single pile settlement of 0.0455 m. The manual Reese and Wright method yielded an ultimate bearing capacity of 2,697.3453 kN and a single pile settlement of 0.0241 m. Meanwhile, the Allpile software calculation yielded an ultimate bearing capacity of 14,391.891 kN and a single pile settlement of 0.0027 m. All settlement values, both for single piles and groups, remain below the maximum permitted limit.

Literature Review on Vehicle Routing Problem: Approaches, Algorithms and Current Challenges

Mery Andani Pangaribuan, Juliza Hidayati, Harmein Nasution — Mon, 13 Oct 2025 00:00:00 +0700

The Vehicle Routing Problem (VRP) is one of the basic combinatorial optimization problems that takes a central place in the sphere of logistics, transportation, and supply-chain management. A systematic literature review (SLR) of VRP scholarship dated 2000 to 2025 is conducted herein, where over 500,000 publications are analyzed to carry out the study of VRP solutions evolution and methodological advancements as well as their practical use. The results highlight the current popularity of metaheuristic algorithms, such as Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO), in solving complex variants of VRP, in particular, the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The combination of real-time data streams, machine-learning methods and adaptive algorithms represents a revolutionary track, and helps to develop more active and responsive VRP models. Moreover, increased attention to sustainability and green logistics has triggered the development of the eco-efficient VRP models, which combine the use of electric vehicles (EVs) and energy-consumption optimization. The spread of autonomous vehicles presents new opportunities and threats to future VRP solutions, particularly in the area of urban freight and last-mile delivery. In conclusion, the review outlines future streams of research, highlighting the need to find adaptive, sustainable, and autonomous VRP models that can resolve the growing complexities in the modern world of logistics.

Grid System Analysis of the Isolated Unit Load Device Timpah in the De-Dieselization Program

Eko Bagus Aribawa, Ahmad Rofii — Thu, 23 Oct 2025 13:29:05 +0700

The de-dieselisasi program has become a key strategy of PT PLN (Persero) in reducing dependence on diesel power plants in isolated areas, including ULD Timpah, Kapuas Regency, Central Kalimantan. This study aims to analyze the effectiveness of the grid system performance after the implementation of de-dieselisasi, focusing on technical, operational, and financial aspects. The method used is a descriptive quantitative approach with a case study, supported by primary and secondary data, as well as system simulations using the ETAP application. The results show that the electricity distribution voltage remained stable within operational standards, both before and after the project (20.54 kV to 19.17 kV). Operationally, the electricity service hours increased from 16 hours to 24 hours per day, improving the quality of service to customers. Initial financial analysis indicated that the project was not feasible; however, an avoided cost analysis resulted in a positive Net Present Value (NPV) of IDR 162.55 billion, demonstrating significant cost efficiency compared to diesel-based operations. In conclusion, the de-dieselisasi program at ULD Timpah successfully improved the efficiency, reliability, and sustainability of the electricity system in remote areas, providing positive impacts technically, operationally, and financially to support national electrification goals.

Analysis of Experience using Shopee E-Commerce AI Features Among Young People with Modified TAM

Dela Dewi Puspita, Arista Pratama, Asif Faroqi — Fri, 24 Oct 2025 14:13:42 +0700

The use of Artificial Intelligence (AI) in e-commerce applications continues to experience rapid development, particularly in providing features that enhance personalization and provide a better shopping experience. However, there remains a challenge in understanding the extent to which experience in using AI features influences the level of usage of the Shopee application, especially among young people who are the largest user segment. This study aims to analyze the effect of experience using AI features on Shopee application usage using a modified Technology Acceptance Model (TAM) approach. The analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to examine the relationship between variables in the research model. A total of 11 hypotheses were proposed and empirically tested based on data obtained from 430 young respondents who are active Shopee users. The analysis results showed that all hypotheses were accepted, which means that experience using AI features plays a significant role in shaping perceptions of usefulness, user experience, and positive attitudes toward technology, which ultimately influence the intention and behavior of using e-commerce applications. These findings indicate the importance of developing AI features that are responsive and relevant to the needs of young users to improve loyalty and shopping experience. Thus, the practical implications of this research provide strategic input for e-commerce service providers in optimizing AI-based experiences to maintain competitiveness in the increasingly competitive digital market

Application of SVM and Naive Bayes with PSO for the Classification of Saloka Amusement Park Reviews

Indira Alifia Putri, Khothibul Umam, Maya Rini Handayani, Hery Mustofa — Tue, 04 Nov 2025 13:56:07 +0700

Visitor opinions on tourist destinations can be evaluated through sentiment analysis based on textual reviews. This study aimed to compare the performance of Support Vector Machine (SVM) and Naive Bayes (NB) algorithms in classifying visitor sentiments toward reviews of Saloka Theme Park, while also assessing the impact of parameter optimization using Particle Swarm Optimization (PSO). A total of 740 reviews were collected from the Traveloka platform and underwent text preprocessing. The optimization process targeted key parameters of each algorithm to improve the F1-score. Experimental results showed that the unoptimized SVM achieved an accuracy of 89 percent, while NB reached 86 percent. After applying PSO, SVM's accuracy dropped to 84 percent, whereas NB improved to 85 percent with more balanced classification across sentiment classes. These results recommend the integration of Naive Bayes with Particle Swarm Optimization as a potential approach for sentiment classification of tourism reviews, particularly in the case study of Saloka Theme Park.

Cost Optimization Through Value Engineering and Risk Analysis in Industrial Building Retrofitting Projects

Ferdinandus Danu Trisamiyanto, Budi Susetyo — Tue, 18 Nov 2025 10:08:57 +0700

Retrofitting projects in industrial facilities are often prone to delays and cost overruns due to various technical, logistical, and operational risks. These challenges include limited working access, delayed material delivery, and regulatory constraints, all of which can significantly affect project efficiency and cost performance. This study aims to optimize project costs by integrating value engineering and risk analysis methods in retrofitting construction. A mixed-method approach was employed, combining case studies with statistical analysis using Structural Equation Modeling – Partial Least Squares (SEM-PLS). Data were collected through surveys, expert interviews, field observations, and project documentation. The findings indicate that the integration of value engineering and risk analysis effectively reduces project costs without compromising quality. The application of value engineering resulted in an alternative solution using fire-rated drywall, which led to a cost saving of approximately IDR 5.36 billion or 9.63 percent of the original estimated cost. Additionally, the Life Cycle Cost (LCC) analysis showed that this alternative provided a more economical long-term solution, with a life cycle cost difference of 13.73 percent compared to the baseline material. These results highlight the practical benefits of integrating VE and risk management, offering a structured and data-driven framework for achieving cost-effective and sustainable outcomes in complex industrial retrofitting projects.