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Evaluasi Implementasi Sistem Manajemen Keselamatan Konstruksi (SMKK) Pada Proyek Waron Hospital
Full text linkFacilities in Surabaya have been developing rapidly in line with the increasing construction of urban infrastructure. Various infrastructure projects are being carried out to enhance the quality of urban facilities and attract investors. One of the most prominent aspects of development is the growing number of high-rise building constructions in Surabaya. In every construction project, the implementation of Occupational Safety and Health (OSH) is a crucial factor that must be considered. OSH serves as a guideline that reminds workers of the importance of maintaining their health, safety, and security while working. This study aims to analyze the implementation of the OSH work program in the Waron Hospital construction project. Data was collected through questionnaires and direct interviews, then analyzed using descriptive statistics with standard deviation and mean methods. Based on the data collected, 48 respondents were directly involved in the project. The study results indicate that the implementation of the OSH program in this project still faces various challenges. Some contributing factors include the company's limited OSH budget, the relatively small project contract value, and the low awareness among workers regarding the importance of the OSH program for their safety
Comparison of GMERF and GLMM Tree Models on Poverty Household Data with Imbalanced Categories
Full text linkDecision tree and forest methods have become popular approaches in data science and continue to evolve. One of these developments is the combination of decision trees with Generalized Linear Mixed Models (GLMM), resulting in the GLMM Tree, which is applicable to multilevel and longitudinal data. Another model, Generalized Mixed Effect Random Forest (GMERF), extends the concept of decision forests with GLMM, effectively handling complex data structures with non-linear interactions. This study compares the performance of GLMM Tree and GMERF models in classifying poor households in South Sulawesi Province, characterized by imbalanced categories. GLMM Tree provides a simple, interpretable classification through tree diagrams, while GMERF highlights variable importance. Initial tests show all three models (GLMM, GLMM Tree, and GMERF) achieve high accuracy and specificity but exhibit low sensitivity. By applying oversampling, sensitivity and AUC are significantly improved, though this is accompanied by a decline in accuracy and specificity, revealing a trade-off. The study concludes that while GLMM, GLMM Tree and GMERF have their strengths, using them together offers a more comprehensive understanding of poverty classification. Handling imbalanced data with oversampling is effective in increasing sensitivity, but careful consideration is needed due to its impact on overall accuracy
Variables Selection Affecting Indonesian Human Development Index Using LASSO
Full text linkAccording to Statistics Indonesia, the Human Development Index (HDI) is a measure that reflects the level of human development achievement in a region, based on three basic dimensions: a long and healthy life, knowledge, and a decent standard of living. There are many factors that are suspected to influence HDI in Indonesia. Another hand, estimation of parameters in regression analysis using the Least Squares Method will experience problems, if the number of independent variables is greater than the number of observations. One method that can be used to overcome this problem is to use the Least Absolute Shrinkage and Selection Operator (LASSO) method. The purpose of this study is the selection of variables that affect Indonesia's Human Development Index (HDI) in 2023 using the LASSO. The LASSO method is known as a model used to select independent variables while overcoming multicollinearity problems. The ridge regression model is used as a comparison model. The results showed that LASSO Analysis is better than Ridge Regression. This can be seen from the Mean Squared Error of Prediction (MSEP) of LASSO (0.34) is smaller than the ridge regression (3.61). In addition, the r-squared value of LASSO is higher, which is 97.6%
Evaluation of cctv placement in industrial areas using the simple additive weighting method
Full text linkDetermining the optimal placement of CCTV cameras in industrial environments is a critical challenge, often complicated by complex layouts, varying operational requirements, and limited resources. This study applied the Simple Additive Weighting (SAW) method to evaluate and prioritize camera placement in four main zones: Production Process Zone, Product Storage Zone, Product Loading Zone, and Access Door/Perimeter. Three multi-criteria decision-making factors were considered: area coverage, installation cost, and operational efficiency of surveillance. The SAW method allows for structured and data-driven analysis, normalizing and weighting each criterion to calculate a final score for each zone. The results revealed that the Product Storage Zone achieved the highest priority score (0.99), followed by the Product Loading Zone (0.84), Access Door/Perimeter (0.77), and Production Process Zone (0.71). These priorities are not in line with the results of the security officer preference survey, but are in line with the opinions of CCTV experts and company managers according to the operational needs of the zones. These findings underscore the effectiveness of the SAW method in providing objective and transparent decision-making for CCTV placement. By integrating quantitative analysis into the design of surveillance systems, this approach optimizes resource allocation and enhances industrial safety. Future research is encouraged to explore the integration of SAW with advanced technologies, such as artificial intelligence and the Internet of Things (IoT), for dynamic and real-time surveillance solutions
Granulator Performance for Urea Granule Quality: A Study on Material Balance and Recycle Seed Ratio
Full text linkGranulation is a critical process in quality of urea fertilizer, particularly their size distribution, significantly affects the product's performance and marketability. Urea synthesis begins with the reaction between ammonia and carbon dioxide, where ammonium carbamate is decomposed to produce urea by granulation process. This research aims evaluate the performance of granulator on urea granule size product quality based on material balance and recycle seed ratio (RSR). The granulator performance in the urea granulation process was evaluated for a production capacity of 3,500 tons/day. The methodology involves data collection from operational records in six days respectively, followed by mass balance analysis and product quality evaluation based on particle size distribution. The analysis revealed a significant deviation between design and actual data. Specifically, the design mass balance indicated a total inlet of 236,726 kg/h and a total outlet of 230,575 kg/h, resulting in a mass deficit of 6,151 kg/h attributed to dust formation and water evaporation. The measured on-size product yield was approximately 98.50% at the outlet, with the desired particle size range of 2–4.75 mm. These findings provide critical insights for process optimization and resource management in urea granulation, emphasizing the need for precise operational control to minimize material losses and ensure product quality compliance with specifications
Comparison of Ordinal Logistic Regression and Artificial Neural Network in Stunting Prevalence Classification
Full text linkThe prevalence of under-five stunting in one of the crucial health problems in Indonesia. Stunting is a growth and development disorder in children due to chronic malnutrition and repeat infections that can have a negative impact on children’s physical and cognitive development. This study aims to analyse the accuracy of the classification of the prevalence of stunting on regencies/cities in Indonesia, in 2022 using two methods, namely Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN). OLR is development of logistic regression applied to response variables with more the two categories that have levels or ranks, while ANN is a method that mimics the function of the biological nervous system and is designed for complex information processing. This study used two proportions of data splitting namely 80:20 and 90:10. Each method produce two models, OLR 1 and OLR 2 for the OLR method, and ANN 1 and ANN 2 for the ANN method. The results show that the ANN 1 model with 80:20 data proportion performs better than other models with an accuracy of 63.37%
Ketalization of Glycerol and Acetone to Solketal: Effect of Temperature, Concentration & Mathematical Model
Full text linkSolketal is a viable method for using glycerol, a by-product of biodiesel production. This study aims to identify the optimal operating parameters for solketal compounds generated from the glycerol ketalization reaction with acetone by using mathematical models that effectively forecast an appropriate framework for this process. This research consists of three critical phases: the ketalization reaction of glycerol with acetone, the characterization of the result solketal products, and the ketalization reaction utilizing the Amberlite IR 120 Na catalyst. The process begins by introducing glycerol and acetone in a mole ratio of 1:3, followed by mechanical Stirring at 500 rpm. The temperature is regulated using a water bath to maintain a constant reaction temperature under specified conditions of 20 °C, 120 °C, 150 °C, and 180 °C, with catalyst masses of 1%, 3%, 5%, and 7%. The mathematical model used is of exponential and polynomial order 2. The findings indicated that the optimal glycerol conversion of 46.01% was attained at 50 °C, using a 5% catalyst concentration throughout a reaction duration of 120 minutes. Second-order polynomial regression is the most appropriate mathematical model to represent this process
COMMUNITY PARTICIPATION IN PHYSICAL TRANSFORMATION OF TOURISM KAMPUNG: THE CASE OF KAMPUNG LAWAS MASPATI
Full text linkUrban kampungs in Indonesia are increasingly being transformed into tourism destinations as part of urban revitalization efforts. However, these transformations often focus on aesthetic improvements and economic outputs without adequately considering the role of community participation in shaping the physical environment. This study investigates how spatial transformation in Kampung Lawas Maspati, Surabaya, is driven not only by policy interventions but also by internal community agency and collaboration with external actors. Using a qualitative descriptive approach with a case study method, the research draws on in-depth interviews, field observation, and spatial mapping through GIS. The analysis focuses on three stages of participation: planning, implementation, and evaluation—and is structured around key transformation categories: public space reorganization, tourism infrastructure integration, and the expression of place identity. The findings reveal that initial transformation was driven by local leadership who mobilized community action toward kampung revitalization. These efforts laid the foundation for formal recognition as a tourism kampung in 2016. The community not only initiated environmental and spatial improvements but continued to co-develop infrastructure with institutional support, including CSR involvement. This changes—community-led in vision, institutionally supported in execution—demonstrates a sustainable form of transformation grounded in local agency. The study highlights how participatory action and place-based leadership are critical to the success and longevity of tourism kampung development. It offers a framework for understanding how physical transformation is shaped through evolving participation and collaboration from the community
Characteristics And Antibacterial Test Of Lactid Acid Bacteria From Sidoarjo Shrimp Petis Against Vibrio Sp, Bacteria
Full text linkIndonesia as the largest archipelago with abundant potential fishery resources contributes to national foreign exchange (Damayanti & Sugiarto, 2022). One of the main commodities of fishery products commonly exported by Indonesia is shrimp (Dewi et al., 2022). Increasing shrimp production through intensive aquaculture faces the challenge of disease, especially Vibrio sp. bacterial infection. The use of antibiotics as a general solution raises resistance problems, so alternatives such as probiotics are needed. Lactic acid bacteria (LAB) are potential candidates for probiotics because of their ability to produce organic acids that inhibit the growth of pathogenic bacteria. This study aims to analyse the characteristics of LAB from Sidoarjo shrimp petis, a typical fermented product that has potential as a source of LAB, and test its antibacterial ability against Vibrio sp. The stages carried out in the study consisted of 5 stages including the first stage of sampling, the second stage of lactic acid bacteria isolation, the third stage of characterisation of lactic acid bacteria isolates, the fourth stage of lactic acid bacteria antibacterial test, and the fifth stage of data analysis. The results showed the total colonies of Lactic Acid Bacteria (LAB) in the six isolates with a value of 7.08 x 106 colonies.mL. LAB characteristics on the six isolates consisted of macroscopic, microscopic, and biochemical characteristics. Microscopic characteristics of the six isolates showed the same results, namely round, white colour, flat and convex elevation, and smooth edges. Microscopic characteristics of the six isolates showed the same results, namely bacillus and gram-positive cell forms. Biochemical characteristics on the six isolates showed different results. Antibacterial tests were carried out after knowing the type of lactic acid bacteria isolates through several characteristic tests, it can be seen that there are 4 isolates including isolates PTS.5.1, PTS.5.2, PTS.6.1, and PTS. 6. 6. The results of antibacterial tests on 4 isolates have antibacterial compounds in inhibiting the growth of gram negative bacteria (Vibrio sp,). Seen the results obtained isolates that have the greatest antibacterial activity is PTS.5.1 with a final result of 10.2625 mm
Phenetic Diversity and Relationships of Sea Lettuce (Ulva spp.) on the Southern Coast of Gunungkidul Yogyakarta Indonesia
Full text linkThe development of tourism areas in the Gunungkidul coastal zone was a potential threat that can affect the marine macroalgae diversity. In order to prevent the decline of macroalgae diversity from these anthropological threats, a comprehensive study is needed to record the population dynamics that occur. The objective of this study is revealing the species diversity and phenetic relationship of the sea lettuce (Ulva spp.). Phenetic analysis of sea lettuce was conducted by observing morphological, anatomical, and biochemical characters. Clustering analysis was done by the UPGMA method, whereas ordination analysis was conducted using the PCA (Principal Component Analysis) method; both of them were calculated using the MVSP 3.1 program. The result of the study discovered four species of Ulva, i.e., Ulva lactuca, Ulva rigida, Ulva compressa, and Ulva linza. The reconstruction of phenetic relationships revealed two main clusters of sea lettuce, exhibiting a percentage difference of 75.2. The PCA analysis exhibited eight characters that significantly influence clustering patterns, including thallus color, blade width, blade thickness, blade hole, stiff edge cells, cell surface diameter, elongated cell shape, and the presence of violaxanthin and neoxanthin