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Bangunan yang berada di daerah rawan gempa dengan tanah lunak cenderung memiliki tingkat kerentanan yang lebih tinggi. Dalam merancang struktur bangunan bertingkat, beban gempa menjadi salah satu parameter utama yang sangat berpengaruh. Kekuatan sebuah bangunan ditentukan oleh elemen-elemen struktur yang menopangnya dan kolom merupakan elemen struktur yang memiliki peran penting dalam konstruksi. Jika kolom mengalami kegagalan, bangunan yang ditopangnya berpotensi runtuh. Oleh karena itu, rendahnya ketahanan konstruksi di wilayah ini tetap menjadi permasalahan yang sedang diteliti saat ini. Tujuan penelitian ini untuk mengetahui seberapa jauh pengaruh variasi dimensi kolom terhadap struktur bangunan terhadap gempa, sehingga diperoleh ukuran dimensi kolom yang paling optimal. Pada penelitian ini, objek yang digunakan adalah bangunan 8 lantai yang terdiri dari lantai 1-3 merupakan kantor dan lantai 4-8 merupakan hunian. Pada perencanaan ulang bangunan ini berpedoman pada persyaratan beton struktural (SNI 2847:2019), perencanaan ketahanan gempa (SNI 1726:2019), dan beban minimum untuk perancangan bangunan gedung dan struktur lain (SNI 1727:2020), dengan pemodelan menggunakan software ETABS V.22. Redesain bangunan ini untuk mengetahui ukuran dimensi kolom yang paling optimal. Hasil penelitian perubahan dimensi kolom terhadap kekuatan struktur bangunan yakni dengan memperbesar dimensi kolom, kapasitas penampang bertambah yang dapat meningkatkan ketahanan kolom terhadap beban gempa. Selain itu, dimensi yang lebih besar juga membantu mengurangi tegangan aksial pada kolom, sehingga menurunkan risiko kegagalan akibat tegangan berlebih atau kelelahan material. Semakin besar dimensi kolom yang digunakan maka semakin kecil nilai simpangan yang dihasilkan. Artinya dengan dimensi kolom yang lebih kecil memiliki resiko yang lebih tinggi, sebab nilai simpangan lebih dekat dengan batas yang diijinkan. Akan tetapi hasil analisis menunjukkan bahwa dimensi kolom dengan berbagai variasi masih aman dari pengaruh delta izin.

Work fatigue is a significant issue faced by employees in Indonesia, with approximately 65% experiencing symptoms that reduce their concentration and performance. Excessive workload, particularly mental workload, can result in cognitive strain and decreased productivity. This study aims to measure the level of mental workload experienced by quality control employees at PT Delta Dunia Tekstil I using the NASA Task Load Index (NASA-TLX) method. The research also investigates the relationship between employees’ age, length of service, and perceived mental workload through T-tests and F-tests to provide a more comprehensive analysis. Data were collected from 20 quality control employees who are responsible for monitoring textile production under strict standards. Results from NASA-TLX calculations indicate that the average mental workload falls within the high category, with effort and own performance emerging as the dominant dimensions contributing to mental strain. Correlation analysis shows a negative but not statistically significant relationship between age, length of service, and mental workload, suggesting that other factors play a stronger role in shaping employees’ perceptions. The study further highlights fluctuations in employee attendance and quality performance indicators, emphasizing the importance of workload evaluation. To address these issues, recommendations include adding more operators per machine, which is projected to reduce mental workload scores by approximately 33%. Such measures can optimize task distribution, improve efficiency, and promote employee well-being. Overall, the findings suggest that systematic workload assessments using tools such as NASA-TLX are essential for enhancing productivity and supporting sustainable human resource management in the textile industry.

This research investigates the implementation of Random Forest algorithms combined with Synthetic Minority Over-sampling Technique (SMOTE) to predict elementary school dropout rates in Indonesia, supporting the Indonesia Emas 2045 vision. A significant gap was identified in previous studies, which, despite utilizing artificial intelligence for dropout interventions, had not integrated temporal dimensions into data analysis. A temporal data-based classification model was developed using Indonesian Ministry of Education data from 2021-2023, incorporating lag features, delta calculations, and rolling statistics. Two models were implemented: one with SMOTE achieving 99% accuracy with perfect recall for high-risk regions, while the non-SMOTE model reached 100% accuracy. Temporal features were identified as crucial predictors, reflecting external fluctuations and annual changes impacting dropout decisions. This approach enables educational institutions to allocate resources more efficiently by prioritizing operational assistance for high-risk schools. The model's capacity to identify high-risk regions with 100% recall represents a strategic investment in strengthening Indonesia's human resource sustainability. To address the limitations of provincial aggregate data, expansion to include individual-level variables and model validation at district or school scales is recommended for future research.

Supplier selection is a strategic element in supply chain management to ensure operational efficiency and business sustainability. This study aims to determine the best urea supplier for PT DSSA 1, a textile company specializing in dyeing and finishing processes, using the Analytic Network Process (ANP) method. This method was chosen for its ability to systematically analyze interdependencies among criteria, leading to more accurate and comprehensive decision-making. Data were collected through direct observation, in-depth interviews with internal company sources, and relevant literature reviews. The analysis was based on five main criteria: quality, price, service, delivery, and flexibility, each further broken down into sub-criteria. The results indicate that quality is the most critical criterion, with the highest weight of 23.19%, followed by price, service, and delivery, each with relatively balanced weights of 19.93%. Flexibility, although ranked last with a weight of 17.01%, remains relevant, especially for urgent orders. Based on the analysis, Supplier A was identified as the best choice with the highest overall weight, followed by Supplier B and Supplier C. This study provides practical contributions by offering strategic recommendations for PT DSSA 1 in selecting suppliers that support production efficiency and business sustainability. The study is limited in the scope of criteria and the number of suppliers analyzed. Future research is recommended to explore more variables, expand the sample size, and consider the implementation of digital technologies to optimize the supply chain.