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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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The paving molding machine is one of the machines used to produce black paving at PT XYZ. Based on the historical data from the engineering maintenance department, this machine has the highest breakdown frequency which affected the low performance and productivity of the machine. To solve this problem, the effectiveness of the paving molding machine was analyzed using overall resource effectiveness (ORE) methods. ORE aims to analyze machine indicators, consisting of readiness, availability of the facility, changeover efficiency, availability of material, availability of manpower, performance efficiency, and quality rate. The ORE analyzed result shows that values of performance efficiency of paving molding machine were 64.54% and still below the standards of the ORE. To increase the ORE, a design of autonomous maintenance (AM) was proposed. The proposed design means the operator is given the responsibility to maintain the basic condition of the machine to minimize the damage of the paving molding machine at PT XYZ. The result of ORE analysis, especially in the performance efficiency, will be an input for the engineering maintenance department to make an AM basic design that can be executed by each of the machine's operators. In general, this research’s novelty is to combine the application of the ORE method with the AM basic design, whereas the AM is one of the pillars of total productive maintenance (TPM).

ABC is a company engaged in the production of automotive spare parts and accessories, especially motorcycles. The problems faced by the company, there is frequent damage to the CNC Milling A. The company applies preventive maintenance and corrective maintenance activities, but these maintenance activities have not yet run optimally. Therefore, a maintenance system is developed to improve machine reliability. The method used is Reliability Centered Maintenance (RCM), with the aim of determining the optimal maintenance interval and estimation of efficient maintenance costs. RCM is done by analyzing the Failure Modes Effect and Critical Analysis (FMECA). The results of the FMECA analysis are in the form of a Risk Priority Number (RPN) that shows the components of rail bearings, spindle bearings, and hoses as critical components of the system. Through the RCM method, the maintenance policy is generated in the form of 2 scheduled on-condition tasks, 2 scheduled restorations, and 3 scheduled discard tasks with maintenance time intervals in accordance with the task category and can save maintenance costs of IDR 175.602.300.Abstrak. PT. XYZ merupakan perusahaan yang bergerak dibidang produksi spare part dan aksesoris otomotif khususnya sepeda motor. Permasalahan yang dihadapi perusahaan yaitu sering terjadinya kerusakan pada mesin CNC Milling A. Perusahaan menerapkan kegiatan preventive maintenance dan corrective maintenance namun kegiatan maintenance ini belum berjalan dengan optimal. Oleh karena itu, dilakukan pengembangan sistem pemeliharaan untuk meningkatkan keandalan mesin. Metode yang digunakan yaitu Reliability Centered Maintenance (RCM) dengan tujuan menentukan interval waktu pemeliharaan yang optimal dan estimasi biaya pemeliharaan yang efisien. RCM dilakukan dengan menganalisis kegagalan dengan analisis Failure Modes Effect and Criticality Analysis (FMECA). Hasil dari analisis FMECA ini berupa nilai Risk Priority Number (RPN) yang menunjukkan komponen bearing rel, bearing spindle dan selang sebagai komponen kritis pada sistem. Melalui metode RCM, dihasilkan kebijakan maintenance berupa 2 scheduled on-condition task, 2 scheduled restoration dan 3 scheduled discard task dengan interval waktu maintenance sesuai dengan kategori task serta dapat menghemat biaya pemeliharaan sebesar Rp175.602.300.