This paper discusses an integrated model of batch production and maintenance scheduling on a deteriorating machine producing multiple items to be delivered at a common due date. The model describes the trade-off between total inventory cost and maintenance cost as the increase of production run length. The production run length is a time bucket between two consecutive preventive maintenance activities. The objective function of the model is to minimize total cost consisting of in process and completed part inventory costs, setup cost, preventive and corrective maintenance costs and rework cost. The problem is to determine the optimal production run length and to schedule the batches obtained from determining the production run length in order to minimize total cost.

This research discusses an integer batch scheduling problems for a single-machine with positiondependent batch processing time due to the simultaneous effect of learning and forgetting. The decision variables are the number of batches, batch sizes, and the sequence of the resulting batches. The objective is to minimize total actual flow time, defined as total interval time between the arrival times of parts in all respective batches and their common due date. There are two proposed algorithms to solve the problems. The first is developed by using the Integer Composition method, and it produces an optimal solution. Since the problems can be solved by the first algorithm in a worst-case time complexity O(n2 n-1 ), this research proposes the second algorithm. It is a heuristic algorithm based on the Lagrange Relaxation method. Numerical experiments show that the heuristic algorithm gives outstanding results.

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Penelitian ini bertujuan merancang sistem prediksi churn pelanggan yang memanfaatkan proses data mining. Sistem yang dihasilkan dapat melakukan integrasi data, pembersihan data, transformasi data, sampling dan pemisahan data, konstruksi model prediksi, memprediksi churn pelanggan dan menampilkan hasil prediksi dalam format laporan tertentu yang diperlukan. Identifikasi variabel-variabel prediksi churn dilakukan berdasarkan model prediksi churn yang telah dikembangkan pada penelitian terdahulu yang antara lain mencakup informasi mengenai pelanggan, metode pembayaran, data percakapan, data penggunaan jenisjenis layanan telekomunikasi dan data yang menggambarkan perubahan perilaku penggunaan layanan telekomunikasi tersebut. Teknik mining yang dipilih adalah teknik klasifikasi dengan algoritma decision tree. Decision tree menghasilkan model visual yang merepresentasikan pola perilaku pelanggan yang churn dan tidak churn. Uji coba sistem yang dilakukan menggunakan data pelanggan Kartu Halo daerah Bandung menghasilkan tingkat akurasi model prediksi sebesar 70,94%. Kata kunci: customer relationship management (CRM), churn, data mining, decision tree, sistem prediksi churn.

The role of information technology (IT) in improving companies' competitiveness, including small and medium enterprises (SMEs), has been widely accepted. But, the IT investment will be in vain, if SMEs do not align their business and IT. There is a need to develop a framework of business-IT alignment. The framework development requires an understanding of the patterns of IT implementation in supporting SMEs' business (e-business initiatives). This paper presents an empirical study on e-business initiatives in Indonesian manufacturing SMEs. The study uses 41 business processes that are grouped into three business focuses: supplier side, internal side and customer side. Based on the complexity of IT support to business processes, supplier side score, internal side score, customer side score and global score can be calculated. These scores are used to develop e-business initiative groups by using cluster analysis. After validated using discriminant analysis, the cluster analysis gives five e-business initiatives. First initiative is e-business implementation in three sides of business focuses, although in the early stage. Second initiative is IT implementation with internal side focus. Third initiative is ebusiness with customer side focus. The fourth is initiative that focuses on internal and customer side. The last is e-business initiative that balances and extends IT implementation on the three sides of business focuses. Then, this paper explains the differences between e-business initiatives.