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Abstrak :
Todays situations force telecomunication company to continuously evaluate, select and prioritize its product portfolio in order to determine the strategically important products. An analysis of critical success factors that determine product’s strategic importance acts as a good introduction to review and manage the product portfolio as well as to seek ways to develop the product. To achieve this, the company is required to have good alliances and collaborations between departments, develop a clear focus and seek innovative ways of doing business, particularly between marketing and product divisions. This study analyzed the alignment between product and marketing divisions of a telecommunication company in Indonesia, in determining the company’s strategic products. The results indicated that both divisions agreed on using financial performance and revenue as the most important criteria and sub-criteria for identifying strategic product. However, the divisions also faced some different visions in selecting alternative criteria. The Product divisions were more focused on technical spesification of product, while the Marketing division were more oriented on customer and market conditions. The study reported the consequences of these difference in practice.

Abstrak :
Dalam makalah ini dipresentasikan Adaptive Neuro Fuzzy Inference Systems (ANFIS) yang digunakan pada konverter dc-dc paralel non identik. Skema pengendalian master-slave dimanfaatkan untuk distribusi arus pada setiap konverter yang disusun paralel, sebagai referensi arus adalah arus keluaran konverter pertama dikurangi dengan arus keluaran konvereter ke dua dan ketiga, sehingga di hasilkan Error 1= I1-I2 dan Error 2= I1-I3 kemudian error tersebut di gunakan sebagai masukan kontroller, konsep inilah yang diusulkan dalam makalah ini. Tujuan lain dalam makalah ini penerapan ANFIS, pengembangan dan pembuktian secara matematika dan keuntungannya melalui perbandingan hasil simulasi, sehingga dihasilkan Root Mean Square Error (RMSE) sekecil mungkin. Struktur ANFIS yang digunakan sebanyak 45 parameter yang dibagi menjadi dua yaitu 27 parameter linier dan 18 parameter non linier dan 9 rules. Jumlah data sebanyak 30005. Hasil simulasi menunjukkan RMSE trn=0.000465492, RMSE chk= 0.0202974 dengan pengulangan 30 kali dan pada pengulangan 15 kali RMSE trn=0.000484379, RMSE chk= 0.020331. Hasil ini menunjukkan terjadi perbaikan sistem dalam keseimbangan distribusi arus yang diberikan oleh masing-masing konverter yang memiliki parameter tidak sama. ......In this paper is presented an Adaptive Neuro Fuzzy Inference Systems (ANFIS) for parallel connected dc-dc converters non identic. Scheme controlling the master-slave be used for the current distribution at each parallel converter, where as reference is the first converter current output lessened with the second converter current output and third converter current output, become the error 1 yielded from I 1-I2 and error 2 from I1-I3, then used as input controller, this is model proposed in the article. The objective of the paper is to introduce an ANFIS application, develop the associated mathematical theory and prove the concept and its advantages through comparative simulation with its existing, so that reached the RMSE as minimum as possible. Structure anfis use 45 parameter which is divided become 27 linear parameter, 18 parameter non linear and also 9 rules. Amount of data is 30005 pairs. Result of simulation of rmse trn = 0.000465492, rmse chk= 0.0202974 at 30 epochs and at 15 epochs mrse trn=0.000484379, rmse chk= 0.020331. This matter show there is system repair in well-balanced current distribution though converter used unegual of its parameter.

Abstrak :
E-learning content being a barrier for e-learning is no longer true on today?s Internet. The current concerns are how to effectively annotate and organize the available content (both textual and non-textual) to facilitate effective sharing, reusability and customization. In this paper, we explain a component-oriented approach to organize content in an ontology. We also illustrate our 3-tier e-learning content management architecture and relevant interfaces. We use a simple yet intuitive example to successfully demonstrate the current working prototype which is capable of compiling personalized course materials. The e-learning system explained here uses the said ontology.