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Abstrak :
[ABSTRAK
Dalam mengemudikan mobil, pengemudi harus membagi konsentrasi antara mengemudi dan mengamati ikon dalam mobil, untuk itu diperlukan ikon yang dapat dikenali dengan baik untuk mengurangi beban mental dan waktu operasi pengemudi mobil. Penelitian ini mengembangkan metode untuk mengidentifikasi penyebab ikon tidak dapat dikenali dengan baik, sehingga dapat menjadi masukan untuk pengembangan ikon baru. 34 ikon yang tidak dapat dikenali dengan baik dengan recognition rate dibawah 80% dalam penelitian sebelumnya oleh C.-F. Chi and Dewi (2014) diuji kembali kepada 14 pengemudi pengalaman melalui wawancara yang dilakukan oleh Hsieh (2014). Pendapat mengenai ikon mobil yang tidak dapat dikenali dengan baik disurvey berdasarakan 3 tahapan pemahaman ikon (Campbell et al, 2004) dan 3 aspek memahami objek alphanumerical (Sanders & McCormick, 1993), yang kemudian digunakan untuk mencari kemungkinan penyebab tidak dikenalinya ikon-ikon ini. Pertanyaan dalam wawancara meliputi apakah ikon ini dapat dilihat, familier, bermakna, menarik dan apabila ada saran untuk desain alternative. Semua pertanyaan akan dibagi menjadi pertanyaan ya/tidak untuk aturan pembuatan keputusan. Sebuah tabel pembuatan keputusan digunakan untuk mengorganisir aturan keputusan sesuai dengan 7 klasifikasi ikon oleh C.-F. Chi and Dewi (2014), dan aturan ini dipastikan sesuai dengan logika dan mutually exclusive (Chi, Tseng, & Jang, 2012). Dengan menggabungkan ikon yang diuji dengan aturan keputusan, tabel keputusan dapat diubah menjadi pohon keputusan untuk mengilustrasi dan memfasilitasi perbaikan desain dari ikon-ikon yang tidak dikenali ini. Ikon-ikon baru dibuat untuk menggantikan ikon-ikon yang tidak dikenali untuk membuktikan bahwa pohon keputusan merupakan sebuah metode efektif untuk evaluasi dan desain ulang.

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ABSTRACT
A comprehensible icon can reduce mental load and operation time for the driver to time share between driving and icon recognition. This study developed a diagnostic tool to identify the causes of poorly recognised icons that could be used for the redesign of existing icons. Thirty-four poorly recognized icons were selected for the current experiment because they had a below 80% recognition rate by experienced drivers in a previous study (C.-F. Chi and Dewi (2014). Fourteen experienced drivers participated in the experiment conducted by Hsieh (2014), where each participant was asked to review all poorly recognized icons one by one based on three stages of icon comprehension (Campbell et al, 2004) and the three aspects of understanding alphanumerical objects (Sanders & McCormick, 1993) to explore possible causes for poor recognition of these icons. Specific questions include whether each icon is visible, familiar, meaningful, and attractive, and if the participants have any suggestion for a better alternative design. All the answers can be further divided into more specific Yes/No decision rules. A decision table is used to organize all the decision rules based on seven categories of icon design, and to ensure these decision rules are logical and mutually exclusive (Chi, Tseng, & Jang, 2012). By associating all the tested icons with the decision rules, the decision table can be transformed into a decision tree illustration to facilitate the redesign of these poorly recognized icons A new set of redesigned icons would be created to replace all the poorly recognized icons to prove that the decision tree is a very effective diagnostic tool for icon evaluation and redesign., A comprehensible icon can reduce mental load and operation time for the driver to time share between driving and icon recognition. This study developed a diagnostic tool to identify the causes of poorly recognised icons that could be used for the redesign of existing icons. Thirty-four poorly recognized icons were selected for the current experiment because they had a below 80% recognition rate by experienced drivers in a previous study (C.-F. Chi and Dewi (2014). Fourteen experienced drivers participated in the experiment conducted by Hsieh (2014), where each participant was asked to review all poorly recognized icons one by one based on three stages of icon comprehension (Campbell et al, 2004) and the three aspects of understanding alphanumerical objects (Sanders & McCormick, 1993) to explore possible causes for poor recognition of these icons. Specific questions include whether each icon is visible, familiar, meaningful, and attractive, and if the participants have any suggestion for a better alternative design. All the answers can be further divided into more specific Yes/No decision rules. A decision table is used to organize all the decision rules based on seven categories of icon design, and to ensure these decision rules are logical and mutually exclusive (Chi, Tseng, & Jang, 2012). By associating all the tested icons with the decision rules, the decision table can be transformed into a decision tree illustration to facilitate the redesign of these poorly recognized icons A new set of redesigned icons would be created to replace all the poorly recognized icons to prove that the decision tree is a very effective diagnostic tool for icon evaluation and redesign.]

Abstrak :
Tambak udang putih merupakan sebuah ekosistem buatan kompleks yang membutuhkan bantuan manusia untuk mempertahankan keseimbangan elemen-elemen pembentuknya. Permasalahan di dalamnya disebabkan oleh proses ekologis baik secara biologi, kimia, maupun fisika yang saling terkait. Sehingga untuk menghasilkan solusi optimal perlu diperhitungkan bagaimana keterhubungan antar elemen. Pengetahuan mengenai hubungan elemen-elemen tersebut umumnya dikuasai oleh pakar, tetapi tidak seluruh tambak mampu menyediakan pakar dalam budidayanya. Pengembangan sistem pakar dalam penelitian ini ditujukan untuk menjawab kebutuhan pakar di tambak udang menggunakan teknik klasifikasi. Pengetahuan pakar direpresentasikan dalam decision table dengan penggunaan multi atribut. Hasil eksperimen menunjukkan bahwa sistem mampu menghasilkan solusi dari berbagai variasi masalah yang mungkin terjadi di tambak seperti pakar dan fleksibel untuk dimodifikasi.

White shrimp embankment is a complex artificial ecosystem that requires human intervention to maintain the balance of its constituent elements. The problems inside are caused by ecological processes therein, either biology, chemistry, and physics that are interlinked so as to produce the optimal solution needs to be taken into account how the connection between elements. Knowledge about the relationships among these elements is generally dominated by experts, but not all embankments are able to provide experts in the cultivation. Development of expert systems in this study aimed to answer the needs of experts in shrimp embankments using the classification technique. Expert knowledge is represented in a decision table with the use of multi attributes. The experimental results show that the system is capable of generating solutions from a variety of problems that may occur in embankments such as expert and versatile to be modified.