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All Journal SISFOTENIKA Jurnal Sisfokom (Sistem Informasi dan Komputer)
Eza Budi Perkasa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Kohonen Network Modeling for Asteroid Name Recognition Eza Budi Perkasa; Benny Wijaya; Jerry Jamhari
SISFOTENIKA Vol 10, No 2 (2020): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.952 KB) | DOI: 10.30700/jst.v10i2.512

Abstract

A small number of asteroids have already had their own permanent names. However, tracking the names of asteroids that have been used before is an impractical work because there are thousands of names that must be traced one by one. This research intends to minimize the search burden of many asteroid name data using the Kohonen network. By using the Kohonen network, it is sufficient to do training on the sample data provided which is far less than the actual data. The result of this training is then used to obtain the number of asteroid names that are successfully identified by the Kohonen network. The result can also be used to propose a new asteroid name so that thestatus of acceptance of the proposed name can be determined. Based on the results of the training on the sample data, the training result is getting better as the learning rate increases. However, when tested with real data, the overall result that is not satisfactory is obtained because the level of recognition is only 49.78%. From the test result, it is also found that there is no linear relationship between the level of learning rate and the number of names that were successfully identified. Further research that can be done are the inclusion of non-asteroid training data, changing Kohonen network parameters, or using other recognition methods.
Analisis Efisiensi Algoritma Alpha Beta Pruning dan MTD(f) pada Connect4 Lukas Tommy; Eza Budi Perkasa
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 5, No 2 (2016): September
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.284 KB) | DOI: 10.32736/sisfokom.v5i2.190

Abstract

Komputer membutuhkan kecerdasan buatan/artificial intelligence agar dapat bermain selayaknya manusia pada Connect Four/Connect4. Terdapat beberapa algoritma yang dapat diterapkan pada Connect4, namun tidak diketahui mana yang cocok. Algoritma yang cocok berarti optimal dalam memilih langkah sekaligus waktu eksekusinya tidak lambat pada kedalaman pencarian/depth yang cukup dalam. Pada penelitian ini, akan dilakukan analisis dan perbandingan antara alpha beta (AB) Pruning dan MTD(f) pada prototipe Connect4, dalam hal keoptimalan (persentase kemenangan) dan kecepatan (waktu eksekusi dan jumlah simpul daun). Pengujian dilakukan dengan menjalankan mode komputer melawan komputer dengan kondisi berbeda. Persentase yang diraih MTD(f) berdasarkan pengujian adalah menang 41,67%, kalah 41,67% dan seri 16,66%. Pada pengujian dengan depth 8, waktu eksekusi MTD(f) 35,19% lebih cepat dan mengevaluasi simpul daun 66,2% lebih sedikit dibandingkan AB Pruning. Hasil dari penelitian ini adalah MTD(f) sama optimalnya dengan AB Pruning pada prototipe Connect4, namun MTD(f) secara rata-rata lebih cepat dan mengevaluasi simpul daun lebih sedikit dibandingkan AB Pruning. Waktu eksekusi MTD(f) tidak lambat dan jauh lebih cepat dibandingkan AB Pruning pada depth yang cukup dalam.
Co-Authors Benny Wijaya Jerry Jamhari Lukas Tommy