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[ABSTRAK
Beberapa penelitian telah menunjukkan bahwa bakteri mampu melakukan pemesinan pada logam, salah satunya adalah Acidithiobacillus ferrooxidans. Keuntungan utama menggunakan bakteri untuk proses pemesinan adalah efisiensi energi yang digunakan. Penelitian sebelumnya telah membuktikan kemampuan Acidithiobacillus ferrooxidans dalam melakukan pemesinan termasuk karakterisasi pelepasan material dan hasil akhir pada benda kerja. Namun tidak satupun dari penelitian tersebut yang meneliti kemungkinan dari bakteri tersebut melakukan pemesinan multi-axis. Dalam penelitian ini akan dibahas mengenai kemungkinan dari Acidithiobacillus ferrooxidans dalam melakukan pemesinan multi axis dengan menggunakan total 15 buah sampel benda kerja. Beberapa benda kerja tersebut diletakkan dalam cairan medium kultur dengan diberikan sudut inklinasi 450 dengan menggunakan inklinator untuk membandingkan hasil pemesinan dengan benda kerja yang tidak diberi inklinasi. Hasil dari mikrografi SEM menunjukkan bahwa benda kerja yang diberi inklinasi memiliki kedalaman pelepasan material dan profil potongan yang berbeda dengan benda kerja yang tidak diberi inklinasi. Benda kerja yang diberi inklinasi memiliki perbedaan kedalaman pelepasan material sebesar 45% lebih banyak pada sisi yang lebih tinggi. Dengan adanya perbedaan karakteritik pemesinan, diharapkan dapat dijadikan acuan untuk pengembangan proses Biomachining multi-axis lebih lanjut. ABSTRAK
Recent studies show that some bacteria have the ability to do machining process, and one of them is Acidithiobacillus ferrooxidans. The main purpose of using bacteria to do the machining process is the efficiency of energy used. Previous studies have already investigate the capability of Acidithiobacillus ferrooxidans to do the machining including the characterization of the material removed and surface finishing of the workpiece. However, none of them investigate the possibility for the bacteria to do the multi-axis machining. In this research, the capability of Acidithiobacillus ferrooxidans to do the machining process was investigated. A total of 15 workpieces were used, and placed in the cultured medium with different conditions. Some of the workpieces were placed without inclination angle while some of them were placed with 450 of inclination angle. The SEM micrograph result showed that there were differences in the cutting depth and cutting profile of the workpieces which were inclined and not inclined. The higher sides have 45% more depth of material removed. According to these result, there is a possibility it might led to the further development of multi-axis Biomachining., Recent studies show that some bacteria have the ability to do machining process, and one of them is Acidithiobacillus ferrooxidans. The main purpose of using bacteria to do the machining process is the efficiency of energy used. Previous studies have already investigate the capability of Acidithiobacillus ferrooxidans to do the machining including the characterization of the material removed and surface finishing of the workpiece. However, none of them investigate the possibility for the bacteria to do the multi-axis machining. In this research, the capability of Acidithiobacillus ferrooxidans to do the machining process was investigated. A total of 15 workpieces were used, and placed in the cultured medium with different conditions. Some of the workpieces were placed without inclination angle while some of them were placed with 450 of inclination angle. The SEM micrograph result showed that there were differences in the cutting depth and cutting profile of the workpieces which were inclined and not inclined. The higher sides have 45% more depth of material removed. According to these result, there is a possibility it might led to the further development of multi-axis Biomachining.]

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We have been successfully synthesized YAG :Ce3+nanoparticle (60 nm in sized) having High crystalinity by using low tenperature sol gel mwthod.....

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Retail sores or departement stores have hundreds sales transaction everyday,transactions usually are made by men,which are calculating and recording many of invoices number.....

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Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature transformation (UFT) methods and joint mutual information maximation (JMIM) methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM) algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

Dataset dengan fitur heterogen dapat mempengaruhi hasil seleksi fitur yang tidak tepat karena sulit untuk mengevaluasi fitur heterogen secara bersamaan. Transformasi fitur adalah cara untuk mengatasi seleksi subset fitur yang heterogen. Hasil transformasi fitur non-numerik menjadi numerik mungkin menghasilkan redundansi terhadap fitur numerik original. Dalam tulisan ini, peneliti mengusulkan sebuah metode untuk seleksi subset fitur berdasarkan mutual information (MI) untuk klasifikasi fitur heterogen. Peneliti menggunakan metode unsupervised feature transformation (UFT) dan metode joint mutual information maximation (JMIM). Metode UFT digunakan untuk transformasi fitur non-numerik menjadi fitur numerik. Metode JMIM digunakan untuk seleksi subset fitur dengan pertimbangan label kelas. Fitur hasil transformasi dan fitur original disatukan seluruhnya, kemudian menentukan subset fitur menggunakan metode JMIM, dan melakukan klasifikasi terhadap subset fitur tersebut menggunakan algoritma support vector machine (SVM). Akurasi klasifikasi diukur untuk sejumlah subset fitur terpilih dan dibandingkan antara metode UFT-JMIM dan Dummy-JMIM. Akurasi klasifikasi rata-rata dari keseluruhan percobaan yang dapat dicapai oleh metode UFT-JMIM sekitar 84.47% dan metode Dummy-JMIM sekitar 84.24%. Hasil ini menunjukkan bahwa metode UFT-JMIM dapat meminimalkan informasi yang hilang diantara fitur hasil transformasi dan fitur original, dan menyeleksi subset fitur untuk menghindari fitur redundansi dan tidak relevan

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A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on inform-ation- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.

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Keberhasilan pemahaman tentang bagaimana membuat komputer belajar akan membuka banyak manfaat baru dari komputer. Sebuah pemahaman yang rinci tentang algoritma pengolahan informasi untuk pembelajaran mesin dapat membuat pemahaman yang sebaik kemampuan belajar manusia. Banyak jenis pembelajaran mesin yang kita tahu, beberapa diantaranya adalah Backpropagation (BP), Extreme Learning Machine (ELM), dan Support Vector Machine (SVM). Penelitian ini menggunakan lima data yang memiliki beberapa karakteristik. Hasil penelitian ini, dari ketiga model yang diamati memberikan akurasi klasifikasi yang sebanding. Penelitian ini memiliki tiga kesimpulan, yang terbaik dalam akurasi adalah BP, yang terbaik dalam stabilitas adalah SVM dan CPU time terbaik adalah ELM untuk data bioinformatika.

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Paper ini memaparkan perancangan pengendali robot berbasis perilaku menggunakan Fuzzy, di mana parameter Fuzzy ditala secara otomatis menggunakan Particle Swarm Optimization (PSO) yang diistilahkan dengan Particle Swarm Fuzzy Controller (PSFC). Suatu fungsi tertentu dirancang untuk meningkatkan performa proses pencarian PSO. Fungsi tersebut mengubah harga bobot inersia menjadi berkurang secara sigmoid (Sigmoid Decreasing Inertia Weight). Empat buah perilaku robot dirancang menggunakan PSFC. Kemudian seluruh perilaku tersebut juga dikoordinasikan menggunakan PSFC. Beberapa simulasi pengendalian pergerakan robot dan percobaan dengan robot MagellanPro telah dilakukan untuk menguji performa algoritma yang dirancang. Algoritma lain, Genetic Fuzzy Controller (GFC) digunakan sebagai pembanding. Dari hasil pengujian dapat dikatakan bahwa pengendali yang dirancang memiliki kemampuan yang baik untuk menyelesaikan tugasnya pada suatu lingkungan nyata.

This paper describes the design of robots controllers based on behaviour using Fuzzy, in which the Fuzzy parameters are automatically tuned using the Particle Swarm Optimization (PSO) which is termed the Particle Swarm Fuzzy Controller (PSFC). A particular function is designed to improve the performance of PSO search process. That particular function changes the value of the inertia weight, so it‟s decreased in sigmoid (Sigmoid Decreasing Inertia Weight). Four types of robots behaviour are designed and coordinated using the PSFC. Some simulation of the robot movement control and experiments with the robot MagellanPro have been conducted to test the performance of the algorithm that have been designed. Another algorithm, Genetic Fuzzy Controller (GFC) is used as a comparison. From the test results, it can be said that the controllers that have been designed have a good ability to accomplish its task in a real environment.

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Perangkat electronic data capture (EDC) merupakan salah satu perangkat pembayaran yang menerima alat pembayaran berbasis kartu. Dalam komunikasinya dengan host bank, biasanya EDC menggunakan jaringan seluler dan kabel. Jaringan seluler lebih populer untuk diterapkan untuk perangkat EDC dikarenakan fleksibilitas dan kemudahan instalasi. Koneksi yang buruk dan tidak kompatibel dengan jaringan 4G dan 5G menjadi beberapa masalah dalam perangkat ini. Tesis ini membandingkan quality of service dan tekno-ekonomi pada jaringan seluler eksisting dan M2M pada koneksi 2G/3G menggunakan data quality index dan cost benefit analysis sebagai metode penelitian. Metode data quality index menunjukkan data kinerja aktual tergantung pada kemampuan perangkat EDC, sedangkan pada metode cost benefit analysis menampilkan kelayakan investasi atas kedua jaringan ini. Hasil penelitian memberikan gambaran kepada pengguna atas QoS dan kelayakan investasi jaringan M2M yang akan diimplementasikan di perangkat EDC mereka. Hasil penelitian menunjukkan bahwa jaringan M2M memiliki QoS yang lebih baik dengan 99,03% dibandingkan jaringan legacy dengan 96,05%. Jaringan M2M juga mendapatkan tingkat keberhasilan transaksi 98,43%, sedangkan jaringan legacy hanya mendapatkan tingkat keberhasilan transaksi 95,82%. Pada kelayakan investasi jaringan legacy memiliki nilai CBR dan PBP yang lebih baik dibandingkan jaringan M2M, hasil ini dipengaruhi oleh besarnya OPEX dari jaringan M2M meskipun nilai pendapatannya lebih tinggi. Pada investasi rekomendasi ditetapkan target net income sebesar minimal 99% dari NPV terbaik. Pada perhitungannya, didapatkan perbandingan jumlah EDC 43.42% pada jaringan legacy dan 56.58% pada jaringan M2M. Dari investasi rekomendasi ini, didapatkan nilai NPV investasi Rp. 308,625,530,678,484, CBR senilai 247.55 dan PBP 0.021 tahun. ......Electronic data capture (EDC) device is one of the payment devices that accept card-based payment instruments. To communicate with the host bank, it is common for EDC to use a mobile and wired network. Due to its flexibility and ease of installation, mobile networks are more popular to apply to EDC devices. Although, poor connection and incompatibility with 4G and 5G networks are some of the problems in these devices. This thesis compares the quality of service and techno-economics on existing cellular networks and M2M on 2G/3G connections using the data quality index and cost-benefit analysis as research methods. The data quality index method will show actual performance data depending on the capabilities of the EDC device, while the cost-benefit analysis method will display the investment feasibility of these two networks. The results of the study give users an overview of the QoS and investment feasibility of the M2M network to be implemented in their EDC devices. The results showed that cellular M2M networks have better QoS with 99.03% compared to legacy networks with 96.05%. The M2M network also got a transaction success rate of 98.43%, while the legacy network only got a transaction success rate of 95.82%. On the feasibility of investing, legacy networks have a better CBR and payback period than M2M networks. This result is affected by the OPEX of M2M network being too high even though, its revenue is higher. Recommendation investment with a net income target of at least 99% of the best NPV. In the calculation, a comparison of the number of EDCs was obtained at 43.42% on legacy networks and 56.58% on M2M networks. From this recommended investment, the NPV investment value of IDR.308,625,530,678,484 was obtained, with a cost-benefit ratio of 247.55 and a payback period of 0.021 years.