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"To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed.Minimum, average, and maximum values of fragrance data acquisition are used to form triangular fuzzy numbers. THen, the fuzzy similarity measure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of hidden units. In experiments, performances of the proposed method is compared with the conventional self-organized Network inspired by Immune Algorithm (SONIA) and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9%. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-15%."

"An automatic construction of neurons in neural network inspired by immune algorithm is proposed. The new network is combined with the contiguity-constrained method to perform clustering analysis. The applicability of this technique is tested with two widely reference machine-learning cases. The experiment shows that the new technique achieved 99.33% and 100% correctness for Iris plant data and wine recognition data respectively, better than other popular clustering methods."

"The purpose of the study was to investigate hand gesture recognition. The hand gestures of American Sign Language are divided into three categories—namely, fingers gripped, fingers facing upward, and fingers facing sideways—using the adaptive network-based fuzzy inference system. The goal of the classification was to speed up the recognition process, since the process of recognizing the hand gesture takes a longer time. All pictures in all of the categories were recognized using K-nearest neighbor. The procedure involved taking real-time pictures without any gloves or censors. The findings of the study show that the best accuracy was obtained when the epochs score was 10. The proposed approach will result in more effective recognition in a short amount of time."

"Penelitian ini bertujuan untuk mengimplementasikan similarity based biclustering SBB dalam memperoleh bicluster sekumpulan gen dengan ekspresi yang similar di bawah kondisi tertentu yang signifikan pada data microarray. Secara teoritis similarity based biclustering terdiri atas tiga tahap utama, yaitu: membangun matriks similaritas baris gen dan matriks similaritas kolom kondisi , mempartisi masing-masing matriks similaritas dengan hard clustering khususnya dalam penelitian ini menggunakan partisi k-means, dan ekstrak bicluster. Sebelum mengimplementasikan metode SBB, strategi seleksi gen diterapkan dan selanjutnya dilakukan normalisasi. Perolehan evaluasi indeks silhouette pada dataset diabetic nephropathy, diabetic retinopathy dan lymphoma berturut-turut pada cluster kondisi yaitu 0,8304; 0,7853 dan 0,7382, sedangkan indeks silhouette untuk cluster gen yaitu 0,5382; 0,5408 dan 0,5464. Dan dari hasil analisis cluster kondisi, akurasi dari dataset diabetic nephropathy dan diabetic retinopathy yaitu 100 , sedangkan dataset lymphoma yaitu 98 . Selanjutnya dapat diketahui regulasi proses seluler yang terjadi pada bicluster dari ketiga dataset. Hasil analisis menunjukkan bahwa gen-gen yang diperoleh dari bicluster sesuai dengan fungsi gen dan proses biologis didukung oleh GO enrichment sehingga menjadi potensi yang besar bagi praktisi medis dalam tindak lanjut suatu penyakit yang diderita oleh pasien.

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This study aims to implement similarity based biclustering SBB in obtaining a bicluster a set of genes that exhibit similar levels of gene expression under certain conditions that is significant in microarray data. Theoretically, similarity based biclustering consists of three main phase constructing the row gene similarity matrix and the column condition similarity matrix, partitioning each matrix similarity with hard clustering especially in this research using k means partition, and extracting bicluster. Before implementing the SBB method, the gene selection strategy is applied and subsequently normalized. The acquisition of silhouette index evaluation in diabetic nephropathy, diabetic retinopathy, and lymphoma on cluster condition respectively is 0.8304, 0.7853 and 0.7382, while the silhouette index for the gene cluster is 0.5382, 0.5408 and 0.5464. In addition, according to the cluster condition analysis, accuracy of dataset diabetic nephropathy and diabetic retinopathy is 100 , whereas dataset lymphoma is 98 . Furthermore, it can be known cellular regulation that occurs on the bicluster of the three datasets. The results of the analysis show that the genes obtained from bicluster are relevant to the function of genes and biological processes supported by GO enrichment , therefore it becomes a great potential for medical practitioners in the follow up of a disease suffered by the patient."

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Permintaan konsumen pada produk pengharum ruangan kini semakin meningkat terutama di kota besar seperti Jakarta. Pengharum ruangan tidak hanya digunakan di gedung-gedung tinggi seperti perkantoran dan rumah sakit, pengharum ruangan juga digunakan di rumah-rumah. Terdapat berbagai jenis aroma pengharum ruangan yang dijual di pasar. Konsumen tentunya memiliki selera tersendiri pada aroma pengharum ruangan. Penelitian ini dilakukan untuk menguji tiga produk pengharum ruangan yang diproduksi oleh Perusahaan K. Tiga produk yang diujikan masing-masing beraroma inspiring nature (XXX), green tea (YYY) dan cheerful blossom (ZZZ). Tujuan dari penelitian ini yaitu mengidentifikasi tingkat wangi dan tingkat segar pada tiga jenis aroma produk pengharum ruangan yang berbeda, kesukaan serta minat beli konsumen pada produk XXX, YYY dan ZZZ, dan profil minat beli konsumen pada setiap produk berdasarkan kewangian, kesegaran, usia dan status sosial ekonomi. Data yang digunakan adalah data yang diperoleh dari salah satu perusahaan marketing research (Pixel Research). Sebanyak 100 responden pengguna pengharum ruangan berpartisipasi dalam penelitian. Responden dipilih dengan metode purposive sampling. Metode analisis data meliputi analisis korespondensi, uji independensi Chi-square atau crosstab, dan classification and regression tree (CRT). Diperoleh hasil bahwa produk XXX cenderung memiliki wangi lemah dan segar lemah, produk YYY memiliki wangi netral dan produk ZZZ memiliki wangi kuat dan segar kuat. Terdapat hubungan antara kesukaan serta minat beli konsumen dengan produk, konsumen cenderung mempunyai kesukaan tinggi dan minat beli tinggi pada produk YYY dan ZZZ sedangkan konsumen mempunyai kesukaan sedang dan minat beli rendah pada produk XXX. Selain itu, didapatkan bahwa variabel yang paling mempengaruhi minat beli konsumen pada produk XXX secara berturut-turut yaitu kewangian, kesegaran, dan usia konsumen sedangkan variabel yang mempengaruhi minat beli konsumen pada produk YYY dan ZZZ adalah kesegaran.

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Consumer's demand of room fragrance has increased nowadays especially in big cities, like Jakarta. Room fragrance is not only used in tall buildings, like office and hospital, but also in resident houses. There are many kinds of room fragrance aroma which is being sold in the market. Consumers have their own preference of fragrance aroma. This research is done to test three room fragrance products of Company K. These three products have different aroma, which are inspiring nature (XXX), green tea (YYY), and cheerful blossom (ZZZ). Main goals of this research are to identify fragrancy and freshness level of the three products, consumer's liking and purchase intention of products XXX, YYY, and ZZZ, and finally to profile consumer's purchase intention of every product based on fragrancy, freshness, age, and socioeconomic status. Data used in this research is gathered from one of market research company, Pixel Research. A hundred of fragrance product consumers participated in the research. Respondents were chosen by purposive sampling method. Data analysis methods, which are used in this research, include correspondence analysis, chi-square independence test (crosstab), and lastly classification and regression tree (CRT). Results implied are that XXX tends to have weak fragrancy and freshness, YYY has neutral fragrancy, and ZZZ has strong fragrancy and freshness. There is a relation between liking and purchase intention, consumers tend to have high liking and purchase intention of YYY and ZZZ, while they have average liking and low purchase intention. Furthermore, it is concluded that the most influential variables to purchase intention of XXX are consecutively fragrancy, freshness, and age, while variable which influence purchase intention of YYY and ZZZ is freshness.

 

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"Backpropagation neural network backpropagation adalah salah satu algoritma machine learning yang dapat digunakan untuk melakukan klasifikasi data. Klasifikasi data dilakukan dalan serangkaian proses training dan testing. Pada akhir proses testing yang juga merupakan akhir dari proses backpropagation, akan didapatkan nilai recognition rate. Nilai recognition rate merupakan nilai yang menandakan banyaknya data yang berhasil diklasifikasi dengan benar pada proses testing terhadap seluruh testing dataset. Recognition rate erat kaitannya dengan masalah underfitting, overfitting, local minima, dan local maxima. Keempat masalah ini menyebabkan nilai recognition rate yang didapatkan kurang optimal. Namun biasanya untuk menangani keempat masalah ini dilakukan pengaturan pada beberapa paramter, misalnya learning rate, momentum, jumlah layer, jumlah nodes, weights, dan lain-lain. Pada tulisan ini akan dijelaskan program optimasi yang melakukan pengaturan pada nilai inisialisasi weights untuk menangani keempat tersebut. Program ini melakukan inisialisasi weights menggunakan genetic algorithm pada backpropagation yang mengimplementasikan k-fold crossvalidation. Untuk menguji dan membandingkan program optimasi terhadap program implementasi backpropagation yang tidak dioptimasi program non-optimasi, digunakan empat dataset, yaitu iris flower dataset, seeds dataset, wine dataset, dan EEG dataset buatan. Pada akhir pengujian didapatkan hasil bahwa program optimasi berhasil mendapatkan nilai recognition rate lebih tinggi pada iris flower dataset, yaitu 97.33 pada program optimasi dan 96.67 pada program non-optimasi. Kemudian didapatkan pula nilai recognition rate yang lebih tinggi pada seeds dataset, yaitu 93.33 pada program optimasi dan 92.86 pada program non-optimasi. Lalu didapatkan pula nilai recognition rate yang lebih tinggi pada EEG dataset buatan, yaitu 37.5 pada program optimasi dan 35.94 pada program non-optimasi. Sedangkan pada wine dataset didapatkan nilai recognition rate yang sama antara program optimasi dan program non-optimasi, yaitu 99.44.

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Backpropagation neural network backpropagation is one of machine learning algorithms that can be used to classify data. The data classification is done in a series of trainig and testing processes. At the end of testing process that is also the end of backpropagation process, the algorithm will produce recognition rate value. Recognition rate value indicates the total of correctly classified data in testing process againts all data in testing dataset. Recognition rate value related to underfitting, overfitting, local minima, and local maxima problems. However, to handle these problems adjusting some parameters are necessary to be done. These parameters are learning rate, momentum, number of layers, number of nodes, weights, etc. In this writting will be explained an optimization program that adjusts the initialization values of weights to handle those four problems. This program initializes weights using genetic algorithm on backpropagation implementing k fold crossvalidation. To test and compare the optimization program with a program that implements backpropagation without optimization non optimzation program four datasets will be used, those are iris flower dataset, seeds dataset, wine dataset, and artificial EEG dataset. At the end of the test, the results show that optimization program obtained higher recognition rate value on iris flower dataset, that is 97.33 on optimization program againts 96.67 on non optimization program. Other than that, optimization program obtained higher recognition rate value on seeds dataset, that is 93.33 on optimization program againts 92.86 on non optimization program. Also, optimization program obtained higher recognition rate value on artificial EEG dataset, that is 37.5 on optimization program againts 35.94 on non optimization program. However, the optimization program obtained an equal recognition rate value on wine dataset, that is 99.44."

"[Dewasa ini, teknologi berkembang dengan sangat pesat, salah satu contoh teknologi yang sedang marak beberapa tahun belakangan ini adalah 3D face recognition. Teknologi ini menggabungkan data biometrik berupa wajah orang yang diambil dari beberapa sudut (horizontal dan vertikal) dan jaringan saraf tiruan. Untuk memperbaiki tingkat rekognisi yang rendah pada saat menggunakan data crisp, maka digunakanlah metode fuzzy. Percobaan akan dilakukan sebanyak tiga kali karena terdapat tiga cluster yang masing-masing cluster terdiri dari beberapa set orang. Pertama-tama, data akan diolah secara bertahap pada fase fuzzification dimulai dari parameter ekspresi, orang, dan sudut. Tahapan selanjutnya adalah membuat referensi pada fase fuzzy manifold untuk kemudian digunakan pada fase fuzzy nearest distance. Pada fase fuzzy nearest distance akan dicari jarak terpendek dari data testing dengan referensi yang sudah ada. Hasil keluaran dari sistem ini adalah kombinasi sudut horizontal dan vertikal dari tiap-tiap cluster yang nantinya akan dimasukkan kedalam Jaringan Saraf Tiruan (JST) dengan lapis tersembunyi berstruktur hemisfer untuk mendapatkan tingkat rekognisi. Secara keseluruhan rata-rata tingkat rekognisi setiap cluster sudah bisa mencapai 80%. Hal ini menunjukkan sistem sudah cukup optimal dalam mengenali pola wajah yang ada. ;The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. ;The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. , The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. ]"

"Pengenalan wajah telah menjadi topik pengolahan citra yang banyak mengalami perkembangan. Pengembangan yang dilakukan bertujuan mengatasi kesulitan-kesulitan dalam pengenalan wajah, diantaranya pose pengambilan gambar. Penelitian ini bertujuan membuat rancang bangun rekognisi wajah tiga dimensi dengan sistem Fuzzy dan Jaringan Saraf Tiruan Hemisfer untuk mengatasi masalah tsersebut. Sistem fuzzy bertujuan untuk mengestimasi sudut pengambilan gambar dengan menempatkan informasi gambar ke suatu titik di ruang vektor fuzzy atau manifold dari data referensi menggunakan jarak terdekat fuzzy. Informasi sudut akan diteruskan ke jaringan saraf tiruan yang mengenali wajah-wajah dengan cara mempelajari wajah-wajah yang disediakan untuk pembelajaran. Informasi gambar yang dimasukkan ke dalam jaringan saraf tiruan terlebih dahulu dikompresi dengan metode Principle Component Analysis (PCA). Keunggulan jaringan saraf tiruan hemisfer dalam pengenalan wajah tiga dimensi adalah adanya faktor pengali neuron yang besarnya bergantung dari informasi sudut pengambilan gambar, sehingga gambar dua dimensi dapat diproyeksikan ke ruang tiga dimensi. Metode pembelajaran yang digunakan pada tulisan ini ialah pengembangan dari metode backpropagation. Penelitian diawali dengan pengambilan data dari alat pengambil gambar wajah tiga dimensi, perancangan sistem fuzzy dan jaringan saraf tiruan dalam MATLAB, dan validasi masing-masing sistem dengan data yang diambil. Sistem ini kemudian dikombinasikan dalam perangkat lunak MATLAB dan diuji dengan sebuah prototipe yang terdiri atas satu kamera. Hasil penelitian menunjukkan tingkat rekognisi sistem sebesar 76,29% pada saat validasi dan 37% saat aplikasi sistem satu kamera. Dari penelitian ini dibuktikan sistem dapat diaplikasikan untuk merekognisi wajah tiga dimensi, namun harus diperhatikan keakuratan pemotongan gambar untuk mendapat hasil yang akurat.

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Face recognition is currently a highly discussed topic on image processing. The developments are aimed to overcome problems on recognizing face, such as various pose of image object. The study tries to solve the problem by creating a system design of 3D face recognition using a fuzzy system and Hemispheric Structure Hidden Layer of Artificial Neural Network to overcome the problem. The fuzzy system estimates pose information of the object taken. It is done by mapping the image taken to a point in a fuzzy vector space or manifold using fuzzy nearest distance. Pose information is then projected to the artificial neural network which is able to recognize faces after formerly learned a set of learning database. The data submitted to the artificial neural network is compressed by Principle Component Analysis (PCA). Main advantage of hemispheric neural network on 3D face recognition is the multiplying factor which values depend on the image pose information, so that the two dimensional images can be projected into three dimensional space. Learning method used in this study is an expansion of backpropagation. The study begins by taking experimental data from 3D face capturing devices, developing fuzzy system and artificial neural network in MATLAB, and validating both systems. The system is then combined in MATLAB and tested by a single unit camera prototype. Results show the system able to reach recognition rate of 76.29% on validation and 37% on single unit camera application. The study proves that the system is applicable for a 3D face recognition system, however the accuracy of image cropping should be taken into consideration for an accurate result."