Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study

Control chart has been widely used for monitoring production process, especially in evaluating the quality performance of a product. An uncontrolled process is usually known by recognizing its chart pattern, and then performing some actions to overcome the problems. In high speed production process, real-time data is recorded and plotted almost automatically, and the control chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural networks for automatic control chart recognition have been studied in detecting its pattern. In the field of computer science, the performance of its automatic and fast recognition ability can be a substitution for a conventional method by human. Some researchers even have developed newer algorithm to increase the recognition process of this neural networks control chart. However, artificial approaches have some difficulties in implementation, especially due to its sophisticated programming algorithm. Another competing method, based on statistical feature also has been considered in recognition process. Control chart is related to applied statistical method, so it is not unreasonable if statistical properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In this paper, the comparison between these two methods starts by evaluating the behavior of control chart time series point, and measured for its closeness to some training data that are generated by simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured

PEMODELAN PREFERENSI PENGAJUAN KREDIT USAHA MIKRO (KUM) DI BANK X OLEH PEMILIK USAHA MIKRO DI SURABAYA DENGAN METODE CHAID

In the aim of developing and strengthening the micro, small and medium enterprises (MSMEs), the government of Indonesia provide financial support for MSMEs enterpreneur through commercial loans which is held by several national bank. In order to assure that the loan goes to the right enterpreneur, those banks need some information about the MSMEs entepreneur’s preference in applying the MSMEs loan. These preference could be predicted by exploring the enterpreneur’s characteristics. One of the quantitative method called CHAID (Chi-Squared Automatic Interaction Detection Analysis) has been used in this research to provide that prediction. With this method, the MSMEs enterpreneur’s preferences on loan application and loan products were modeled by two classification tree

FRAKSI ETIL ASETAT KULIT BATANG FALOAK (Sterculia quadrifida R.Br ) MENGINDUKSI APOPTOSIS DAN SIKLUS SEL PADA SEL KANKER PAYUDARA T47D

Faloak (Sterculia quadrifida R.Br) is one source of bioactive compounds that could be developed as chemotherapeutic agent. Empirically East Nusa Tenggara people use boiled water of faloak bark as a cure for hepatitis (types A, B, and C), and gastroenteritis. This study was performed to test anticancer activity fraction of n-hexane, ethyl acetate, ethanol, and methanol of ethanolic extract from faloak stem bark for the type of breast cancer cell line T47D, and normal cell types Vero using cytotoxic 3- (4,5-dimetilazol- 2-yl) -2,5-difeniltetrazolium bromide (MTT) test method. Ethanolic extract was subjected to column chromatography using different solvents polarity level as n-hexane, ethyl acetate, ethanol, and methanol. Testing the cytotoxic effects using the MTT assay in T47D breast cancer cells and normal Vero cells with EC50 parameter. Ethyl acetate fraction in inducing apoptosis and cell cycle modulation was observed with flowcytometry method. The test results cytotoxic fraction indicating the fraction of ethyl acetate has the lowest activity with EC50 of 24.88 ?g/mL and selectivity index of 15.58. Ethyl acetate fraction effects an accumulation of cells in S phase (27.43%) in breast cancer cells T47D which is able to induce apoptosis. These results demonstrate that the ethyl acetate fraction can be developed as a chemotherapeutic agent in improving the effectiveness of breast cancer treatment

Quantification Method for In-Vitro Tissue Culture Plants Morphology using Object Tracking and Digital Image Analysis

Manual measurement of morphology variables on in-vitro stored plants usually cause either physical damage or microorganism infection such that further monitoring of their in-vitro performance is precluded. This study adapted computer vision technology by which it is possible to conduct such measurement without physical contact or destructive test. Moreover, by applying object tracking and pattern recognition technique in the algorithm, the system could provide automatic and real time analysis. It was shown that this quantification method reach 80.2% and 87.9% in the measurement of leaf area and chlorophyll intensity. Intensity histogram and Fourier spectrum found to be the best feature for leaf recognition and interpolation usage to adjust pixel amount over the camera distance provide better estimation on leaf area

Keefektifan Neural Network dalam Memprediksi Respon Eksperimen Ortogonal Array Sebagai Alternatif Pendekatan Taguchi Klasik

Tujuan dari penelitian ini adalah untuk mengevaluasi efektifitas neural network dalam memprediksi respon eksperimen sebagaimana telah diaplikasikan oleh beberapa peneliti. Beberapa penelitian menunjukkan bahwa neural network biasanya digunakan untuk memprediksi data dengan rekaman data historis maupun cross-section yang cukup. Dalam eksperimen Taguchi, orthogonal array yang disediakan terdiri dari kombinasi level yang disusun untuk eksperimen. Kombinasi level tersebut beserta responnya dapat diperlakukan sebagai data training untuk neural network. Bagaimanapun, permasalahan muncul berkaitan dengan kekompleksan model neural network, yakni overestimate dan penentuan banyaknya hidden node dan layer. Selain neural network, pendekatan Taguchi klasik yang sederhana dapat dipertimbangkan untuk menghasilkan prediksi yang lebih efektif, dengan hanya perbedaan yang kecil meski neural network lebih baik. Penelitian ini menggunakan kasus-kasus eksperimen Taguchi yang diambil dari beberapa penelitian, dan membandingkan kedua metode tersebu

Computer Aided Simulation of DNA Fingerprint Amplified Fragment Length Polymophism (AFLP) Using Suffix Tree Indexing and Data Mining

AFLP is one of the DNA Fingerprinting techniques which have broad application as genetic marker in various fields. Begin with the DNA sequence digestion using one or more particular restriction enzyme, ligation of the adapters to the overhanging sticky ends followed by DNA fragments amplification using PCR. The PCR reaction uses primers that match the adapter sequence and have some (1 to 3) dditional “selective” bases which could be any bases, this reduces the number of bands that will be amplified. Such technique intended to increase the amplified fragments peculiarity so the polymorphism of the organism being studied could be well visualized by gel electrophoresis. The computer aided of AFLP simulation developed in this research was aimed to predict this electrophoresis result by simulate the digestion, ligation and PCR process using some pattern recognition algorithm applied to the DNA sequence from online databases. Through this simulation the researcher could determine the best combination of restriction enzyme and selective bases for their laboratory experiment. Suffix tree indexing was conducted during the exploration process of the genome sequence (in FASTA format) to find the restriction sites rapidly and create fragments of it. Data modeling enable the system draws the fragments into virtual DNA’s electrophoresis pattern. Data mining accomplish the simulation by exploring overall possible virtual DNA’s electrophoresis pattern and determine the best restriction enzyme and selective bases combination by calculating certain quantitative criteria

Tutorial Pembelajaran Interaktif Elektroforesis

Tutorial Pembelajaran Interaktif Elektroforesis adalah program computer berupa flash yang merupakan media pembelajaran interaktif yang berisi teknik dasar elektroforesis DNA. Tutorial pembelajaran ini mendukung beberapa matakuliah seperti matakuliah Analisis DNA, Biologi Molekuler dan Biokimia. Tutorial ini berisi definisi elektroforesis dan tahapan-tahapan dalam analisis elektroforesis baik dalam bentu gambar maupun video. Pengenalan proses elektroforesis dilakukan secara interaktif, di mana pengguna tutorial diajak meng-klik opsi-opsi tertentu untuk mengaktifkan alat dan melihat proses yang terjadi ketika suatu tahapan dilakukan. Animasi yang diberikan akan memudahkan pengguna memahami proses elektroforesis baik secara konseptual maupun secara praktis. Pada bagian akhir tutorial disertai dengan latihan soal untuk membantu mengevaluasi sejauh mana pemahaman terhadap materi yang ada

Tutorial Pembelajaran Interaktif Isolasi DNA

Tutorial Pembelajaran Interaktif Isolasi DNA adalah program computer berupa flash yang merupakan media pembelajaran interaktif yang berisi teknik dasar isolasi DNA. Tutorial pembelajaran ini mendukung beberapa matakuliah seperti matakuliah Analisis DNA, Biologi Molekuler dan Biokimia. Tutorial ini berisi tentang gambaran keberadaan DNA di dalam sel, sejarah penemuan DNA dan cara mengisolasinya, tahapan-tahapan dalam isolasi DNA dari berbagai sampel seperti tanaman, hewan maupun mikroorganisme yang dipaparkan dalam bentuk animasi dan video. Video-video yang diberikan akan memudahkan pengguna memahami proses isolasi DNA baik secara konseptual maupun secara praktis. Pada bagian akhir tutorial disertai dengan latihan soal untuk membantu mengevaluasi sejauh mana pemahaman terhadap materi yang ada

Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning

Implementation of Plant Image Quality Improvement based on Machine Learning on Camera Variation to Predict Photosynthetic Pigments. Pigments are natural dyes found in plants and animals. In photosynthesis, there are 3 essential pigments: chlorophyll, cartenoid, and anthocyanin. Pigment analysis can be performed with High Performance Liquid Chromatography (HPLC) and a spectrophotometer. However, HPLC and spectrophotometers require high resources and time. Thus, the Fuzzy Piction Android application built using the FP3Net model is the best choice in pigment prediction since it is low on cost and accessible. However, the Fuzzy Piction produces different performance, which is affected by light conditions and camera specifications. The experiment used ten sample images for Jasminum sp., P. betle, Syzygium oleina of green and red variations, and Graptophyllum pictum leaves with three smartphone cameras and three lighting levels. Improvements using 3D-TPS produced the best SSIM values in the range of 0.9191 – 0.9797 for images Syzygium oleina of green and red variations leaves, and the predicted MAE value of pigment was 0.0296 – 0.0492.Keywords: 3D-TPS, plant leaves, pigment, image quality improvement   Pigmen merupakan pewarna alami yang ditemukan pada tumbuhan dan hewan. Dalam proses fotosintesis terdapat tiga pigmen yang penting, yaitu klorofil, kartenoid, dan antosianin. Analisis pigmen dapat dilakukan dengan Kromatorafi Cair Kinerja Tinggi (KCKT) dan spektrofotometer. Namun,KCKT dan spektrofotometer membutuhkan sumber daya dan waktu yang tinggi. Sehingga, aplikasi Android Fuzzy Piction yang dibangun menggunakan model FP3Net mejadi pilihan dalam prediksi pigmen dengan biaya murah dan mudah. Akan tetapi, aplikasi Android Fuzzy Piction menghasilkan kinerja yang berbeda-beda yang dipengaruhi oleh kondisi cahaya dan spesifikasi kamera. Dilakukan percobaan dengan mengambil sepuluh sampel citra daun dari empat varietas tanaman yaitu, pucuk merah, daun ungu, melati, dan sirih. Citra diambil dengan tiga kamera smartphone dan tiga tingkat pencahayaan yang berbeda. Perbaikan yang dilakukan menggunakan algoritma 3D-TPS menghasilkan nilai SSIM terbaik pada rentang 0.9191 –0.9797 untuk citra daun pucuk merahdan nilai MAE prediksi pigmen sebesar 0.0296 –0.0492.Kata Kunci: 3D – TPS, daun tanaman, pigmen, perbaikan kualitas citr

Tutorial Pembelajaran Interaktif Polymerase Chain Reaction (PCR)

Tutorial Pembelajaran Interaktif Polymerase Chain Reaction (PCR) adalah program computer berupa flash yang merupakan media pembelajaran interaktif yang berisi teknik dasar PCR. Tutorial pembelajaran ini mendukung beberapa matakuliah seperti matakuliah Analisis DNA, Biologi Molekuler dan Biokimia. Tutorial ini berisi deskripsi dan tahapan-tahapan dalam analisis PCR mulai dari tahap denaturasi, primer annealing dan elongasi baik dalam bentuk gambar maupun video. Pengenalan tahapan PCR dilakukan secara interaktif, di mana pengguna tutorial diajak meng-klik opsi-opsi tertentu untuk mengaktifkan simulasi perhitungan jumlah amplikon dan melihat proses yang terjadi ketika suatu tahapan dilakukan. Animasi yang diberikan akan memudahkan pengguna memahami proses PCR baik secara konseptual maupun secara praktis. Pada bagian akhir tutorial disertai dengan latihan soal untuk membantu mengevaluasi sejauh mana pemahaman terhadap materi yang ada