PENGUJIAN COGNITIVE WALKTHROUGH ANTARMUKA PERPUSTAKAAN DIGITAL (E-LIBRARY) PUSAT DOKUMENTASI DAN INFORMASI ILMIAH – LIPI (PDII-LIPI)

Interface design has a significant role towards the successful of digital librarys application use. Digital libraryservices developed by Centre for Scientific Documentation and Information - Indonesian Institute of Sciences (PDII-LIPI) has yet to be evaluated. This research analyzed the design of the web interface of PDII-LIPI’s digitallibraries using the method of cognitive walkthrough (CW). The aim of the research is to identify user constraints inusing PDII-LIPI’s digital libraries. Object of this study are three menus in digital library web interface that is “KaryaIlmiah Indonesia”, “Buku Elektronik”, and “Jurnal Indonesia (ISJD)”. CW testing parameters for PDII-LIPI’s digitallibrary interface consists of the successful completion of the task, and the effectiveness of the task. The successfulcompletion of the task was assessed by comparing the standard time with task completion time by respondents. Effectiveness accessed based on the processing time of each stage and the number of mistakes made by therespondent. The test results showed that all respondents successfully completed the task with the time that goesbeyond the standard set time. The analysis was conducted on all test results indicate that the obstacles faced byusers in general are finding menu of “E-Library”, specify the search facility is used, and searching the articles

Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification

K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time.

Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016

Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents.Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Data Analysis.  Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result.Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016.Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters. 

Identification of Tuna and Mackerel based on DNA Barcodes using Support Vector Machine

Tuna and mackerel are important fish in Indonesia that have great demand in the community and contain good nutrients for health. Many of the processed products have been faked including processed fish, by replacing the content of products that have high sales value to other lower price one. For ensuring food safety, fraudulent should be prevented by identifying the content of refined product. In this research, we implemented support vector machine (SVM), one of the popular methods in machine learning, to yield a model for identifying the content of refined product based on DNA barcode sequences. The feature extraction of DNA barcode Sequences was conducted by calculating k-mers frequency of each sequences. In this study, we used trinucleotide (3-mers) and tetranucleotide (4-mers). These features were inputted to SVM to classify and identify whether the DNA barcode sequences belong to the class of tuna, mackerel, or other fish. The evaluation results showed model SVM was able to perform identification with the accuracy 88%

SISTEM MANAJEMEN PENGETAHUAN PERLINDUNGAN ANAK (STUDI KASUS: SAKTI PEKSOS DI KEMENTERIAN SOSIAL)

In 2014, The Ministry of Social Affairs ofThe Republic of Indonesia (MOSA) send 670 social workers, called “Sakti Peksos†to all provinces in Indonesia. Sakti Peksos, which stands for “Satuan Bakti Pekerja Sosial†is the social workers who assist the Child Welfare Program in Indonesia or known as “PKSAâ€. To assist the children’s case, the social workers need many practice knowledge of how to deal with the children’s issues. Therefore, it is beneficial to manage the existing information by developing knowledge management system. Through the application based web program, this knowledge management system will develop all the Sakti Peksos’s knowledge. This web aims at organizing all the informations that they have so that it can be shared to other Sakti Peksos. The purpose of this study was to develop a Child Protection Knowledge Management System for Sakti Peksos in the Ministry of Social Affairs. This research uses Knowledge Management System Life Cycle methodology which is adopted from award and ghaziri methodology. This research involves four steps; gathering the knowledge, designing the brueprint of knowledge management system, helding verification as well as validation process, and implementing knowledge management system. The result of this research is a web based child protection knowledge management system, equipped with CakePHP framework and MySQL as Relational Database Management System (RDBMS)

Klasifikasi Kanker Tumor Payudara Menggunakan Arsitektur Inception-V3 Dan Algoritma Machine Learning

Breast cancer is a disease that arises due to breast tissue cells that grow abnormally and continuously. This disease is a disease with a large increase in number of around 13 million per year, with a mortality rate of 9.6% from a total of 65,858 cases. Early detection of breast cancer for prevention needs to be done, with the hope that breast cancer is easier to treat and cure and can even be prevented before it enters an advanced stage. In this research, build a model with transfer learning technique for breast cancer classification. There are 4 methods tested, namely Inception-V3 feature extraction with the Radial Basic Function Neural Network classification method, FeedForward Neural Network, Logistic Regression and feature extraction by making changes to the hyperparameter layer. This study compares the four models to get the best one to solve the problem of breast cancer classification. The data used in this study are breast cancer image data with a zoom scale of 40X, 100X, 200X and 400X. The dataset was sourced from The Laboratory University of Parana with P&D Laboratory Pathological Anatomy and Cytopathology, Parana, Brazil. The results of this study indicate that the Inception-V3 feature extraction method with the Logistic Regression classification method on the 40X zoom scale data provides the best accuracy (93.00%), precision (94.00%), and recall (91.00%) F1-score (92.00%)

Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means

In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal.

Performance Comparison of Data Sampling Techniques to Handle Imbalanced Class on Prediction of Compound-Protein Interaction

The prediction of Compound-Protein Interactions (CPI) is an essential step in the drug-target analysis for developing new drugs as well as for drug repositioning. One challenging issue in this field is that commonly there are more numbers of non-interacting compound-protein pairs than interacting pairs. This problem causes bias, which may degrade the prediction of CPI. Besides, currently, there is not much research on CPI prediction that compares data sampling techniques to handle the class imbalance problem. To address this issue, we compare four data sampling techniques, namely Random Under-sampling (RUS), Combination of Over-Under-sampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link (T-Link). The benchmark CPI data: Nuclear Receptor and G-Protein Coupled Receptor (GPCR) are used to test these techniques. Area Under Curve (AUC) applied to evaluate the CPI prediction performance of each technique. Results show that the AUC values for RUS, COUS, SMOTE, and T-Link are 0.75, 0.77, 0.85 and 0.79 respectively on Nuclear Receptor data and 0.70, 0.85, 0.91 and 0.72 respectively on GPCR data. These results indicate that SMOTE has the highest AUC values. Furthermore, we found that the SMOTE technique is more capable of handling class imbalance problems on CPI prediction compared to the remaining three other techniques

Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179

SELEKSI FITUR YANG BERPENGARUH MENGGUNAKAN NILAI MEAN PADA KLASIFIKASI FRAGMEN METAGENOME

Pekuwali (2018) has conducted research into the classification of metagenome fragments using spaced k-mers. Optimize the arrangement of features using Genetic Algorithms. Pekuwali (2018) concluded that the best arrangement of features or called chromosomes is 111111110001 with a fitness value of 85.42. Chromosome 111111110001 produces 336 features of extracting DNA fragments. This research aims to find out which features influence classi fi cation and the resulting accuracy. The method used is the Mean value. The mean value method was chosen because the data distribution is normal or close to normal. This study concludes that the influential features in the classification are features 22 to 27 with an accuracy of 78.83% and features 38 to 43 with an accuracy of 79.67%.    Pekuwali (2018) telah melakukan penelitian klasifikasi fragmen metagenome menggunakan spaced k-mers. Optimasi susunan fitur menggunakan Algoritma Genetika. Pekuwali (2018) menyimpulkan bahwa susunan fitur terbaik atau disebut kromosom adalah 111111110001 dengan nilai fitness 85,42. Kromosom 111111110001 menghasilkan 336 fitur pengekstraksi fragmen DNA. Penelitian kali ini bertujuan untuk mengetahui fitur mana saja yang berpengaruh dalam pengklafikasian dan akurasi yang dihasilkan. Metode yang digunakan adalah nilai Mean. Metode nilai mean dipilih karena sebaran data normal atau mendekati normal. Penelitian ini menyimpulkan bahwa fitur yang berpengaruh dalam pengklasifikasian adalah fitur 22 sampai 27 dengan akurasi sebesar 78,83% dan fitur 38 sampai 43 dengan akurasi sebesar 79,67%. &nbsp