Persepakbolaan Indonesia belakangan ini memiliki banyak polemik mulai dari kasus pengaturan skor, pergantian pelatih timnas senior hingga pergantian ketua umum Persatuan Sepak bola Seluruh Indonesia (PSSI). Polemik ini menimbulkan banyaknya opini maupun pendapat dari pengguna twitter terhadap persepakbolaan di Indonesia sehingga diperlukan sebuah sistem untuk memudahkan dalam mengetahui sentimen pada setiap kalimat. Tujuan dari penelitian ini adalah untuk menganalisis sentimen pada setiap kalimat dari pengguna twitter terhadap persepakbolaan Indonesia apakah memiliki sentimen negatif atau positif. Data yang digunakan dalam penelitian ini didapatkan dari hasil crawling dari media sosial twitter terkait persepakbolaan di Indonesia yang diambil dari akun twitter resmi PSSI. Setelah data dikumpulkan kemudian akan dilakukan beberapa tahapan yaitu preprocessing yang terdiri dari cleansing, tokenizing, stopword removal, dan stemming. Pembobotan kata menggunakan Term Frequency-Invers Document Frequency (TF-IDF). Pada tahap validasi data dilakukan pengujian silang sebanyak 10 kali menggunakan k-fold cross validation, kemudian diklasifikasikan dengan metode K-Nearest Neighbor dapat menghasilkan akurasi yang cukup baik. Dari 2000 data tweet berbahasa indonesia didapatkan hasil akurasi optimal pada nilai k=23 sebanyak 79.9%

It is well known that human voice at the perceptual level consists of multimodality information. Therefore, a modality can be shared via neural emotion network through the independent stimuli processes. The expression and identification of emotions are significant steps for the human communication process. This process is biologically adaptive in a continuous manner, and for this reason, human voice identification becomes useful for classifying and identifying an effective specific characteristic between them. In this paper, we propose to identify the difference between the six essentials of human voice emotion. They will be generated using prosodic and spectral features extraction by utilizing Deep Neural Networks (DNNs). The result of our experiment has obtained accuracy as much as 78.83%. It presupposed that the higher intensity of emotions found in the sound sample would automatically trigger the level of accuracy as same as a higher one. Moreover, gender identification was also carried out along with the approximate accuracy at 90%. Nevertheless, from the learning process with the composition of 80:20, the training-testing data has obtained an exact accurate result by 100%.

Development of microarray technology makes the number of research about Bioinformatics will increase as well. Microarray dataset contains genetic information and can be used to analyze thousands of samples and features. Especially in cancer research, the cancer data is generated by microarray technology, and will be a primary data for training and testing in the machine learning process. The main difficulties lie in the nature of microarray gene expression data which usually are noisy and high-dimensional. Microarray dataset usually has a large number of attributes or features, but it has a small number of samples. This condition makes the learning process microarray dataset has become harder because of the curse of dimensionality, where the machine will be difficult to handle a number of data with a very high-dimensional. The solution to handle a high-dimensional dataset and improve the accuracy of microarray dataset is using feature selection. The method of feature selection is followed the principle of natural selection, called Evolutionary Algorithms. We proposed to implement some Evolutionary Algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), which ACO improved F-measure of 9 datasets and ROC Area of 7 datasets from 11 datasets existing in the cancer research.

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their contents. To solve this drawback, we have proposed a reference paper collection system using a web scraping technology and natural language models. However, our previous system often finds a limited number of relevant reference papers after taking long time, since it relies on one paper search website and runs on a single thread at a multi-core CPU. In this paper, we present an improved reference paper collection system with three enhancements to solve them: (1) integrating the APIs from multiple paper search web sites, namely, the bulk search endpoint in the Semantic Scholar API, the article search endpoint in the DOAJ API, and the search and fetch endpoint in the PubMed API to retrieve article metadata, (2) running the program on multiple threads for multi-core CPU, and (3) implementing Dynamic URL Redirection, Regex-based URL Parsing, and HTML Scraping with URL Extraction for fast checking of PDF file accessibility, along with sentence embedding to assess relevance based on semantic similarity. For evaluations, we compare the number of obtained reference papers and the response time between the proposal, our previous work, and common literature search tools in five reference paper queries. The results show that the proposal increases the number of relevant reference papers by 64.38% and reduces the time by 59.78% on average compared to our previous work, while outperforming common literature search tools in reference papers. Thus, the effectiveness of the proposed system has been demonstrated in our experiments.

Breast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisher’s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between Naïve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient.

The development of the dairy cows cattle industry has management standards ranging from feed management, breeding, and milk production. Inbreeding of dairy cows, Artificial Insemination (AI) is carried out from selected stud semen so that dairy cows have more opportunity to calve female cows. Dairy cows have indication of lust regularly called as estrous cycle. Good handling of AI during the estrous period, will increase the percentage pregnancy of the cow. A balanced plan between stud semen stock with AI needs in the farm will help the management of dairy cows. This research uses a dataset of 1,790 dairy cows with the collection of training and testing data. The stored data will be processed using the Multiple Logistic Regression method to be calculated as a result of the prediction ahead of the estrous cycle. Data on dairy cows that meet the processing requirements will be categorized by themselves. Variables in the dataset used in prediction calculations are time-series data. The results showed that the two features of the independent variables χ1 and χ2 have constant-coefficient values (βO) = 0.7927, (β1) = 0.0156, (β2) = 0.0000926 with model accuracy> 80%. Independent variable multinomial calculations produce an accuracy of 83.2%.