Judul: Modeling Risk Cluster based on Sentiment Analysis in Bahasa Indonesia for SME Business Risk Analysis Documents
Abstrak: Currently, there are two risk analysis models that commonly used for business financing in banking industry, namely, the quantitative model and the qualitative model. The quantitative model are mostly implemented as an credit scoring system that consists of several accounting formulation that calculates the financial statement and business performance to determine the feasibility of bank customers in accepting loan. The second model, namely qualitative model, emphasizes the risk analysis, opinion, and mitigation from risk analyst to support the decision makers in accepting loan proposal. From the observation through the Standard Operating Procedure (SOP) the quantitative model has some drawbacks in measuring the acceptance criteria, since the data are originated from the customer itself and vulnerable to have a manipulation, especially when the financial statement has no any inspection from external auditor. The second drawback is that the quantitative model tend to be subjective since the credit scoring calculation are performed by the marketing staff that stand sides to the customer. Hence, the qualitative model are deployed to overcome these drawbacks, where the analysis is objectively proceed by risk analysts. However, the implementation of qualitative model are not a hundred percent perfect, since the qualitative model neither has decision criteria nor risk measurement. Another issue is that the risk analysis documents that consist of risk opinion and mitigation from previous analysis, are not well managed. Actually, these documents are useful for the risk analyst to evaluate and relearn from the previous analysis. In this research, the opinion or sentiment analysis against the risk analysis documents is conducted by modeling the risk cluster to help the risk analyst in refine the analysis. There are three tasks that have been conducted, those are clustering the risk analysis documents based on the term presence. Secondly is quantify the risk level within each cluster by measuring the term importance and sentiment score using TF-IDF and SentiWordNet 3.0 respectively. The task is eventually finished by evaluating the cluster quality using Silhouette function and examining the most frequent terms by its importance and sentiment. We also develop a prototype that enables risk analysts to retrieve the risk analysis documents by entering query terms and presents the level of risk from each document. The results has been shown that sentiment mining technique is effective and could be utilized to model risk cluster. This could be seen in how relevant is the cluster model with the 5Cs Credit criteria that commonly used in banking industry. In discussion, there are also some suggestions for the management on what criteria they should resharpen in conducting the qualitative model. By giving the risk score in each cluster, it is expected that the model also could be used as a benchmark to qualify the submitted loan proposal and assist the decision maker to have a better decision making.
Keyword: centroid optimation, K-Means clustering, opinion mining, risk analysis, SME business, sentiment analysis

Judul: A Study of Sentiment Analysis Using Statistical Machine Learning Approach
Abstrak: Sentiment analysis, which was introduced in the early 2000s, is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. The news released by the mass media can influence public opinion, which, in turn, might influence their behavior. This research is essential to understand the public response to Covid-19 so that the governments can make the right decision to handle it. Data acquisition is the initial stage of the analysis. Web scraping is a data acquisition technique that can obtain data from public sources. It is a technique for converting unstructured data into structured data that can be stored and analyzed in a database or spreadsheet. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Sampling was carried out by using two-stage random sampling. The first stage was used to get sample of news portals using stratified random sampling, and the second stage was to obtain news articles using systematic random sampling. In general, available data tend to come without annotation. Of course, this is a problem because sentiment modelling requires annotated training data, with its annotation as the response variable. As a result, pre-analysis is needed to find representative news for the labelling process. In this research, descriptive analysis and topic modelling are carried out as the basis for selecting news to be labelled. This research used news articles related to Covid-19 in Indonesia. The news data were extracted from Indonesian news portals using web scraping methods. Only articles that were published between March 2020 and March 2021 are extracted. The explanatory variables in this research result from the wordembedding and Bag-of-Word process, while the response variable is the sentiment category (positive, negative, and neutral) The research is conducted in three stages: data acquisition, pre-processing, and modelling. In the data acquisition stage, sampling and web scraping were employed to get news article data. Data pre-processing was done by employing data cleansing, n-gram tokenization, term weighting using TF-IDF, word embedding (word2vec) and data labelling. The models used in this research are multinomial logistic regression by applying L1 and L2 regularization, Support Vector Machine (SVM) with Radial Basic Function (RBF) kernel, and Convolutional Neural Network (CNN). Then, the model is evaluated by using precision, recall, and F1- scores. In order to determine the best model, hypothesis testing is employed by using two-way MANOVA and Tukey testing. Based on the results of topic modeling and positive negative word counting, the news is grouped into nine group. Based on the grouping results, researcher decided to label 1500 news. News selection was made randomly in each group. A linguistic expert will do the labeling process. This process will be repeated three times by three different people, and the sentiment will then be determined based on the voting results of the
Keyword: data acquisition, data preparation, sentiment modeling, web scraping, data acquisition, data preparation, sentiment modeling, web scraping

Judul: Tingkat Kelahiran Ikan Plati Sunset Xiphophorus maculatus (Günther, 1866) pada Beberapa Tingkat Suhu Air
Abstrak: Ikan plati merupakan ikan hias air tawar yang bereproduksi dengan cara melahirkan (viviparous) dalam beberapa waktu. Memproduksi ikan plati dalam skala besar ternyata dihadapkan pada kendala yang besar, yaitu belum ada teknologi kelahiran anak serentak dalam satu waktu. Hal ini berdampak pada umur anak ikan tidak seragam dan pola tanam produksi terganggu, sehingga berujung pada ketidakefisienan dalam produksi. Pada penelitian ini dilakukan upaya penyerentakkan proses kelahiran oleh induk plati melalui rangsangan eksternal berupa peningkatan suhu secara mendadak. Penelitian ini menggunakan rancangan faktorial dengan empat perlakuan sebanyak tiga ulangan. Perlakuan kenaikan suhu yang diberikan adalah 30-32°C selama 12 dan 24 jam. Hasil yang diperoleh perlakuan kenaikan suhu ke 30°C selama 12 jam perlakuan memberikan tingkat induk melahirkan tertinggi.
Keyword: Ikan hias, Kelahiran, Poecillidae, Suhu, Xiphophorus maculatus