Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflicts between citizens. Moreover, hate speech has a target, category, and level that also need to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approaches with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general the RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.

Tersedianya data histori rekam medis pasien kanker serviks pada institusi pelayanan kesehatan, tidak disertai dengan proses ekstraksi menjadi sebuah pengetahuan atau informasi. Penggunaan teknik data mining sangat berpotensi untuk diimplementasikan kedalam sistem yang dapat melakukan prediksi penyakit kanker serviks. Pada penelitian ini berfokus pada dataset diagnosa medis pasien yang akan melakukan tes Pap Smear. Algoritma yang digunakan untuk melakukan klasifikasi penyakit kanker serviks adalah Classification And Regression Trees (CART), Naive Bayes, dan k-Nearest Neighbor (k-NN). Pengujian yang dilakukan terhadap algoritma CART Decision Tree, Naive Bayes, dan k-NN, menggunakan formula Confusion Matrix, dengan menggunakan teknik pemecahan dataset Holdout. Hasil pengujian terhadap algoritma yang digunakan, menunjukkan algoritma Naive Bayes memiliki akurasi terbaik sebesar 94,44%, sedangkan tingkat akurasi yang dihasilkan algoritma CART dan k-NN adalah 88,89%, 85,04%. Performa yang didapatkan oleh masing-masing algoritma yang digunakan, memungkinkan penggunaan sistem prediksi penyakit kanker serviks untuk mendukung keputusan klinis pada pasien baru.

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Many companies globally apply ISO 14001 including in Indonesia. In there, most of the implementers are largely established companies in terms of quality, financial, infrastructure documentation, knowledge and understanding of the ISO. ISO 14001 was issued in 2015 to replace ISO 14001: 2004 and consider a risk-based approach in its application. This research examines the SMEs laundry machine industry in Yogyakarta that attempts to implement and achieve ISO 14001: 2015 certificate. Research results inform the implementation of ISO 14001: 2015 in the SMEs industry to achieved the extended result and applied the risk-based approach. The description of every step is expected to give understanding and model for implementing ISO 14001:2015 in this case study location. This study finds out that SMEs can be effective in some of the clauses of ISO 14001:2015. With implementation ISO 14001:2015 in SMEs expected to change the environment for better, especially in reducing the impact of the waste produced where the waste contributes to the formation of greenhouse gases which means accelerating the occurrence of global climate change.

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Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative. However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result. This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multilabel classification. In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning. The features we compared are n-gram language model (unigram, bigram, unigram-bigram). For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using selfdeveloped word embedding. The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively. For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively. On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1-score for food aspect, 88.16%. On price aspect, GRU leads with 83.01%. However, for service and ambience, BiLSTM achieved higher F1score, 89.03% and 84.78%.