An ontology is defined as an explicit specification of a conceptualization, which is an important tool for modeling, sharing and reuse of domain knowledge. However, ontology construction by hand is a complex and a time consuming task. This research presents a fully automatic method to build bilingual domain ontology from text corpora and ontology design patterns (ODPs) in Alzheimer's disease. This method combines two approaches: ontology learning from texts and matching with ODPs. It consists of six steps: (i) Term & relation extraction (ii) Matching with Alzheimer glossary (iii) Matching with ontology design patterns (iv) Score computation similarity term & relation with ODPs (v) Ontology building (vi) Ontology evaluation. The result of ontology composed of 381 terms and 184 relations with 200 new terms and 42 new relations were added. Fully automatic ontology construction has higher complexity, shorter time and reduces role of the expert knowledge to evaluate ontology than manual ontology construction. This proposed method is sufficiently flexible to be applied to other domains. Keywords: fully automatic, ontology building, ontology design patterns, Alzheimer disease AbstrakOntologi didefinisikan sebagai spesifikasi eksplisit dari sebuah konseptualisasi, yang merupakan alat penting untuk pemodelan, pembagian, dan penggunaan kembali pengetahuan domain. Namun, konstruksi ontologi dengan tangan merupakan tugas yang rumit dan memakan waktu. Penelitian ini menyajikan metode otomatis untuk membangun ontologi domain bilingual dari pola desain korporat teks dan ontologi (ODPs) pada penyakit Alzheimer. Metode ini menggabungkan dua pendekatan: pembelajaran ontologi dari teks dan sesuai dengan ODP. Ini terdiri dari enam langkah: (i) ekstraksi istilah & hubungan (ii) Pencocokan dengan glosarium alzheimer (iii) Pencocokan dengan pola desain ontologi (iv) Perhitungan skor kesamaan istilah & hubungan dengan ODPs (v) Ontologi bangunan (vi) Evaluasi Ontologi. Hasil ontologi yang terdiri dari 381 istilah dan 184 hubungan dengan 200 istilah baru dan 42 hubungan baru ditambahkan. Konstruksi ontologi otomatis lengkap memiliki kompleksitas yang lebih tinggi, waktu yang lebih singkat dan mengurangi peran pengetahuan ahli untuk mengevaluasi ontologi daripada konstruksi ontologi manual. Metode yang diusulkan ini cukup fleksibel untuk diterapkan pada domain lain.Kata Kunci: fully automatic, ontology building, pola desain ontologi, penyakit alzheimer
Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text. This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the support vector machine (SVM) and Multinomial Naïve Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second step classifies data that contain emotions into five types of emotions i.e. happy, angry, sad, scared, and surprised. This study used three scenarios, namely SVM with TF-IDF, SVM with Word2Vec, and MNB with TF-IDF. The SVM with TF-IDF method generate the highest accuracy compared to other methods in the first dan second steps classification, then followed by the MNB with TF-IDF, and the last is SVM with Word2Vec. Then, the evaluation using precision, recall, and F1-measure results that the SVM with TF-IDF provides the best overall method. This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
Monitoring activity in computer networks is required to detect anomalous activities. This monitoring model is known as an intrusion detection system (IDS). Most IDS model developments are based on machine learning. The development of this model requires activity data in the network, either normal or anomalous, in sufficient amounts. The amount of available data also has an impact on the slow learning process in the IDS system, with the resulting performance sometimes not being proportional to the amount of data. This study proposes an IDS model that combines DBSCAN modification with the CART algorithm. DBSCAN modification is performed to reduce data by adding a MinNeighborhood parameter, which is used to determine the distance of the density to the cluster center point, which will then be marked for deletion. The test results, using the Kaggle and KDDCup99 datasets, show that the proposed system model is able to maintain a classification accuracy above 90% for 80% data reduction. This performance was also followed by a decrease in computation time, for the Kaggle dataset from 91.8 ms to 31.1 ms, while for the KDDCup99 dataset from 5.535 seconds to 1.120 seconds.
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