No abstract

Abstrak Rambut kepala merupakan organ tubuh dari manusia yang memiliki bentuk seperti helaian benang yang tumbuh di kulit dengan mengandung banyak keratin serta dapat muncul dari lapisan epidermis. Terdapat berbagai faktor yang dapat mengakibatkan perubahan kondisi kulit kepala dan rambut seperti faktor usia lanjut, depresi, berkurangnya aktifitas kelenjar minyak dikulit kepala, gangguan pembuluh darah, gangguan hormon, pengaruh kosmetika, paparan sinar matahari secara terus menerus dan kurangnya makanan yang bergizi untuk kepentingan pertumbuhan rambut. Penelitian ini melakukan perancangan model Fuzzy Sugeno untuk menentukan tingkat kerontokan rambut kepala pada pria berdasarkan faktor-faktor penyebabnya. Salah satu tujuan dalam penelitian ini adalah untuk mengetahui tingkat kerontokan rambut kepala pada pria menggunakan metode Sugeno. Pada model Fuzzy Sugeno mendapatkan hasil yang rendah dalam menentukan tingkat kerontokan rambut kepala pada pria, yaitu memperoleh nilai error sebesar 114,870 untuk nilai MSE dan 5,73% untuk nilai MAPE.

This study proposes a new approach in the sentence tokenization process. Sentence tokenization, which is known so far, is the process of breaking sentences based on spaces as separators. Space-based sentence tokenization only generates single word tokens. In sentences consisting of five words, tokenization will produce five tokens, one word each. Each word is a token. This process ignores the loss of the original meaning of the separated words. Our proposed tokenization framework can generate one-word tokens and multi-word tokens at the same time. The process is carried out by extracting the sentence structure to obtain sentence elements. Each sentence element is a token. There are five sentence elements that is Subject, Predicate, Object, Complement and Adverbs. We extract sentence structures using deep learning methods, where models are built by training the datasets that have been prepared before. The training results are quite good with an F1 score of 0.7 and it is still possible to improve. Sentence similarity is the topic for measuring the performance of one-word tokens compared to multi-word tokens. In this case the multiword token has better accuracy. This framework was created using the Indonesian language but can also use other languages with dataset adjustments.

<p>Sentence segmentation that breaks textual data strings into individual sentences is an important phase in natural language processing (NLP). Each word in the string that is added a punctuation mark such as a period, question mark, or exclamation point, becomes the location for splitting the string. Humans can easily see the punctuation and split the string into sentences, but not machines. Basically, the three punctuation marks also perform other functions so that the sentence segmentation process must really be able to detect whether a word marked with punctuation is a sentence boundary or not. This research proposes a sentence segmentation system called segmentasi kalimat bahasa Indonesia (SKBI) or Indonesian language Sentence Segmentation by applying a set of rules and can be used in Indonesian texts and can be adapted for English. There are 34 rules built with a combination of 27 fairly complete features that contribute to this research. The experimental results for the Indonesian text show that the SKBI is able to achieve an F1-Score of 96.89% and 97.07% for English. Both need to be improved but now better than previous research.</p>