Implementation of TF-IDF Algorithm to detect Human Eye Factors Affecting the Health Service System

Elderly is someone whose age is around 60-74 years, at that age, one's health tends to decrease, and it has an impact on reduced perception, cognition, and psychometry. One result of cognitive decline is a decrease in memory. Programs have been provided by the Indonesian government, such as submitting information, producing brochures, and making announcements on the health services website. But this counseling is not optimal because the elderly tend to be lazy to read this because the eyes have begun to look away from other than that the eye health of the elderly has already started to decrease. So that the health information provided by the health department can be optimized, we try to make a model that is used to summarize an article so that the article is easily understood by the elderly. To summarize the article, this study uses the term frequency-inverse document frequency (TF-IDF) algorithm. TF-IDF It is an algorithm used to summarize sentences so that it is easier to understand and understand. By using the TF-IDF algorithm, it is hoped that the elderly will more easily read health articles. User Experience Questionnaire after the application of writing software summary is higher than before the application of writing software summary that is 25.27> 19.30. Keywords—Elderly, Information, Summary, TF-IDF Abstrak— Lansia adalah seseorang yang usianya berkisar 60-74 tahun, pada usia itu kesehatan seseorang cenderung menurun berdampak pada penurunan persepsi, kognitf dan psikometri. Salah satu akibat penurunan kognitif adalah penurunan memori. Program telah disediakan oleh pemerintah Indonesia, seperti memberikan informasi, memberikan brosur, dan memberikan pengumuman di situs web layanan kesehatan. Tetapi konseling ini tidak optimal karena para lansia, cenderung malas membaca ini karena mata sudah mulai berpandangan jauh selain itu kesehatan mata lansia sudah mulai berkurang. Agar informasi kesehatan yang diberikan oleh dinas kesehatan dapat optimal kami mencoba membuat model yang digunakan untuk meringkas sebuah artikel sehingga artikel tersebut mudah dipahami oleh para lansia. Untuk meringkas artikel, penelitian ini menggunakan algoritma term frequency–inverse document frequency (TF-IDF). TF-IDF Merupakan algoritma yang digunakan urntuk meringkas kalimat sehingga lebih mudah dipahami dan dimengerti Dengan menggunakan algoritma TFIDF diharapkan lansia akan lebih mudah mudah membaca artikel kesehatan. User Experience Quetionaire sesudah penerapan software summary tulisan lebih besar daripada sebelum penerapan software summary tulisan yaitu 25.27 > 19.30. Kata Kunci—Lansia, Informasi, Ringkasan, TF-IDF INTENSIF, Vol.4 No.1 February 2020 ISSN: 2580-409X (Print) / 2549-6824 (Online) DOI: https://doi.org/10.29407/intensif.v4i1.13858 124 INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi

[1]  G. Waters,et al.  Effects of age, speed of processing, and working memory on comprehension of sentences with relative clauses. , 2011, Psychology and aging.

[2]  S. Carmen.  Importance of Counselling for Elderly Before Institutionalization , 2013 .

[3]  L. Obler,et al.  The Effect of Plausibility on Sentence Comprehension Among Older Adults and its Relation to Cognitive Functions , 2015, Experimental aging research.

[4]  Weisi Dai.  Improvement and Implementation of Feature Weighting Algorithm TF-IDF in Text Classification , 2018 .

[5]  Yeonhee Ryu,et al.  Trends in deqi research: a text mining and network analysis , 2018, Integrative medicine research.

[6]  Hamid R. Tizhoosh,et al.  Automatic Classification of Pathology Reports using TF-IDF Features , 2019, ArXiv.

[7]  Shailesh D. Kamble,et al.  Text Mining Using Metadata for Generation of Side Information , 2016 .

[8]  Taoufiq Gadi,et al.  Ranking of text documents using TF-IDF weighting and association rules mining , 2018, 2018 4th International Conference on Optimization and Applications (ICOA).

[9]  Bening Herwijayanti.  Klasifikasi Berita Online Dengan Menggunakan Pembobotan TF-IDF Dan Cosine Similarity , 2017 .

[10]  Made Sudarma,et al.  The Thinning Zhang-Suen Application Method in the Image of Balinese Scripts on the Papyrus , 2014 .

[11]  Desmon Limawan,et al.  GAMBARAN KADAR KALSIUM SERUM PADA USIA 60-74 TAHUN , 2015 .

[12]  Kusrini,et al.  Classification method of multi-class on C4.5 algorithm for fish diseases , 2016, 2016 2nd International Conference on Science in Information Technology (ICSITech).

[13]  Sarit Chakraborty,et al.  An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation , 2018, ArXiv.

[14]  Sucipto Sucipto,et al.  Hoax Detection at Social Media With Text Mining Clarification System-Based , 2018 .

[15]  Namil Kim,et al.  Data Mining of Acupoint Characteristics from the Classical Medical Text: DongUiBoGam of Korean Medicine , 2014, Evidence-based complementary and alternative medicine : eCAM.

[16]  Sucipto Sucipto,et al.  Cyberbullying Detection Modelling at Twitter Social Networking , 2018 .

[17]  Agus Santoso,et al.  Peningkatan Mutu Pelayanan Kesehatan Usia Lanjut Melalui Peningkatan Kinerja Kader Posyandu Lansia , 2018 .

[18]  Sheetal Chaudhari,et al.  Text analysis and information retrieval of text data , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).