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All Journal Majalah Ilmiah Teknologi Elektro
Veronica Ambassador Flores
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Electrical & Electronics Engineering

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Pembangkit Pertanyaan Otomatis pada Materi Pelajaran Ilmu Pengetahuan Alam Berbahasa Indonesia di Tingkat Sekolah Dasar Berdasarkan Revisi Taksonomi Bloom Veronica Ambassador Flores; Lie Jasa; Rukmi Sari Hartati
Jurnal Teknologi Elektro Vol 20 No 2 (2021): (Juli-Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2021.v20i02.P19

Abstract

Creating questions on an exam is a complex process, because this process requires knowledge and takes a long time to make. The process of creating questions can be done more easily, quickly, and structured with the Automatic Question Generator (AQG) system. This application uses the Text Matching Method to find keywords in a paragraph, where the Expected Answer Type (EAT) method would identify these keywords. The EAT method helped to identify the type of answers in a paragraph therefore the type of questions would be recognized. The types of questions used are 5W + 1H consisting of Who, Where, When, Why, What, How, and How Many.The next method is the Template Based Method which played a role in compiling the question sentence based on the pre-registered template. The questions were produced using the Revised Bloom's Taxonomy concept, where these questions consisted of categories (1) remembering; (2) understand; (3) apply; (4) analyze; (5) evaluate; and (6) create. The trial result in 14 learning materials, showed that the application could generate 826 questions with an average level of accuracy of 89%.
Aplikasi Sistem Pakar Diagnosa Penyakit Anjing Berbasis Facebook Messenger Veronica Ambassador Flores; Linawati Linawati
Jurnal Teknologi Elektro Vol 19 No 1 (2020): (Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2020.v19i01.P17

Abstract

Abstract—An Expert System of Disease Diagnosis in Dogs Based on Facebook Messenger was a useful application to provide an initial diagnosis of the diseases based on the symptoms given by the users. The application uses the Facebook Messenger-based on Natural Language Processing (NLP) method to allow users to easily and comfortably interacting with the app. Diagnosis was obtained by identifying the pattern, and classifying the pattern. The method used in the pattern identification process is N-gram, this method was a matching pattern method where the number of words or character pattern that would be match could be adjusted. The method used to classify a disease is a Matching Template, this method worked by matching the template pattern with the test pattern to find the similarities between the patterns. The study concluded that the application of an expert systems using the N-gram method and the Matching Template had an accurate diagnosis rate of 80%.
Penerapan Web Scraping Sebagai Media Pencarian dan Menyimpan Artikel Ilmiah Secara Otomatis Berdasarkan Keyword Veronica Ambassador Flores; Putri Agung Permatasari; Lie Jasa
Jurnal Teknologi Elektro Vol 19 No 2 (2020): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2020.v19i02.P06

Abstract

Artikel ilmiah adalah sebuah referensi yang biasanya digunakan oleh seorang mahasiswa atau peneliti untuk membuat sebuah penelitian. Artikel Ilmiah dapat dicari melalui Internet secara bebas, namun proses pencarian ini seringkali memakan waktu dikarenakan banyaknya artikel ilmiah yang ada di Internet. Pencarian ini dapat dilakukan secara otomatis dengan memanfaatkan Teknik Web Scraping sebagai teknik pengambilan data pada suatu situs web di Internet. Metode Depth First Search (DFS) adalah metode pencarian yang dapat dimanfaatkan untuk melakukan web Scraping data di Internet. Sistem ini bekerja dengan mengolah Keyword berupa kata kunci dari topik artikel ilmiah yang akan dicari yang diberikan oleh pengguna. Kemudian sistem akan mengirimkan request kepada server untuk mencari tautan yang berhubungan dengan Keyword tersebut. Setelah hasil pencarian sudah ditemukan, sistem akan secara otomatis mengunduh dan menyimpan artikel yang berformat .pdf ke komputer pengguna. Hasil uji coba dari 134 artikel yang berhasil diunduh menggunakan sistem ini menunjukkan bahwa sistem ini dapat bekerja dengan sangat baik untuk melakukan pencarian artikel ilmiah dengan tingkat relevansi antara artikel yang dicari dengan artikel yang diunduh sebesar 99%.
Analisis Sentimen untuk Mengetahui Kelemahan dan Kelebihan Pesaing Bisnis Rumah Makan Berdasarkan Komentar Positif dan Negatif di Instagram Veronica Ambassador Flores; Lie Jasa; Linawati Linawati
Jurnal Teknologi Elektro Vol 19 No 1 (2020): (Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2020.v19i01.P07

Abstract

Abstract— Formulating a marketing strategy for a pioneering or long-running restaurant business is a very important thing. Analyze the weaknesses or strengths of business competitors is one of these strategies. Identification of weaknesses and strengths can be done by taking data from comments on competitors' Instagram accounts using Text Preprocessing Techniques. Text Preprocessing is a text processing algorithm, consisting of Transform Cases, Stopword Filters, Tokenize Filters, and Stemming. Instagram is one of the most widely used social media accounts as a promotional media in Indonesia. Another method that can be used is Full Text Search, this method can analyze the patterns in comments that have been parsed for classified into positive, negative, or neutral sentiment categories. This study concludes that this sentiment analysis system can automatically recognized weaknesses (based on negative comments) and strengths (based on positive comments) based on comments on Instagram accounts owned by restaurant business competitors with an accuracy of 85% and a precision value of 79%.
Co-Authors Lie Jasa Linawati Linawati Putri Agung Permata Sari Rukmi Sari Hartati