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All Journal International Journal of Electrical and Computer Engineering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Journal of Data Science and Its Applications eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat
Widi Astuti
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Analisis Sentimen Masyarakat Pada Media Sosial Twitter Terhadap Partai Politik Peserta Pemilihan Umum 2019 Menggunakan Naive Bayes Classifier Aprillia Rizki Adiati; Anisa Herdiani; Widi Astuti
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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AbstrakMenjelang Pemilihan Umum 2019, banyak partai politik memanfaatkan media sosial untuk berkampanyedan meningkatkan popularitas. Salah satu media sosial yang banyak digunakan dalam media promosipartai politik adalah twitter. Selain itu, media sosial twitter juga dapat dijadikan tempat oleh masyarakatdalam memberikan opini terhadap partai terkait baik opini positif maupun opini negatif. Pada tugas akhirini dibuat untuk menganalisis opini masyarakat terhadap partai politik peserta pemilu 2019 menggunakanmetode Naïve Bayes Classifier. Berdasarkan sistem yang dibangun, didapatkan hasil sentimen positifsebesar 53,8% dan sentimen negatif 46,13% dengan rata-rata akurasi sebesar 78,03%. Kata kunci: pemilihan umum, partai politik, analisis sentimen, Twitter, Naïve Bayes Classifier. AbstractAhead of the 2019 general election, many political parties used social media to campaign and increase popularity. One of the social media that is widely used in the media promotion of political parties is Twitter.In addition, social media twitter can also be used as a place by the public in providing opinions to partiesrelated to both positive and negative opinions. In this final project, it is made to analyze public opiniontowards political parties participating in the 2019 elections using the Naïve Bayes Classifier method. Basedon the system that was built, obtained an average accuracy of 78.03% and the results of positive sentimentof 53.8% and negative sentiment of 46.13%.Keywords: election, political parties, sentiment analysis, Twitter, Naïve Bayes Classifier
Implementasi Minimum Redundancy Maximum Relevance Sebagai Teknik Reduksi Dimensi Pada Klasifikasi Kanker Usus Besar Menggunakan Random Forest I.G.N.P.Vasu Geramona; Adiwijaya Adiwijaya; Widi Astuti
eProceedings of Engineering Vol 7, No 1 (2020): April 2020
Publisher : eProceedings of Engineering

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Abstrak Kanker merupakan penyakit yang mematikan. Mengutip informasi dari kementrian kesehatan Republik Indonesia pada tahun 2017 sembilan juta orang meninggal akibat kanker. Oleh sebab itu diperlukan sebuah metode untuk mendeteksi kanker salah satunya dengan gen expression. Microarray adalah salah satu teknik dari gen expression. Microarray sendiri memiliki feature yang banyak, feature yang banyak ini tidaklah selalu berkaitan dengan masalah yang sedang dihadapi. Sehingga dibutuhkan teknik reduksi dimensi untuk menyeleksi feature yang bersesuaian dengan masalah yang sedang dihadapi. Pada tugas akhir ini digunakan teknik reduksi dimensi menggunakan Minimum Redundancy Maximum Relevance yang selanjutnya akan disingkat dengan MRMR. Adapun Classifier yang digunakan adalah Random Forest, dimana teknik ini membuat beberapa tree untuk mengklasifikasi data lalu dilakukan voting untuk hasil terbanyak. Persamaan MRMR yang digunakan adalah FCD dan FCQ karena data yang digunakan bernilai kontinu. Setelah semua proses telah dilakukan, diperoleh hasil akurasi dari klasifikasi data microarray dengan menggunakan FCQ sebesar 83,87% dan dengan FCD 61,29%. Kata kunci : microarray, gen expression, random forest, MRMR Abstract Cancer is a deadly disease. Quoting information from the Ministry of Health of the Republic of Indonesia in 2017 nine million people died from cancer. Therefore we need a method to detect cancer, one of which is by gene expression. Microarray is a technique of gene expression. Microarray itself has many features, many of these features are not always related to the problem being faced. So we need a dimension reduction technique to select features that correspond to the problem being faced. In this final project a dimension reduction technique will be used using the Minimum Redundancy Maximum Relevance which will then be abbreviated as MRMR. The Classifier that will be used is Random Forest, where this technique creates several trees to classify data and then will vote for the most results. The MRMR equation used is FCD and FCQ because the data used is continuous. After the process done, the result from classify microarray data using FCQ is 83.87% and with FCD 61.29% Keywords: microarray, gen expression, random forest, MRMR
Identifikasi Teks Gereflekter Pada Buku Anak Dengan Algoritma K-nearest Neighbor I Kadek Ananda Prana Widya; Widi Astuti
eProceedings of Engineering Vol 7, No 1 (2020): April 2020
Publisher : eProceedings of Engineering

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Abstrak Buku anak merupakan salah satu sumber pengetahuan bagi pembaca, khususnya anak. Ketika buku itu dibaca, anak akan berusaha memaknai setiap kata dan kalimat di dalamnya. Terdapat permasalahan ketika ditemukan kesalahan konten pada buku tersebut. Konten yang dimaksud yaitu kata maupun kalimat yang memiliki makna kurang sopan, seksual, serta kata kasar. Bagi anak-anak di tingkat sekolah dasar konten tersebut menjadi hal yang bermakna gereflekter (tabu). Berdasarkan permasalah tersebut, maka dilakukan penelitian tugas akhir terhadap cerita anak yang diambil dari buku fiksi dan buku pelajaran. Penelitian ini dilakukan dengan membangun sistem untuk mendeteksi konten gereflekter pada teks cerita yang dijadikan sebagai dataset. Penelitian dilakukan dengan membangun model menggunakan algoritma klasifikasi teks k-Nearest Neighbor dengan pendekatan distance measure. Distance measure yang digunakan adalah Euclidean Distance dan Manhattan Distance. Sistem dievaluasi dengan menggunakan precision, recall, dan F1 score. Berdasarkan hasil evaluasi, skenario pengujian menggunakan Euclidean distance dan Manhattan distance mendapatkan nilai precision 0.915, recall 0.845, dan F1 score 0.895. Kata kunci : buku anak, distance measure, gereflekter, k-Nearest Neighbor Abstract Children's books are one source of knowledge for readers, especially children. When the book is read, the child will try to make sense of every word and sentence in it. There was a problem when a content error was found in the book. The content in question is words and sentences that have meanings that are not polite, sexual, and rude words. For children at the elementary school level, the content becomes meaningful reflectivity (taboo). Based on these problems, a final assignment research was carried out on children's stories taken from fiction books and textbooks. This research was conducted by building a system for detecting reflector content on story text that is used as a dataset. The study was conducted by building a model using the k-Nearest Neighbor text classification algorithm with a distance measure approach. Distance measure used is Euclidean Distance and Manhattan Distance. The system is evaluated using precision, recall, and F1 score. Based on the evaluation results, testing scenarios using Euclidean distance and Manhattan distance get a precision value of 0.915, recall 0.845, and F1 score 0.895. Keywords: children’s book, distance measure, gereflekter, k-Nearest Neighbor
Klasifikasi Teks Artikel Berita Hoaks Covid-19 Dengan Menggunakan Algotrima K-nearest Neighbor Berlian Kaida Palma; Danang Triantoro Murdiansyah; Widi Astuti
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Peran internet dan pertumbuhan informasi yang diberitakan di media sosial membuat perkembangan dan penyebaran berita semakin mudah, begitu pun dalam mengaksesnya. Pada masa pandemi Covid-19 saat ini banyak sekali berita yang tersebar sehingga masyarakat luas mencari atau mendapat informasi tentang virus ini. Berita berjudul Covid-19 ini banyak berisi informasi tidak penting bahkan memberitakan informasi hoaks. Hal ini membuat masyarakat internasional khususnya Indonesia resah akan berita yang beredar selama masa Covid-19. Oleh karena itu, penulis membuat sebuah model sistem untuk melakukan klasifikasi berita yang sesuai terjadi di lapangan. Informasi yang tersebar di media sosial sangat variatif sehingga banyak berita yang tidak penting bahkan berisikan informasi hoaks. Klasifikasi berita akan dilakukan dengan K-Nearest Neighbor (KNN). Berita yang ada dibagi menjadi beberapa kelas berdasarkan kategorinya, kemudian berita dilakukan klasifikasi teks dengan metode K-Nearest Neighbor (KNN) dan k-fold cross validation sebagai validasi model yang dibuat. Proses klasifikasi dilakukan dengan skema menggunakan 80% data train dan 20% data test serta mengubah parameter nilai k pada K-Nearest Neighbor dengan k = 3, k = 5, k = 7, k = 9, dan pada k-fold cross validation sebanyak k = 5 dan k = 10. Untuk evaluasi digunakan confusion matrix. Akhirnya, dari setiap model yang dilakukan dengan mengubah nilai k pada K-Nearest Neighbor didapatkan hasil akurasi terbaik dengan F1-Score sebesar 48% dari nilai k = 5, hasil validasi dari k-fold cross validation k = 5 sebesar 42% dan k = 10 sebesar 45%. Kata Kunci : Covid-19, K-Nearest Neighbor, Hoax, intenet, klasifikasi.
Analisis Perbandingan Klasifikasi Microarray Menggunakan Naïve Bayes Dan Support Vector Machine (svm) Untuk Deteksi Kanker Dengan Feature Extraction Pca Vina Mutiara Purnama; Widi Astuti; Adiwijaya Adiwijaya
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Kanker merupakan salah satu penyebab kematian manusia terbanyak di dunia. Diperkirakan penderita kanker terus meningkat setiap tahunnya. Kanker yang dapat terdeteksi lebih dini memiliki probabilitas lebih tinggi untuk mendapatkan penanganan yang lebih cepat dan tepat. Salah satu caranya dengan menggunakan teknologi Microarray. Teknologi Microarray dapat menganalisis ribuan profil gene expression dalam waktu yang bersamaan. Dengan melakukan analisa terhadap data Microarray selanjutnya dapat diketahui apakah seseorang terkena kanker atau tidak. Namun, permasalahan dalam data Microarray adalah jumlah atribut yang jauh lebih banyak dibandingkan sampel sehingga perlu dilakukannya reduksi dimensi. Untuk mengatasi hal tersebut, penulis menggunakan salah satu teknik reduksi dimensi yaitu Principal Component Analysis (PCA) dan menggunakan 2 metode klasifikasi yaitu Naïve Bayes dan Support Vector Machine (SVM), yang selanjutnya akan dibandingkan dan dianalisa hasil performansi dari kedua metode tersebut untuk mencari mana yang lebih baik. Akurasi dari hasil penelitian ini menunjukkan 4 dari 5 data kanker mendapatkan akurasi sebesar 77-96% sedangkan 1 data lainnya yaitu data breast cancer mendapatkan akurasi terkecil yaitu 54.6%. Kata kunci : Kanker, Microarray, Reduksi Dimensi, Principal Component Analysis (PCA), Naïve Bayes, Support Vector Machine (SVM).
Single-Label and Multi-Label Text Classification using ANN and Comparison with Naïve Bayes and SVM M. Mahfi Nurandi Karsana; Kemas Muslim L.; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6024

Abstract

Machine learning has become useful in daily life thanks to improvements in machine learning techniques. Text classification as an important part in machine learning. There are already many methods used for text classification such as Artificial Neural Network (ANN), Naïve Bayes, SVM, Decision Tree etc.  ANN is a branch in machine learning which approximate the function of natural neural network. ANN have been used extensively for classification. In this research a simple architecture of ANN is used. But it needs to be pointed out that the architecture used in this research is relatively simple compared to the cutting edge in ANN development and research to show the potential that ANN have compared to other classification method. ANN, Naïve Bayes and SVM performance are measured using f1-macro. Performance of classification model is measured of multiple single-label and multi-label dataset. This research found that in single-label classification ANN have a comparable f1-macro with 0.79 compared to 0.82 for SVM. In multi-label classification ANN have the best f1-macro with 0.48 compared to 0.44 in SVM.
Pelatihan media e-learning classroom untuk guru SMKN 1 Peureulak Timur Ichwanul Muslim Karo Karo; Widi Astuti; Ramanti Dharayani
TEKMULOGI: Jurnal Pengabdian Masyarakat Vol 2, No 2 (2022): November 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3755.966 KB) | DOI: 10.17509/tmg.v2i2.48963

Abstract

One of the sectors affected by the covid-19 pandemic is the education sector. SMKN 1 Peureulak Timur as the only vocational high school in Peureulak Timur sub-district, East Aceh must continue to provide education services for all students of the best quality. One of the efforts to improve educating and teaching skills for teachers at SMKN 1 Peureulak Timur is by providing e-learning training. One of the e-learning media that is often used is Goggle Classroom because of its complete features and quite easy to operate. Therefore, the activity that will be carried out at this community dedication is Google Classroom training. It is hoped that after receiving this Google Classroom training, teachers of SMKN 1 Peureulak Timur can improve their performance in facing the Industrial Revolution 4.0.
Sentiment Analysis of Practo Application Reviews Using Naïve Bayes and TF-IDF Methods Rizal Adi Putranto; Mahendra Dwifebri Purbolaksono; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6311

Abstract

Entering the 4.0 era, it seems that the healthcare industry is the one most likely to benefit from the combination of physical, digital and biological systems. Digital health applications or telemedicine have experienced significant growth in recent years. In the current era, the development of telemedicine is accelerating, one of which is the Practo application. As the number of users using this app increases, it is important to get their opinions in order to improve the health services provided by the app. Therefore, sentiment analysis of the comments regarding the health services on the app is necessary to find out the users' opinions. By utilizing sentiment analysis, it is possible to use the sentiment analysis results obtained as a sample that corresponds to both positive and negative comments. In addition, it can be revealed that there is a mismatch between the ratings and comments given by users. This information has the benefit of being able to improve the Practo application and improve the health services provided to more effectively meet the needs and expectations of users. This research employs the Naïve Bayes approach for sentiment analysis, utilizing TF-IDF feature extraction. Naïve Bayes was chosen because it is known as an efficient classification algorithm but has a high level of accuracy. This approach involves utilizing the Bayes rule formula to calculate probabilities and make classifications. It is applicable for solving classification problems that involve either numeric or nominal feature data. Meanwhile, TF-IDF was chosen because it can associate each word in a document with a numerical value that reflects its level of relevance to the document. TF-IDF is used to measure the weighting of words as features in the summary. In this study, the best model achieved a performance with an f1-score of 85.50%.
Sentiment Analysis using Random Forest and Word2Vec for Indonesian Language Movie Reviews Fahriza Ichsani Rafif; Mahendra Dwifebri Purbolaksono; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6299

Abstract

The film industry in recent years has become one of the industries that people are most interested in. The convenience of watching movies through streaming services is one of the reasons why watching movies is so popular. This ease of access resulted in a large selection of available movies and encouraged the public to look for movie reviews to find out whether the movies was good or bad. Freedom of expression on the internet has resulted in many movie reviews being spread. Therefore, sentiment analysis was conducted to see the positive or negative of these reviews. The method used in this research is Random Forest and Word2Vec skip-gram as feature extraction. The Random Forest classification was chosen because Randomforest is a highly flexible and highly accurate method, while Word2Vec Skip-Gram is used as a feature extraction because it is an efficient model that studies a large number of word vectors in an irregular text. The best model obtained from this experiment is a model built with stemming, Word2Vec with 300 dimensions, and a max_depth value of 23, achieving an f1-score of 83.59%.
Co-Authors Adiwijaya Afrian Hanafi Anisa Herdiani Anisa Nur Aini Aprillia Rizki Adiati Artamira Rizqy Amartya Maden Arya Rafif Muhammad Fikri Berlian Kaida Palma Bety Elysabeth Pasaribu Bintang Peryoga Dana Sulistiyo Kusumo Danang Triantoro Murdiansyah Daniel Tanta Christopher Sirait Donni Richasdy Fahriza Ichsani Rafif Ghozy Ghulamul Afif Hendro Prasetyo I Kadek Ananda Prana Widya I.G.N.P.Vasu Geramona Ichwanul Muslim Karo Karo Ilham Kurnia Syuriadi Ilham Yunirakhman Jondri Jondri Kemas Muslim Lhaksmana M. Mahfi Nurandi Karsana Mahendra Dwifebri Purbolaksono Mayya Tania Wewengkang Moch Arif Bijaksana Mochamad Agusta Naofal Hakim Mohamad Irwan Afandi Mubaroq Iqbal Muhammad Hanafiah Muhammad Naufal Mukhbit Amrullah Munirsyah Munirsyah Ragil Haditira Ramanti Dharayani Rizal Adi Putranto Rizky Dhian Syarif Salsabila Putri Adityani Sarah Suryaningsih Sean Akbar Ryanto Shidqi Aqil Naufal Suryaningsih, Sarah Syifa Khairunnisa Salsabila Talitha Kayla Amory Tiara Fitri Berlian Valentino Rossi Fierdaus Vina Mutiara Purnama Yahya Setiawan