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All Journal International Journal of New Media Technology Jurnal ULTIMATICS Jurnal ULTIMA Computing Jurnal Informatika: Jurnal Pengembangan IT
Alethea Suryadibrata
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Visualisasi Algoritma sebagai Sarana Pembelajaran K-Means Clustering Alethea Suryadibrata; Julio Christian Young
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.166 KB) | DOI: 10.31937/ti.v12i1.1523

Abstract

Algorithm Visualization (AV) is often used in computer science to represents how an algorithm works. Educators believe that visualization can help students to learn difficult algorithms. In this paper, we put our interest in visualizing one of Machine Learning (ML) algorithms. ML algorithms are used in various fields. Some of the algorithms are used to classify, predict, or cluster data. Unfortunately, many students find that ML algorithms are hard to learn since some of these algorithms include complicated mathematical equations. We hope this research can help computer science students to understand K-Means Clustering in an easier way.
Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average Sherly Florencia; Alethea Suryadibrata
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1831

Abstract

Tourism is an important factor for the development of a country. Tourism can be used as a promotion to introduce natural beauty and cultural uniqueness. Government needs to predict how many tourists will come every year to do a planning. Therefore, an application is needed to help to predict the arrival of tourists in each country. In this paper, we use Weighted Exponential Moving Average (WEMA) method to predict the arrival of tourist, tourism expenditure in the country, and departure using data from 2008 to 2018. Error measurement is calculated using the Mean Absolute Percentage Error (MAPE). The result shows that the lowest average MAPE on arrival data with span 2 is at 3.28. The lowest average MAPE on tourism expenditure data with span 2 is at 3.99%. The result shows that the lowest average MAPE on departure data with span 2 is at 3.63%.
Implementasi Algoritma Complement dan Multinomial Naïve Bayes Classifier Pada Klasifikasi Kategori Berita Media Online Muhammad Naufal Randhika; Julio Christian Young; Alethea Suryadibrata; Hadian Mandala
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.1921

Abstract

Perkembangan teknologi dan penyebaran informasi di internet terus mengalami peningkatan. Salah satu bentuk informasi yang jumlahnya terus bertambah adalah berita. Media cetak dan elektronik yang kini telah dikemas dalam bentuk digital atau sering dikenal dengan portal berita online atau media online. PT Merah Putih Media merupakan media berita online. Berita yang disampaikan terdiri dari tiga kategori mulai dari berita tentang Indonesia, Hiburan dan Gaya Hidup, serta Olahraga. Namun, pembagian artikel berita ke dalam kategori dilakukan secara manual oleh kepala redaksi jurnalis. Text Mining adalah salah satu teknik yang dapat digunakan untuk melakukan klasifikasi sebuah dokumen. Pada penelitian ini dilakukan klasifikasi kategori otomatis dengan algoritma Multinomial Naïve Bayes, Complement Naïve Bayes, dan gabungan kedua model. Model yang memiliki performa terbaik dinilai dari metrik F1-Score dengan jumlah pembagian data latih dan data uji sebanyak 80:20, diperoleh keberhasilan performa sebesar 90,13% F1-Score.
Implementasi Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Karakter pada Dokumen Tercetak Alethea Suryadibrata; Dian Puspita Chandra
Ultima Computing : Jurnal Sistem Komputer Vol 11 No 2 (2019): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (734.748 KB) | DOI: 10.31937/sk.v11i2.1456

Abstract

Digital documents from the scanner device cannot be edited. To be able to edit digital documents, Optical Character Recognition (OCR) technology is needed. This research was conducted with the aim of implementing backpropagation artificial neural networks in printed documents and to find out how the accuracy of the implementation of backpropagation artificial neural networks in printed documents. This research uses multilayer networks with three layers. The input layer consists of 225 nodes with 15 × 15 pixels digital image as input, hidden layer consists of 110 nodes, and the output layer consists of 54 nodes representing A-Z, a-z, point punctuation (.), and comma punctuation (,). The learning rate used in this research is 0,29. The average accuracy level obtained from the implementation of backpropagation artificial neural networks in this research was 94 % for Ms Arial Unicode font type, 96,6 % for Tahoma font type, and 94 % for Times New Roman font type.
Predicting the Case of COVID-19 in Indonesia using Neural Prophet Model Efraim Yahya Wijaya; Alethea Suryadibrata
IJNMT (International Journal of New Media Technology) Vol 9 No 2 (2022): IJNMT : International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v9i2.2821

Abstract

Since the initial entry of the COVID-19 virus in Indonesia, the spread of cases increased significantly. This has a huge impact on hospitals that serve chronic COVID-19 patient. There are several ways that the government used to suppresses the spread of COVID-19 case in Indonesia, such as making PPKM policies and providing vaccinations. Through this research, the prediction method is used to find increasing and decreasing of COVID-19 cases using the Neural Prophet model. Then the model will be compared with the Facebook Prophet as comparison model. In this study dataset is used from (covid19.go.id) which was taken on 23 June 2022 with scraping technique. The results of this study indicate that the Neural Prophet model has better value in RMSE, and MAE compared to the Facebook Prophet.
Implementasi Algoritma Naïve Bayes untuk Klasifikasi Konten Twitter dengan Indikasi Depresi Andre Budiman; Julio Christian Young; Alethea Suryadibrata
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 2 (2021): JPIT, Mei 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i2.2419

Abstract

Depresi merupakan salah satu permasalahan kesehatan yang sangat berdampak bagi para penderitanya. Terdapat begitu banyak faktor depresi, di antaranya pengalaman hidup, pekerjaan, ataupun kehidupan sosial. Pada tahun 2018, diperkirakan 6.1% dari 267.7 juta penduduk di Indonesia mengalami depresi. Hal ini tentunya sangat dipengaruhi oleh stigma masyarakat terkait dengan penyakit kejiwaan dan rendahnya tingkat kesadaran masyarakat untuk melakukan konsultasi kejiwaan. Melalui perkembangan teknologi, saat ini, mayarakat seringkali mengekspresikan dirinya melalui konten-konten di media sosial. Pada penelitian ini dilakukan proses pengumpulan data-data dengan kata kunci yang mengindikasikan gangguan depresi di platform Twitter. Kemudian, dengan melibatkan seorang psikiatri, dilakukan proses pelabelan terhadap dataset untuk menentukan apakah konten memiliki label “terindikasi depresi” ataupun “tidak terindikasi”. Berdasarkan dataset tersebut, dikembangkan model prediktif dengan menggunakan metode Multinomial Naïve Bayes (MNB) dan Complement Naïve Bayes (CNB) sebagai metode klasifikasi dan metode Term Frequency–Inverse Document Frequency (TF–IDF) sebagai metode ekstraksi fitur. Berdasarkan eksperimentasi yang telah dilakukan gabungan metode TF–IDF dan MNB berhasil mencapai tingkat F-score sebesar 91.30% sementara gabungan metode TF–IDF dengan CNB berhasil mencapai tingkat performa sebesar 91.98%.
Co-Authors Andre Budiman Dian Puspita Chandra Efraim Yahya Wijaya Hadian Mandala Julio Christian Young Julio Christian Young Muhammad Naufal Randhika Sherly Florencia