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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sains dan Informatika International Journal of New Media Technology Jurnal ULTIMATICS JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Alethea Suryadibrata
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Classification of pneumonia from X-ray images using siamese convolutional network Kennard Alcander Prayogo; Alethea Suryadibrata; Julio Christian Young
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14751

Abstract

Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images.
Review of Various A* Pathfinding Implementations in Game Autonomous Agent Alethea Suryadibrata; Julio Cristian Young; Richard Luhulima
IJNMT (International Journal of New Media Technology) Vol 6 No 1 (2019): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1728.215 KB) | DOI: 10.31937/ijnmt.v6i1.1075

Abstract

Among many pathfinding algorithms, A* search algorithm is an algorithm that most commonly used in grid-based pathfinding. This is due to implementation of A* search which proven to be able to generate the optimal path in a relatively short time by combining two characteristics of Djikstra's and best-first search algorithm. In the implementation of A* search, the selection of heuristic function and data structure can affect the performance of the algorithm. The purpose of this research is to find the best heuristic function and data structure with regards of the performance in A* search implementation as a pathfinding algorithm in a 3D platform. In the experiment, some known heuristic functions and data structures will be tested on the various 3D platform with a different size and obstacle percentage. Based on experiment that have been done, euclidean squared distance is best heuristic function for 3D pathfinding problem, with regards of the implementation performance. In addition, we also found that binary heap is the best data structure to be implemented for 3D pathfinding problem, with regards of implementation performance.
A Comparison of Traditional Machine Learning Approaches for Supervised Feedback Classification in Bahasa Indonesia Andre Rusli; Alethea Suryadibrata; Samiaji Bintang Nusantara; Julio Christian Young
IJNMT (International Journal of New Media Technology) Vol 7 No 1 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.434 KB) | DOI: 10.31937/ijnmt.v1i1.1485

Abstract

The advancement of machine learning and natural language processing techniques hold essential opportunities to improve the existing software engineering activities, including the requirements engineering activity. Instead of manually reading all submitted user feedback to understand the evolving requirements of their product, developers could use the help of an automatic text classification program to reduce the required effort. Many supervised machine learning approaches have already been used in many fields of text classification and show promising results in terms of performance. This paper aims to implement NLP techniques for the basic text preprocessing, which then are followed by traditional (non-deep learning) machine learning classification algorithms, which are the Logistics Regression, Decision Tree, Multinomial Naïve Bayes, K-Nearest Neighbors, Linear SVC, and Random Forest classifier. Finally, the performance of each algorithm to classify the feedback in our dataset into several categories is evaluated using three F1 Score metrics, the macro-, micro-, and weighted-average F1 Score. Results show that generally, Logistics Regression is the most suitable classifier in most cases, followed by Linear SVC. However, the performance gap is not large, and with different configurations and requirements, other classifiers could perform equally or even better.
Cyberbullying Sentiment Analysis with Word2Vec and One-Against-All Support Vector Machine Lionel Reinhart Halim; Alethea Suryadibrata
IJNMT (International Journal of New Media Technology) Vol 8 No 1 (2021): 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.v8i1.2047

Abstract

Depression and social anxiety are the two main negative impacts of cyberbullying. Unfortunately, a survey conducted by UNICEF on 3rd September 2019 showed that 1 in 3 young people in 30 countries had been victims of cyberbullying. Sentiment analysis research will be conducted to detect a comment that contains cyberbullying. Dataset of cyberbullying is obtained from the Kaggle website, named, Toxic Comment Classification Challenge. The pre-processing process consists of 4 stages, namely comment generalization (convert text into lowercase and remove punctuation), tokenization, stop words removal, and lemmatization. Word Embedding will be used to conduct sentiment analysis by implementing Word2Vec. After that, One-Against-All (OAA) method with the Support Vector Machine (SVM) model will be used to make predictions in the form of multi labelling. The SVM model will go through a hyperparameter tuning process using Randomized Search CV. Then, evaluation will be carried out using Micro Averaged F1 Score to assess the prediction accuracy and Hamming Loss to assess the numbers of pairs of sample and label that are incorrectly classified. Implementation result of Word2Vec and OAA SVM model provide the best result for the data undergoing the process of pre-processing using comment generalization, tokenization, stop words removal, and lemmatization which is stored into 100 features in Word2Vec model. Micro Averaged F1 and Hamming Loss percentage that is produced by the tuned model is 83.40% and 15.13% respectively. Index Terms— Sentiment Analysis; Word Embedding; Word2Vec; One-Against-All; Support Vector Machine; Toxic Comment Classification Challenge; Multi Labelling
Peningkatan Kemampuan Membuat Surat dan Laporan Menggunakan Ms.Word pada Siswa MA Raudatul Irfan Adhi Kusnadi; Marlinda Vasty Overbeek; Yaman Khaeruzzaman; Moeljono Widjaja; Alethea Suryadibrata
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol 1 No 2 (2020)
Publisher : Politeknik Dharma Patria

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v1i2.341

Abstract

Kegiatan pelatihan ini bertujuan untuk meningkatkan kemampuan siswa/siswi MA Raudatul Irfan dalam penggunaan MS.Office, terutama MS.Word. Peningkatan kemampuan siswa/siswi akan meningkatkan peluang mereka untuk memasuki jenjang pendidikan lebih tinggi dan dunia kerja. Kegiatan ini dilaksanakan selama dua hari dimulai dari pagi jam 8.00 WIB sampai selesai. Banyaknya jumlah siswa/siswi yaitu berjumlah 80 orang ditambah staf dan guru sebanyak 10 orang, maka untuk meningkatkan efektifitas dalam pelatihan di laboratorium Kampus UMN, peserta dibagi dua kelompok sebanyak 40 orang untuk setiap kelasnya. Metode yang digunakan dalam pelatihan ini adalah demontrasi dan contoh. Pada awal sesi peserta akan diperkenalkan pada komputer, sistem operasi, dan MS.Office, kemudian diakhiri dengan evaluasi dan latihan. Dengan mengikuti pelatihan ini, siswa/siswi akan meningkat kemampuannya dalam menggunakan komputer terutama MS.Office dengan tujuan utama membuat surat dan laporan menggunakan MS.Word. Secara keseluruhan kegiatan ini berlangsung dengan lancar dan baik, dan hasil latihan/evaluasi yang baik dengan rata-rata indeks prestasi 2,7 dari skala 1-4
Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine Alethea Suryadibrata; Suryadi Darmawan Salim
Ultimatics : Jurnal Teknik Informatika Vol 11 No 1 (2019): 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 (1490.611 KB) | DOI: 10.31937/ti.v11i1.1076

Abstract

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F-score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.
Rekomendasi Pemilihan Mobil dengan Algoritma VIKOR Brian Kristianto; Alethea Suryadibrata; Seng Hansun
Jurnal Sains dan Informatika Vol. 7 No. 1 (2021): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v7i1.269

Abstract

Penjualan mobil di Indonesia terus meningkat tiap tahunnya. Pada tahun 2009 terdapat 7,9 juta unit mobil di Indonesia dan pada 2016 telah mencapai 14,6 juta unit. Peningkatan penjualan kendaraan disebabkan oleh meningkatnya permintaan pasar. Namun demikian, tidak semua calon pembeli mengetahui mobil mana yang akan dibeli dan yang sesuai kebutuhannya. Peningkatan permintaan dari konsumen juga menyebabkan munculnya tipe-tipe mobil dengan kriteria inovatif untuk menarik calon pembeli. VIKOR merupakan algoritma yang cukup baik dalam pemecahan sistem rekomendasi dengan multi criteria. Oleh karenanya, algoritma VIKOR diterapkan di sini dengan menggunakan PHP dan framework CodeIgniter. Uji coba dilakukan dengan meminta tiga puluh responden untuk mengisi kuesioner EUCS yang mengukur kepuasan pengguna terhadap sistem yang dibangun. Hasil uji coba mendapatkan nilai persentase EUCS sebesar 77,167% yang tergolong dalam predikat baik.
Co-Authors Adhi Kusnadi Andre Rusli Brian Kristianto Julio Christian Young Julio Christian Young Julio Cristian Young Kennard Alcander Prayogo Lionel Reinhart Halim Marlinda Vasty Overbeek Moeljono Widjaja Richard Luhulima Samiaji Bintang Nusantara Seng Hansun Suryadi Darmawan Salim Yaman Khaeruzzaman