Garuda - Garba Rujukan Digital

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

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.

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

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

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%.

Sentiment Analysis on Song Lyrics for Song Popularity Prediction Using BERT Agatha, Hana; Putri, Farica Perdana; Suryadibrata, Alethea
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3420

The increasingly competitiveness in music industry is giving some musicians disadvantages. Musicians need to pay more attention to the factors that influence the popularity of a song, so their song can be popular and they can gain a lot of profit. One of the various factors that can affect a song popularity is the lyrics. The influence of the lyrics can be explored through sentiment analysis. Sentiment analysis is a computing study that identify sentiments or emotions in a text. By conducting sentiment analysis on the lyrics, song popularity can be predicted. Based on the prediction result, songwriters can evaluate their lyrics, so their song can be popular. Bidirectional Encoder Representations from Transformers (BERT) is an excellent algorithm in terms of sentiment analysis. In this study, a BERT model was developed to predict the song popularity, based on the sentiment analysis of the song lyrics. The popularity class of a song will be predicted, based on the results of lyrics sentiment analysis. The developed model is a model that has been trained with English songs. Based on the experiment, the model that used oversampling method achieved accuracy by 87%, precision by 88%, recall by 87%, and f1-score by 87%.

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

Cross-Platform Mobile Based Crowdsourcing Application for Sentiment Labeling Using Gamification Method Elaine, Elaine; Putri, Farica Perdana; Suryadibrata, Alethea
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3935

Sentiment analysis is the application of natural language processing which aims to identify the sentiment of texts. To carry out sentiment analysis, data which has been labeled sentiment is needed to be included in the training model. Crowdsourcing is considered as the most optimal method to label data because it has a high level of accuracy at a relatively low cost. However, the use of crowdsourcing platforms has its own challenge, which is to increase user interest and motivation. A solution which can be applied is to design and build a crowdsourcing platform or application using the gamification method. The definition of gamification is an effort to increase one's intrinsic motivation for an activity by applying game elements to it. Therefore, a cross-platform mobile based crowdsourcing application for sentiment labeling using gamification method was carried out. The gamification design process was done based on the 6D framework and the application was developed using the Ionic-React framework. Application was examined through black box testing and the result showed that the application was functioning properly and according to the design requirements. There was also an evaluation carried out by distributing Intrinsic Motivation Inventory questionnaires to users who had used the application for 2 weeks. From a total of 40 respondents, the result showed that the level of user motivation and interest in using the application was high with a percentage of 83.10%.

Cross-Platform Mobile Based Crowdsourcing Application for Sentiment Labeling Using Gamification Method Elaine, Elaine; Putri, Farica Perdana; Suryadibrata, Alethea
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3935

Sentiment analysis is the application of natural language processing which aims to identify the sentiment of texts. To carry out sentiment analysis, data which has been labeled sentiment is needed to be included in the training model. Crowdsourcing is considered as the most optimal method to label data because it has a high level of accuracy at a relatively low cost. However, the use of crowdsourcing platforms has its own challenge, which is to increase user interest and motivation. A solution which can be applied is to design and build a crowdsourcing platform or application using the gamification method. The definition of gamification is an effort to increase one's intrinsic motivation for an activity by applying game elements to it. Therefore, a cross-platform mobile based crowdsourcing application for sentiment labeling using gamification method was carried out. The gamification design process was done based on the 6D framework and the application was developed using the Ionic-React framework. Application was examined through black box testing and the result showed that the application was functioning properly and according to the design requirements. There was also an evaluation carried out by distributing Intrinsic Motivation Inventory questionnaires to users who had used the application for 2 weeks. From a total of 40 respondents, the result showed that the level of user motivation and interest in using the application was high with a percentage of 83.10%.

Sentiment Analysis on Song Lyrics for Song Popularity Prediction Using BERT Agatha, Hana; Putri, Farica Perdana; Suryadibrata, Alethea
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3420

The increasingly competitiveness in music industry is giving some musicians disadvantages. Musicians need to pay more attention to the factors that influence the popularity of a song, so their song can be popular and they can gain a lot of profit. One of the various factors that can affect a song popularity is the lyrics. The influence of the lyrics can be explored through sentiment analysis. Sentiment analysis is a computing study that identify sentiments or emotions in a text. By conducting sentiment analysis on the lyrics, song popularity can be predicted. Based on the prediction result, songwriters can evaluate their lyrics, so their song can be popular. Bidirectional Encoder Representations from Transformers (BERT) is an excellent algorithm in terms of sentiment analysis. In this study, a BERT model was developed to predict the song popularity, based on the sentiment analysis of the song lyrics. The popularity class of a song will be predicted, based on the results of lyrics sentiment analysis. The developed model is a model that has been trained with English songs. Based on the experiment, the model that used oversampling method achieved accuracy by 87%, precision by 88%, recall by 87%, and f1-score by 87%.

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

Fine-tuning convolutional neural network for artificial intelligence generated image detection enhancement Steven Vincent Hendrawan; Moeljono Widjaja; Alethea Suryadibrata
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2238-2246

The relationship between art and technology has changed how people engage with creativity, leading to the industrialization of the field. Various digital media have been utilized in the endeavour of art creation, such as artificial intelligence (AI) generation for images. The utilization of AI-generated art has yielded negative reactions due to its exploitative nature on pre-existing artworks without the creator’s consent, which raises plagiarism concerns. This research utilized convolutional neural network (CNN) to help detect such images to reduce public concerns on the abuse of AI images. The algorithm is proposed to detect such images as it involves spatial convolution within two-dimensional spaces, matching the nature of images. The model was developed from pre-existing architectures, namely EfficientNetB1 and Xception, which was pre-trained on ImageNet classification task with the modification of inclusion or exclusion of dropout in the top layer. After assessing the models, removing top layer dropout from EfficientNetB1 model improved it to reach the F1-score of 97.66% compared to 97.44% in the base model and Xception with a dropout layer yields lower F1-score of 95.56% compared to 97.07% in the base model.

Title

Found 20 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 20 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 20 Documents
Search