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All Journal Jurnal Teknik Informatika Jurnal EECCIS Applied Information System and Management Jurnal Teknologi Terpadu
Kambey, Feisy Diane
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Pemanfaatan Plaform Pemrograman Daring dalam Pembelajaran Probabilitas dan Statistika di Masa Pandemi CoVID-19 Sengkey, Daniel Febrian; Kambey, Feisy Diane; Lengkong, Salvius Paulus; Joshua, Salaki Reynaldo; Kainde, Henry Valentino Florensius
Jurnal Teknik Informatika Vol 15, No 4 (2020): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.15.3.2020.31685

Abstract

Sejak akhir tahun 2019, dunia menghadapi ancaman dari sebuah virus baru yang dikenal sebagai novel Corona virus, yang kemudian dinamai ulang sebagai SARS-CoV-2. Karena sifat penularannya, maka pembatasan sosial diambil sebagai langkah untuk memperlambat penyebaran virus tersebut. Segala sesuatu harus dilakukan dari jarak jauh, tanpa kontak fisik secara langsung, kecuali untuk kondisi-kondisi tertentu. Oleh karenanya, berbagai hal harus dilakukan dari rumah, termasuk belajar dan mengajar. Makalah ini membahas tentang penggunaan platform pemrograman daring dalam mendukung proses belajar-mengajar pada mata kuliah Probabilitas dan Statistika, yang diselenggarakan pada Program S1 Teknik Informatika, Universitas Sam Ratulangi, Manado.
Application For Prediction Of The Spread Of The Covid-19 Outbreak In Manado Vortuna, Vharadien Veronika; Jacobus, Agustinus; kambey, feisy diane
Jurnal Teknik Informatika Vol 16, No 3 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.16.3.2021.34195

Abstract

Prediction system or system that predicts is a system that is used to predict something that will happen in the future or the future. Currently, the whole world, especially the city of Manado, is in a situation where the Covid-19 virus outbreak has paralyzed various aspects. Therefore, this study aims to provide information on predicting the spread of COVID-19 in Manado. Because this disease spreads to all corners of society easily, of course it requires information about the development of its spread in the future so that people can know the developments about the spread of the disease. The research entitled "Applications  for Predicting the Spread of the Covid-19 Outbreak in Manado". This application can process covid-19 data that has been adjusted with the fb prophet method and use the Python programming language in order to get calculation results that can predict the spread of the covid-19 virus that will be infected in the future and will also display a prediction plot for the spread. This research will be displayed to the user in the form of a website application. Sistem prediksi atau sistem yang memperkirakan merupakan system yang di gunakan untuk  meramalkan sesuatu yang akan terjadi di masa mendatang atau masa yang akan datang. Saat ini seluruh Dunia terlebih khusus kota manado dalam situasi terserang wabah virus covid-19 yang melumpuhkan berbagai aspek. Oleh karena itu penelitian ini memiliki tujuan untuk memberikan informasi prediksi penyebaran covid-19 di Manado. Dikarenakan penyakit ini menyebar keseluruh penjuru masyarakat dengan mudah tentunya memerlukan informasi tentang perkembangan penyebarannya di masa yang akan datang agar supaya masyarakat dapat mengetahui perkembangan tentang penyebaran penyakit tersebut. Penelitian yang mengangkat judul “Aplikasi Prediksi Penyebaran Wabah Covid-19 Di Manado”. Aplikasi ini dapat mengolah data-data covid-19 yang telah di sesuaikan dengan metode fb prophet dan menggunakan bahasa pemrograman Python agar mendapatkan hasil perhitungan yang dapat memprediksi penyebaran virus covid-19  yang akan terjangkit dimasa yang mendatang dan juga akan menampilkan plot prediksi penyebaran. Penelitian ini akan di tampilkan kepada user dalam bentuk aplikasi website.
Aplikasi Pembelajaran Asmaul Husna Menggunakan Speech Recognition wisdhani, zul; Ontowirjo, Abdul Haris Junus; Kambey, Feisy Diane
Jurnal Teknik Informatika Vol 16, No 3 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.16.3.2021.34181

Abstract

Pendidikan agama merupakan hal yang sangat penting bagi kehidupan manusia. Dalam agama islam, umat muslim wajib untuk mengetahui Asmaul Husna. Secara Bahasa Asmaul husna memiliki arti nama-nama yang terbaik dan indah yang dimiliki Allah SWT. Nama – nama tersebut bukan hanya menunjukkan keindahan, tetapi juga mewakili kesempurnaan dan keangungan-Nya.Dalam islam terdapat berbagai aspek pembelajaran agama salah satunya yaitu pembelajaran untuk mengetahui 99 Asmaul husna. Saat ini sangat sedikit orang yang dapat mengetahui dan memahami penjelasan dari asmaul husna. Teknologi yang sudah berkembang dan banyaknya pengguna smartphone android dapat dijadikan sebagai media pembelajaran dengan mengimplementasikan speech recognition sebagai teknik pencocokkan suara untuk melatih pengucapan,mencari kata, dan menjawab soal evaluasi. Speech recognition berfungsi untuk mencocokkan suara yang dimasukkan pengguna kedalam aplikasi.Library yang digunakan dalam aplikasi ini adalah library google speech sehingga aplikasi harus terhubung dengan jaringan internet pada saat digunakan.Kata kunci — Asmaul Husna; Andorid; Google Speech API; Speech Recognition
Identifikasi Citra Penyakit Mata Katarak Menggunakan Convolutional Neural Network Bu'ulolo, Geza Jeremia; Jacobus, Agustinus; Kambey, Feisy Diane
Jurnal Teknik Informatika Vol 16, No 4 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.16.4.2021.34208

Abstract

Convolutional Neural Network architecture is part of deep learning. In this architecture has a biological process that has a pattern of connectivity between neurons resembling the visual cortex. In the output of this architecture in the form of predictions for identification in an image of a cataract eye and an image of a normal eye. Based on testing of the system on a web-based application, the system that was built succeeded in identifying cataract eye images and normal eye images with test data as much as 30% of the training data. From the tests carried out on parameter values and tests on the optimizer on the Convolutional Neural Network architecture method, the accuracy results with an average of 96.8% on the RMSProp optimizer, 94.5% on the Adam optimizer, 74.5% on the SGD optimizer, and 63.5% on the AdaDelta optimizer.Arsitektur Convolutional Neural Network merupakan bagian dari deep learning. Dalam arsitektur ini memiliki proses biologi yang memiliki pola konektivitas antar neurons menyerupai korteks visual.  Dalam hasil keluaran dari arsitektur ini berupa prediksi untuk identifikasi pada suatu citra mata katarak maupun citra mata normal. Berdasarkan pengujian terhadap sistem pada aplikasi berbasis web, sistem yang dibangun berhasil mengidentifikasi citra mata katarak dan citra mata normal dengan data uji sebanyak 30% dari data pelatihan. Dari pengujian yang dilakukan terhadap nilai parameter dan pengujian terhadap optimizer pada metode arsitektur Convolutional Neural Network mendapatkan hasil akurasi dengan rata-rata adalah 95% pada optimizer RMSProp, 94,5% pada optimizer Adam, 74,5% pada optimizer SGD, dan 63.5% pada optimizer AdaDelta.
Implementasi Bi-LSTM dengan Ekstraksi Fitur Word2Vec untuk Pengembangan Analisis Sentimen Aplikasi Identitas Kependudukan Digital Onsu, Romario; Sengkey, Daniel Febrian; Kambey, Feisy Diane
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1225

Abstract

The Indonesian government is striving to enhance digital public services, including the Digital Identity Application (IKD) launched in 2022 by the Directorate General of Population and Civil Registration. Since its launch, IKD has received various responses from the public. User reviews on Google Play Store indicate a decline in ratings from June to December 2023. Review analysis is essential to understand user satisfaction, identify issues, and guide application improvements. This study aims to perform sentiment analysis on IKD user reviews using Bidirectional Long Short-Term Memory (Bi-LSTM) and Word2Vec methods. Bi-LSTM and Word2Vec are used to develop sentiment analysis from previous research that still used Machine Learning methods. This research is expected to contribute to the development of sentiment analysis models using Deep Learning for the IKD application. Review data was collected from the Google Play Store using scraping techniques for the period January-December 2023 and categorized into positive and negative. The Bi-LSTM model was trained with Word2Vec CBOW and Skip-Gram variations with dimensions of 100, 200, and 300. The results show that the combination of Bi-LSTM and Word2Vec CBOW with a dimension of 200 and a data split ratio of 80/20 produced the highest accuracy of 96.06%, with a precision of 96.44%, recall of 95.64%, and an f1-score of 96.04%. All combinations of Bi-LSTM and Word2Vec outperformed other Machine Learning algorithms.
Implementation of Bidirectional Long Short-Term Memory and Convolutional Neural Network in Detecting Hoax Content on Social Media Lantang, Oktavian A.; Sendow, Raphael Edber Christopher; Kambey, Feisy Diane
Applied Information System and Management (AISM) Vol 8, No 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45222

Abstract

The advancement of internet technology has facilitated the spread of information, including false information or fake news. The dissemination of hoaxes on social media, such as Twitter, can cause confusion and negatively impact society. This study aims to implement a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) for hoax detection. The dataset used consists of English tweets containing both real and fake news, collected between 2020 and 2022, as provided by the TruthSeeker dataset. The model utilizes an embedding layer with word2vec, a Conv1D layer, and a BiLSTM layer to effectively capture temporal and spatial patterns in text data. Additionally, experiments were conducted by varying the number of BiLSTM units and CNN filters to analyze their impact on model performance. After conducting parameter experiments, the best results were achieved using a Conv1D layer with 64 filters and a BiLSTM layer with 64 neurons/units. The evaluation results on the test data indicate an accuracy of 96.14%, a precision of 96%, a recall of 96.25%, and an F1-score of 96%. These results demonstrate the model's high capability in accurately detecting hoaxes, which is significant for combating misinformation on social media. With its strong performance, the model has potential applications in real-time content moderation systems, early hoax detection tools, and digital literacy platforms to help reduce the spread of false information.
Implementation of Feature Extraction Using BERT in Aspect Based Sentiment Analysis Turangan, Andreas Dwi Putra; Jacobus, Agustinus; Kambey, Feisy Diane
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1770

Abstract

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique that not only identifies overall sentiment, but also reveals opinions on specific aspects of an entity. To facilitate computer processing, a numerical representation of words into vectors (word embedding) is used, where each word or phrase is mapped into a vector of dimension N. Although static embedding such as Word2Vec or GloVe has been widely used, these approaches have limitations in capturing the dynamic context essential for deep sentiment analysis. This research develops and tests several deep learning algorithms, namely CNN, Bi-LSTM, CNN+BiLSTM, and CNN+BiLSTM+Attention Mechanism, which initially use static embedding and then modified by integrating BERT as contextual embedding. The results show that the use of BERT improves sentiment prediction accuracy by 15% and aspect prediction accuracy by 11% compared to models with static embedding. In particular, the combination of BERT+CNN obtained the best accuracy, which was 94% for aspect prediction and the combination of BERT+CNN+BiLSTM+Attention Mechanism 87% for sentiment prediction. These findings demonstrate the significant potential of BERT integration in improving ABSA performance, which can be applied in social media opinion analysis and sentiment-based recommendation systems.
Implementation of Bidirectional Long Short-Term Memory and Convolutional Neural Network in Detecting Hoax Content on Social Media Lantang, Oktavian A.; Sendow, Raphael Edber Christopher; Kambey, Feisy Diane
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45222

Abstract

The advancement of internet technology has facilitated the spread of information, including false information or fake news. The dissemination of hoaxes on social media, such as Twitter, can cause confusion and negatively impact society. This study aims to implement a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) for hoax detection. The dataset used consists of English tweets containing both real and fake news, collected between 2020 and 2022, as provided by the TruthSeeker dataset. The model utilizes an embedding layer with word2vec, a Conv1D layer, and a BiLSTM layer to effectively capture temporal and spatial patterns in text data. Additionally, experiments were conducted by varying the number of BiLSTM units and CNN filters to analyze their impact on model performance. After conducting parameter experiments, the best results were achieved using a Conv1D layer with 64 filters and a BiLSTM layer with 64 neurons/units. The evaluation results on the test data indicate an accuracy of 96.14%, a precision of 96%, a recall of 96.25%, and an F1-score of 96%. These results demonstrate the model's high capability in accurately detecting hoaxes, which is significant for combating misinformation on social media. With its strong performance, the model has potential applications in real-time content moderation systems, early hoax detection tools, and digital literacy platforms to help reduce the spread of false information.
Co-Authors Abdul Haris Junus Ontowirjo Agustinus Jacobus Bu'ulolo, Geza Jeremia Daniel Febrian Sengkey Kainde, Henry Valentino Florensius Oktavian A. Lantang Onsu, Romario Salaki, Reynaldo Joshua salvius paulus lengkong Sendow, Raphael Edber Christopher Soetiman, Reyvaldo Vincentius Turangan, Andreas Dwi Putra Vecky Poekoel, Vecky Vortuna, Vharadien Veronika wisdhani, zul