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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Informatika dan Rekayasa Elektronik JAIA - Journal of Artificial Intelligence and Applications The Indonesian Journal of Computer Science
Lusiana Efrizoni
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm Rizky Afrinanda; Lusiana Efrizoni; Wirta Agustin; Rahmiati Rahmiati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2640

Abstract

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.
DESIGNING UI FOR STUDENT PRESENCE MOBILE APPLICATIONS USING THE HCD METHOD Josua Hutasoit; Vanda Rohmatulloh; Nazlah Sari Putantri; Lusiana Efrizoni
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 1 (2023): Desember 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2816

Abstract

 Abstract: Student attendance is one of the activities carried out in the lecture process. Attendance is also an element of a student's final grade for each course offered in accordance with a university's academic guidelines. Attendance at STMIK Amik Riau is done manually, where students are called one by one and then the lecturer changes the attendance data in SIMDOS. This method is of course very inefficient, and takes a long time. Apart from that, this manual process is prone to fraud where students can make absences. Therefore, this research proposes a QR-Code based student attendance mobile application prototype at STMIK Amik Riau using the User Centered Design (UCD) method. The results of the research are in the form of a prototype or user interface design for the student attendance mobile application. It is hoped that this prototype can help programmers to build QR-Code based student attendance mobile applications. Keywords: Presence, QR-Code, User Interface, User Centered Design =Abstrak: Kehadiran atau presensi mahasiswa merupakan salah satu kegiatan yang dilakukan dalam proses perkuliahan. Presensi juga merupakan salah satu elemen nilai akhir mahasiswa setiap mata kuliah yang ditawarkan sesuai dengan panduan akademik suatu perguruan tinggi. Presensi di STMIK Amik Riau dilakukan secara manual, dimana mahasiswa dipanggil satu per satu lalu dosen merubah data presensi yang ada pada SIMDOS. Cara ini tentunya sangat tidak efisien, dan membutuhkan waktu yang lama. Selain itu proses manual ini rentan terjadi kecurangan dimana mahasiswa dapat melakukan titip absen. Oleh karena itu penelitian ini mengusulkan prototype aplikasi mobile presensi mahasiswa berbasis QR-Code di STMIK Amik Riau menggunakan metode User Centered Design (UCD). Hasil dari penelitian berupa prototype atau rancangan user interface aplikasi mobile presensi mahasiswa. Diharapkan prototype ini dapat membantu programmer untuk membangun aplikasi mobile presensi mahasiswa berbasis QR-Code. Kata kunci: Presensi, QR-Code, User Interface, User Centered Design 
Detection Of Malaria Parasites In Human Blood Cells Using Convolutional Neural Network Lusiana Efrizoni; Rais Amin; Ahmad Rizali
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.947

Abstract

Malaria is a blood disease caused by the Plasmodium parasite which is transmitted by the bite of the female Anopheles mosquito. The diagnosis of malaria is carried out by a microscopist through examination of human blood cells. Their level of accuracy depends on the quality of the tool, expertise in classifying and counting infected and uninfected parasite cells. The disadvantages of examining this way include the difficulty in making a diagnosis on a large scale and the poor quality of the results. The dataset used in model evaluation is a dataset developed by LHNVBC which contains 27,558 cell image data. The malaria dataset will be processed through data science processing using a Convolutional Neural Network with the ResNet architecture. The model will conduct training on the dataset and then the model will be able to recognize malaria parasites in human blood cells. The model will be trained by optimizing multinomial logistic regression using Stochastic Gradient Descent (SGD) and Nesterov momentum values. The results of training data validation accuracy from model training with 50 epochs were obtained at 96.23% and 97% after being tested on data testing.
KOMPARASI ALGORITMA K-NEAREST NEIGHBOR (K-NN), SUPPORT VECTOR MACHINE (SVM), DAN DECISION TREE DALAM KLASIFIKASI PENYAKIT STROKE Andri Setiawan; Febrio Waleska, Rangga; Muhammad Adji Purnama; Rahmaddeni; Lusiana Efrizoni
Jurnal Informatika dan Rekayasa Elektronik Vol. 7 No. 1 (2024): JIRE APRIL 2024
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v7i1.1161

Abstract

Penyakit stroke adalah penyebab kematian dan kecacatan pada manusia. Stroke dapat menyebabkan aliran darah otak terganggu. Ini menghambat semua fungsi organ, menyebabkan organ-organ tertentu kehilangan elastisitas, oksigen, atau nutrisi, dan akhirnya menyebabkan mereka mati dengan cepat. Tujuan dari penelitian ini adalah untuk mengklasifikasi  seseorang yang mengalami stroke. Dalam penelitian ini, metode K-NN mempunyai kemampuan untuk menangani masalah yang kompleks tanpa terpengaruh oleh berbagai faktor dan sifatnya yang kuat, intensif, dan tidak asumsif, metode SVM memiliki kemampuan untuk menangani masalah yang kompleks tanpa terpengaruh oleh berbagai factor dan melakukan pelatihan secara efektif, sedangkan Decision Tree mengembangkan pengetahuan berdasarkan data pelatihan dan labelnya, melakukan prediksi terkait kategori atau label kelas. Dari metode-metode tersebut akan melakukan klasifikasi pada data penyakit stroke dari 4981 record. Hasil pengujian metode dengan spliting data 80:20 menunjukkan bahwa metode K-NN mendapatkan hasil akurasi 94%, dan SVM mendapatkan hasil akurasi 95%, sedangkan Decision Tree mendapatkan hasil 92%. Dari hasil tersebut metode SVM lebih baik dibandingkan dua metode K-NN dan Decision Tree. Studi ini menggunakan Streamlite untuk membuat visualisasi data menjadi lebih menarik.
OPTIMASI TEKNIK VOTING PADA SENTIMEN ANALISIS PEMILIHAN PRESIDEN 2024 MENGGUNAKAN MACHINE LEARNING Kharisma Rahayu; M. Khairul Anam; Lusiana Efrizoni; Nurjayadi; Triyani Arita Fitri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4119

Abstract

The presidential election is an important event in the democratic system of the Unitary State of the Republic of Indonesia or NKRI held every five years. There are many pros and cons of the presidential election, especially on social media Twitter or X. X is one of the media platforms where people leave positive, neutral, and even negative comments. Therefore, this research aims to build a sentiment analysis model to classify the sentiment of the 2024 presidential election. This research uses the Support Vector machine, Naïve Bayes and Decision Tree algorithms in performing classification with the addition of the Syntethic Minority Over-Sampling and Ensemble Voting methods. The test results show that public sentiment towards the presidential election dominates negative sentiment of 5008 positive 3582 and neutral 1411 sentiments. Then the results of data processing SVM, NB and DT algorithms plus SMOTE and ensemble voting optimization, provide 92.8% accuracy, 93% precision, 93% recall and 93% F1-Score. This research can make a significant contribution by classifying public sentiment towards the 2024 presidential election data.
Co-Authors Ahmad Rizali Amin Rais Anam, M Khairul Andri Setiawan Febrio Waleska, Rangga Josua Hutasoit Kharisma Rahayu Muhammad Adji Purnama Nazlah Sari Putantri Nurjayadi Nurjayadi Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rizky Afrinanda Triyani Arita Fitri Vanda Rohmatulloh Wahyu Tawa Bagus Wirta Agustin