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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer)
Mohammed Hafizh Al-Areef
UNIVERSITAS NEGERI MEDAN
Computer Science & IT

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SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMILIH LAPTOP IDEAL DENGAN METODE SAW jamrud khatulistiwa; Wahyu Nur Fadillah; Mohammed Hafizh Al-Areef
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 4 No 1 (2023): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/j-icom.v4i1.5410

Abstract

Laptops are one of the tools that are widely used in today's modern era, with a portable design that is able to meet the needs of everyday work. To facilitate the selection of a laptop that is comfortable and in suitable with the needs of the work, a decision support system is needed to assist users in choosing the right laptop. This system uses the Simple Additive Weighting method which is a weighted addition method from all existing data. The results of the research are in the form of a decision support system application in choosing the ideal laptop based on a website that can make it easier for users to choose the ideal laptop and according to their needs based on predetermined laptop criteria
Analisis Sentimen Pengguna Twitter Mengenai Calon Presiden Indonesia Tahun 2024 Menggunakan Algoritma LSTM Mohammed Hafizh Al-Areef; Kana Saputra S
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 22, No 2 (2023): Agustus 2023
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v22i2.8680

Abstract

Platform media sosial Twitter menjadi salah satu platform yang banyak digunakan oleh masyarakat Indonesia untuk berkomunikasi, dan mengakses informasi dengan cepat.  Hal ini menyebabkan banyak sekali sentimen masyarakat Indonesia yang dapat dijadikan sebagai studi kasus salah satunya mengenai calon presiden Indonesia tahun 2024. Beberapa tokoh publik seperti Ganjar Pranowo, Prabowo Subianto, dan Ridwan Kamil sudah mulai banyak dibicarakan oleh masyarakat sebagai calon presiden dalam beberapa sentimen pada platform Twitter. Sentimen mengenai para tokoh publik tersebut akan diklasifikasikan dengan algoritma Long Short Term Memory (LSTM) dengan label positif dan negatif. Data sentimen selanjutnya akan melewati proses pre-processing, pelabelan data dengan textblob, word embedding dengan fasttext, hingga data balancing dengan SMOTE sebelum akhirnya model akan dilatih dan diuji. Tahapan penentuan hyperparameter tuning dilakukan sebelum melatih model LSTM seperti penentuan jumlah unit, learning rate, dropout, batch size hingga jumlah epoch agar menghasilkan model latih yang baik. Hasil uji dan evaluasi performa untuk setiap model yang telah dilatih adalah 82% akurasi, 86% presisi, 92% recall, dan 89% f1-score pada model Ganjar Pranowo. 82% akurasi, 82% presisi, 96% recall, dan 89% f1-score pada model Prabowo Subianto. 87% akurasi, 91% presisi, 95% recall, 93% f1-score pada model Ridwan. 87%.
Penerapan Algoritma Convolutional Neural Network Untuk Menentukan Retinopati Hipertensi Melalui Citra Retina Fundus Kana Saputra S; Insan Taufik; Debi Yandra Niska; Raiyan Fairozi; Mhd Hidayat; Mohammed Hafizh Al-Areef
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4307

Abstract

Hypertension is a disease that spreads in the human body caused by increased blood pressure that exceeds normal limits. The increase occurs over a long period, causing complications in human organs that cannot be seen clearly, such as complications in the heart, kidneys, brain, and retina. One of the disorders or complications of high blood pressure is in the retina. The disorder in the retina can also be said as hypertensive retinopathy. Patients suffering from hypertensive retinopathy can only be diagnosed by an ophthalmologist; this is because hypertensive retinopathy cannot be seen with the naked eye. However, one of the earliest signs is the thinning of the arterioles, which can cause blindness. Therefore, computer-assisted processing and analysis of eye fundus images to identify hypertensive retinopathy is an important thing to do by applying the Convolutional Neural Network algorithm. There are nine Convolutional Neural Network architectures used, namely AlexNet, DenseNet, Inception-V3, InceptionResNetV2, Lenet-5, MobileNetV2, ResNet50, VGG16, and VGG19. Based on the experimental results, it was found that of the nine Convolutional Neural Network architectures, two of them, namely AlexNet and Lenet-5, obtained an F1 Measure value of 0.66 and the highest accuracy of 0.67.
Co-Authors Insan Taufik jamrud khatulistiwa Kana Saputra S Mhd Hidayat Niska, Debi Yandra Raiyan Fairozi Wahyu Nur Fadillah