Article Per Year (5 Year)
p-Index From 2021 - 2026
2.735
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Creative Information Technology Journal Jurnal Komtika (Komputasi dan Informatika) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Informasi Interaktif Respati Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Ekonomi dan Teknik Informatika Innovative: Journal Of Social Science Research Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Komtika (Komputasi dan Informatika)
Hanif Al Fatta
Universitas Amikom Yogyakarta
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Transportation Other

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 38 Documents
Search

 1   2   3   4 
Sentiment Analysis Comments Covid-19 Variant Omicron on Social Media Instagram with Bidirectional Encoder from Transformers (BERT) Pradipta, Dody; Kusrini, Kusrini; Fatta, Hanif Al
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 3 No 1: JTECS Januari 2023
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v3i1.3219

Abstract

Internet merupakan alat komunikasi yang banyak diminati karena pesatnya perkembangan teknologi dan informasi beberapa tahun belakangan ini. Ini adalah konteks untuk memodernisasi dan sepenuhnya mendigitalkan komunikasi. Salah satu perubahan dalam komunikasi digital adalah media sosial, platform digital yang memungkinkan orang berbicara satu sama lain, berbagi informasi, dan lainnya. Pada platform media sosial ini, yang dirancang untuk mendapatkan masukan inti dari pengguna atau konsumen secara efisien, komentar dapat digunakan untuk mengumpulkan opini dari pengguna. Instagram adalah salah satu platform media sosial paling populer saat ini, dan banyak penggunanya menggunakannya untuk menyuarakan pendapat (komentar) mereka tentang pandemi Covid-19. Menggunakan metode Bidirectional Encoder from Transformers (BERT), komentar masyarakat nantinya dapat diklasifikasikan menjadi sentimen positif, negatif, dan netral. Analisis sentimen mengungkapkan bagaimana perasaan orang tentang varian Omicron pandemi Covid-19. Prosedur scraping menghasilkan 1.052 data yang terdiri dari 663 komentar negatif, 388 komentar netral, dan 1 komentar positif. Hasil tes memiliki akurasi sebesar 0,632 (63%).
Klasifikasi Penyakit Jantung Menggunakan Random Forest Clasifier Hidayat, Hidayat; Sunyoto, Andi; Al Fatta, Hanif
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 7 No. 1 (2023): Volume VII - Nomor 1 - September 2023
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v7i1.464

Abstract

Abstract— Penelitian ini bertujuan untuk meningkatkan peforma model saat melakukan klasifikasi peyakit jantung. Algoritma random fores digunakan untuk melakukan klasifikasi peyakit jantung berdasarkan fitur-fitur yang ada pada dataset. Klasifikasi ini dilakukan dengan menggunakan Heart Disease Dataset dari kaggle yang mempunyai 2 class diantaranya 0 (tidak terindikasi penyakit), dan 1 (terindikasi penyakit). Selanjutnya dataset tersebut dilakukan teknik pre-prosessing data, normalisasi data, split data, klasifikasi dan yang terakhir evaluasi metode. Penelitian ini mengungkapkan bahwa metode random forest berhasil menghasilkan tingkat akurasi yang lebih tinggi dalam proses klasifikasi penyakit jantung, dibandingkan dengan hasil penelitian sebelumnya yaitu mencapai akurasi sebesar 94%. Hal ini menunjukkan bahwa penggunaan Random forest dibantu dengan teknik pre-prosessing, dan normalisasi data dapat menjadi alternatif yang baik dalam melakukan klasifikasi. Penelitian ini memberikan manfaat saat klasifikasi penyakit jantung secara cepat dan akurat. Keywords —Penyakit Jantung, Random Forest
PERANGKINGAN DALAM PENENTUAN E-COMMERCE TERBAIK DENGAN METODE ANALYTICAL HIERARCHICAL PROCESS Tri Nugroho, Arief; Kusrini, Kusrini; Al Fatta, Hanif
JURNAL PERANGKAT LUNAK Vol 5 No 3 (2023): Jurnal Perangkat Lunak
Publisher : Indragiri Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/jupel.v5i3.2798

Abstract

Revolusi Industri 4.0 menyebabkan adanya pergeseran interaksi antar manusia, diantaranya dalam proses bertransaksi jual beli. Salah satu produk Revolusi Industri 4.0 yang dihasilkan untuk mempermudah proses bertransaksi jual beli adalah e-commerce. E-commerce diprediksi akan terus bertumbuh seiring berkembangnya ekonomi digital di Indonesia. E-commerce yang hadir di Indonesia sudah semakin menjamur dan bersaing dengan memberikan beragam fasilitas yang memanjakan konsumen diantaranya program diskon, cashback, dan, gratis biaya pengiriman. Semakin banyaknya pilihan e-commerce ini membuat konsumen cenderung bingung dan akhirnya mengikuti gelombang tanpa tahu tujuan sebenarnya dalam penggunaan e-commerce. Salah satu cara yang dapat digunakan konsumen untuk memilih e-commerce yang paling efektif dan efisien adalah dengan metode AHP (Analytical Hierarchical Process). Metode AHP akan memberikan rekomendasi e-commerce yang paling tepat dan layak digunakan berdasarkan bobot kriteria yang dihasilkan.
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm Kusrini, Kusrini; Arif Yudianto, Muhammad Resa; Al Fatta, Hanif
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3752-3761

Abstract

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.
Penentuan Marker untuk Mempermudah Motion Tracking dalam Video Barnea, Samson; Suyanto, M; Fatta, Hanif Al
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 8 No 1 (2018): Maret 2018
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (690.82 KB) | DOI: 10.33020/saintekom.v8i1.55

Abstract

In the motion tracking is required a marker that will be easier to process this motion tracking, marker certainly has a shape, size and position that will help facilitate the process of this motion tracking. In this research will be done shooting a scene with an experiment with 3 types of marker form and each marker has 5 different sizes, then the results of the taking will be analyzed. From the results of the analysis will get the results of numbers that will show the highest number for what markers with what size can help the process of motion tracking, as well as position marker where the most frequently appear on the results of the marker position analysis later. This can be seen from the results of software analysis that will be used later. Keyword : CGI, marker , motion tracking
Analisis Penerapan Metode Convex Hull Dan Convexity Defects Untuk Pengenalan Isyarat Tangan Saputra, Artha Gilang; Utami, Ema; Fatta, Hanif Al
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 8 No 2 (2018): September 2018
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.28 KB) | DOI: 10.33020/saintekom.v8i2.59

Abstract

Research of Human Computer Interaction (HCI) and Computer Vision (CV) is increasingly focused on advanced interface for interacting with humans and creating system models for various purposes. Especially for input device problem to interact with computer. Humans are accustomed to communicate with fellow human beings using voice communication and accompanied by body pose and hand gesture. The main purpose of this research is to applying the Convex Hull and Convexity Defects methods for Hand Gesture Recognition system. In this research, the Hand Gesture Recognition system designed with the OpenCV library and then receives input from the user's hand gesture using an integrated webcam on the computer and system generates a language output from the hand-recognizable gestures. Testing involves several variables which affect success in recognizing user's hand gestures, such as hand distance with webcam, corner of the finger, light conditions and background conditions. As a result, the user's hand gestures can be recognized with a stable and accurate when at a distance of 50cm-70cm, corner of the finger 25o–70o, light conditions 150lux-460lux and plain background conditions.
CNN and SVM Combination for Multi-Class Classification of Diabetic Retinopathy Based on Fundus Imaging Agustin, Tinuk; Purwidiantoro, Moch. Hari; Utami, Ema; Fatta, Hanif Al
Telematika Vol 15, No 2: August (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i2.2086

Abstract

Diabetic Retinopathy (DR) is a cause of blindness. Early detection has the potential to save the patient's vision. Reading fundoscopic photos requires more expertise and effort by the ophthalmologist. There are many visual similarities in lesions and only minor differences in the spatial domain. A computer-assisted automatic detection system is needed to assist medical experts in DR diagnosis and can reduce costs. This study proposes a combination method of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for the automatic classification of Diabetic Retinopathy (DR). The pre-train architecture Inception-V3 uses for feature extraction of input data. After training and getting the best model, the next is classification with SVM. Data augmentation techniques use to multiply and add variations to the dataset. Before the feature extraction stage, the dataset will process by separating the green channel from the RGB image. Next, the CLAHE will require increasing the contrast of the picture. This study aims to improve the performance in multi-class DR classification. The proposed model uses four classes of unbalanced and small datasets from retinal fundus images. This paper also compares the combined performance of CNN SVM with CNN Softmax based on the accuracy value to validate the results. Our experiments show that the combination of CNN SVM can increase the accuracy of auto-detection of DR severity up to 11.48% better than CNN softmax. The results showed that the pre-trained architectural model from the combination of Inception-V3 with SVM classification improves the accuracy extensively, even on small and unbalanced datasets.
Metode Support Vector Machine pada Klasifikasi Pengaduan Masyarakat Anggraini, Resti Kusuma; Kusrini, K; Fatta, Hanif Al
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.547

Abstract

Public complaint service is a very important service in a government agency. Service 112 is a public complaint service that is connected directly to the 112 call center that has been appointed by the local government. The government of the city of Samarinda through the Office of Communication and Informatics also has a call center service 112 which is commonly called by Samarinda City residents, namely the Samarinda Siaga Service 112. Various kinds of complaints from the public make operators have to be more careful in categorizing every complaint that comes in, and not infrequently the community convey it using the regional language which can make it difficult for operators to categorize the complaint. Text mining is one of the methods used in the classification process. In this study, the text mining process was used to classify public complaints using the Support Vector Machine method. The results of the research that has been carried out using as many as 120 public complaint data, which are then divided into 2, namely as training data as much as 96 data and data testing as much as 24 data using the Support Vector Machine method using the RBF kernel get an accuracy result of 79%, a precision of 56%, recall of 100%, f1-score of 71% and support 5.
Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa Gori, Takhamo; Sunyoto, Andi; Al Fatta, Hanif
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241118074

Abstract

Pendidikan merupakan aspek penting dalam kehidupan masyarakat dan memiliki peran yang sangat vital untuk menciptakan sumber daya manusia yang handal dan berkualitas dalam menghadapi berbagai tantangan pada era modernisasi. Namun, putus sekolah dan retensi siswa menjadi tantangan serius bagi perkembangan pendidikan saat ini. Salah satu faktor pemicu putus sekolah adalah kinerja akademik siswa yang rendah, mendorong perlunya tindakan pencegahan yang efektif untuk mengurangi tingkat kegagalan pendidikan. Penelitian ini bertujuan untuk memprediksi kinerja akademik siswa dengan mengintegrasikan metode Correlation-Based Feature Selection (CFS) dan Algoritma Naïve Nayes pada gabungan dataset pelajaran Matematika dan Bahasa Portugis dua sekolah menengah di Portugal. Proses preprocessing data melibatkan integrasi data, pelabelan data, transformasi data, dan pembersihan data diterapkan pada tahap awal penelitian. Hasil penelitian menunjukkan bahwa atribut signifikan yang mempengaruhi kinerja akademik siswa meliputi G2, G1, Higher, Medu, Studytime, goout, Absences, dan Failures. Melalui pemodelan algoritma Naïve Bayes, metode CFS terbukti meningkatkan nilai accuracy, recall, precision, dan f1-score dalam memprediksi kinerja akademik siswa. Sebelum CFS, model Naïve Bayes menunjukkan accuracy sebesar 89.27%, dengan recall, precision, danf1-score masing-masing sebesar 89.27%, 89.86%, dan 89.47%. Setelah implementasi CFS, evaluasi model prediksi mengalami peningkatan signifikan menjadi 91.22%, 91.22%, 92.24%, dan 91.48%.
Klasifikasi Penyakit Daun Apel Menggunakan Arsitektur CNN dengan Transfer Learning Rahman, Aulia Tegar; Setyanto, Arief; Fatta, Hanif Al
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 6, No 1 (2024): Jurnal SENOPATI Vol 6, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2024.v6i1.6574

Abstract

Salah satu hasil produk pertanian subtropis yang dapat ditanam di Indonesia adalah apel. Dalam budidaya apel, pengendalian hama dan penyakit merupakan salah satu faktor kunci dalam perkembangan tanaman apel, karena dapat mempengaruhi hasil apel. Salah satu teknologi yang berkembang pesat dalam pendeteksian atau diagnosis penyakit tanaman dapat menyederhanakan proses klasifikasi penyakit tanaman khususnya penyakit daun apel dan membantu dalam diagnose dini adalah deep learning. Terdapat salah satu arsitektur deep learning yang dapat digunakan dalam klasifikasi citra, salah satunya Convolutional Neural Networks (CNN). Arsitektur CNN dengan transfer learning yang menghasilkan nilai akurasi yang masih bisa diterima, waktu yang diperlukan pendek pada klasifikasi penyakit daun apel. Hasil dari klasifikasi penyakit daun apel dengan VGG16 mendapatkan akurasi sebesar 99,31 %.
Co-Authors AA Sudharmawan, AA Abidarin Rosidi Agustin, Tinuk Anas, Syukron Andi Sunyoto Anggraini, Resti Kusuma Arief Setyanto Bambang Soedijono, Bambang Barnea, Samson Barnea, Samson Bayu Setiaji Christian Budi Andrianto, Christian Budi Darmanto Darmanto Ema Utami Eri Sasmita Susanto Faisal Reza Pradhana Fauji Maulana Ramlan, Fauji Maulana Fitriana, Frizka Gori, Takhamo Hasan, Nur Fitrianingsih Hidayat Hidayat I Gede Ngurah Arya Indrayasa Imam Adi Nata Kristama, El Johan Kurniasari, Iin Kusrini Kusrini Kusrini, K Kusrini, Kusrini Kusrini, Kusrini M. Suyanto, M. Maksom, Zulisman Mohammad Suyanto Muhammad Resa Arif Yudianto NABILA OPER Nurmasani, Atik Pradipta, Dody Purwidiantoro, Moch. Hari Purwoko, Agus Rahman, Aulia Tegar Risa Helilintar Safagi, Ardian Yuligar Saifudin, Saifudin Saputra, Artha Gilang Saputra, Artha Gilang Setiawan, Hendi Siti Rihastuti Sugihandono, Agus Sutanto, Yudi Suyanto, M Suyanto, M Tri Nugroho, Arief Tukan, Ewaldus Ambrosius Wijaya, Tri Amri Wira Dimuksa Zakaria, Mohd Hafiz