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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) CESS (Journal of Computer Engineering, System and Science) Jurnal Inspiration JOIV : International Journal on Informatics Visualization Creative Information Technology Journal SISFOTENIKA Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Komtika (Komputasi dan Informatika) RESEARCH : Computer, Information System & Technology Management DoubleClick : Journal of Computer and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) Voice Of Informatics Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Multitek Indonesia : Jurnal Ilmiah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Ilmiah Sinus Informasi Interaktif Majalah Ilmiah Bahari Jogja CCIT (Creative Communication and Innovative Technology) Journal TAFAQQUH: Jurnal Hukum Ekonomi Syariah Dan Ahwal Syahsiyah Infotekmesin Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Respati Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Journal of Technology and Informatics (JoTI) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Ekonomi dan Teknik Informatika Literasi Nusantara Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Techno Innovative: Journal Of Social Science Research Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Teknomatika: Jurnal Informatika dan Komputer Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah Explore Jurnal Teknologi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika) Jurnal Bisnis Digital dan Sistem Informasi
Hanif Al Fatta
Universitas Amikom Yogyakarta
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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.
ANALISIS SENTIMEN PADA OPINI PENGGUNA APLIKASI QASIR MENGGUNAKAN SUPPORT VECTOR MACHINE DAN RANDOM FOREST Dhana Aulia Ayu Kurniawan; Ema Utami; Hanif Al Fatta
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.83

Abstract

Qasir is an Android-based Point-Of-Sale (POS) application that can be accessed for free on the Google Playstore. With so many POS applications available, users will be more selective in choosing the application to use. One aspect that can influence the decision to choose an application is the opinion on the application. Opinion is information obtained after using the application that can contain criticism or suggestions. So based on this the user can conclude how other users use the application. Besides being useful for users, opinions if processed properly will produce information that can be used for evaluation for the development team. To analyze and find relationships between owned data, you can use Data Mining. This research will use the Support Vector Machine and Random Forest methods, but each method has its advantages and disadvantages so that the accuracy of the two methods will be compared. The results obtained are that the Support Vector Machine has the highest accuracy value with 80.63% while the Random Forest is 80.21%.
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%.
Prediksi Jumlah Kunjungan Wisatawan Kabupaten Lombok Barat Menggunakan Algoritma Long Short Term Memory M. Imam Budi Laksamana; Ema Utami; Hanif Al Fatta
TAFAQQUH Vol. 6 No. 2 (2021): Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah
Publisher : STIS DAFA MATARAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70032/4k3jrv73

Abstract

Kabupaten Lombok Barat merupakan salah satu wilayah di Indonesia yang memiliki daya tarik tersendiri bagi wisatawan lokal maupun internasional. Salah satu sektor yang paling terdampak besar terhadap intensitas kunjungan wisata adalah hotel. Untuk meningkatkan diperlukan upaya yang tepat untuk memelihara objek wisata sehingga dapat menjadi daya tarik bagi wisatawan. Dalam upaya pemeliharaan objek wisata, Dinas Pariwisata Lombok Barat perlu melakuakan analisa dan prediksi kedatangan wisatawan lokal maupun internasional, dalam prosesnya analisa dan prediksi, pemerintah kabupaten Lombok Barat melakukan pengumpulan data kunjungan wisatawan dari setiap pintu masuk objek wisata yang dimana pada prosesnya memerlukan waktu yang cukup lama dan membutuhkan sumber daya manusia yang cukup tinggi. Untuk mengatasi permasalahan tersebut dilakukan proses prediksi menggunakan sistem komputasi dengan machine learning agar nantinya waktu yang dibutuhkan dalam analisa dan prediksi menjadi lebih singkat dan kebutuhan akan sumber daya manusia yang tinggi bisa teratasi. Metode yang akan diterapkan dalam prediksi adalah Long Short Term Memory (LSTM), atribut dan nilai yang digunkan dalam model LSTM adalah nilai input layer 1, lalu nilai epochs 100 dan batch size 1, berdasarkan hasil pengujian yang dilakukan pada penelitian ini, Long Short Term Memory (LSTM) memiliki performa yang kurang baik dalam memprediksi jumlah kunjungan wisata kabupaten Lombok Barat menggunakan data rentang waktu bulanan dari tahun 2017-2021, hal ini dibuktikan dengan hasil uji evaluasi yang dilakukan dengan mencari nilai Root Mean Square Error (RMSE), dimana hasil model prediksi akan dikatakan baik jika memiliki nilai error yang lebih kecil. dimana nilai Root Mean Square Error (RMSE) yang dihasil dalam penelitian ini cukup tinggi yaitu 10479,30.
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 %.
Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan Nur Fitrianingsih Hasan; Kusrini Kusrini; Hanif Al Fatta
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 2 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i2.1607

Abstract

The inhibition of the production and distribution of bottled water has become a serious problem in the survival of the community and the company, so there is a need for a solution to this problem both short-term and long-term solutions. One of the things that can be done by the company or management is that the right amount of production and distribution is by forecasting sales. Sales forecasting is the process of predicting which products will be sold in the future made based on data that has ever happened. This paper aims to determine the level of accuracy of the use of Backpropagation ANN in estimating the sales of bottled water.The ANN architecture used is 12-10-1 with the MSE value of 0,00043743 and the MAPE value of 6.88%. Forecasting sales results of Robong Holo 600ml brand using Backpropagation ANN for 2019 is 2271 pcs in January, 2019 pcs in February, 1358 pcs in March, 917 pcs in April, 462 pcs in May, 324 pcs in June, 739 pcs in July, 370 pcs in August, 367 pcs in September, 1073 pcs in October, 765 pcs in November and 1388 pcs in December. Keywords— AMDK,Backpropagation,Jaringan Syaraf Tiruan,Penjualan,Peramalan
Co-Authors AA Sudharmawan, AA Aam Shodiqul Munir Abidarin Rosidi Abidarin Rosidi Abidarin Rosidi Abidarin Rosidi Abidarin Rosidi Agam Saka Jati Agus Susilo Nugroho Agustin, Tinuk Ahmad Hajar Alva Hendi Muhammad Alvhinia Meinda Amitaba Anas, Syukron Andi Sunyoto Anggie Ariawan Dewa Putra Anggit Dwi Hartanto Anggraini, Resti Kusuma Annas Al Amin Arief Setyanto Bambang Soedijono Bambang Soedijono W A Bambang Soedijono, Bambang Barnea, Samson Barnea, Samson Bayu Setiaji Bety Wulan Sari Chan Uswatun Khasanah Chriscel Novian Christian Budi Andrianto, Christian Budi Constantin Menteng Darmanto, Darmanto Dewa Saksana, Jidan Dhana Aulia Ayu Kurniawan Dimas Setiawan Donni Prabowo Dwi Sari Widyowaty Ema Utami Eri Sasmita Susanto Faisal Reza Pradhana Fajar Dwi Insani Fandli Supandi Fatimah Nur Arifah Fauji Maulana Ramlan, Fauji Maulana Firstyani Imannisa Rahma Fitriana, Frizka Gori, Takhamo Hadi Sucipto Hafidh Rezha Maulana Hari Agung Budi Santoso Hasan, Nur Fitrianingsih Hendra Kurniawan HENDRA SETIAWAN Hery Maryanto Hidayat Hidayat I Gede Ngurah Arya Indrayasa I Gede Ngurah Arya Indrayasa I Gede Ngurah Arya Indrayasa Imam Adi Nata Khairullah Khairullah Kristama, El Johan Kurniasari, Iin Kusrini Kusrini, K M Suyanto M Suyanto M Suyanto M. Imam Budi Laksamana M. Imam Budi Laksamana M. Nuraminudin M. Suyanto M. Suyanto M. Suyanto M. Suyanto, M. Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Maksom, Zulisman Moch Ali Machmudi Moh Taufik H Mohammad Suyanto Muhammad Resa Arif Yudianto Muhammad Surahmanto Mutiara Dwi Anggraini NABILA OPER Noor Abdul Haris Noto Narwanto Nugroho Setio Wibowo Nugroho, Rakhmat Prasetyo Agung Nur Khasan Nurmasani, Atik Olivia Maria Inacio Tavares Pamungkas, Prima Giri Pradipta, Dody Purwidiantoro, Moch. Hari Purwoko, Agus Raditya Maulana Anuraga Rahman, Aulia Tegar Rakhma Shafrida Kurnia Risa Helilintar Riska Dwi Handayani Rizki Mawan Safagi, Ardian Yuligar Saifudin, Saifudin Saputra, Artha Gilang Saputra, Artha Gilang Setia Wardani Setiawan, Hendi Siti Rihastuti Sofyan Pariyasto Sri Lestari Rahayu Sri Ngudi Wahyuni Sri Sumarlinda Sugihandono, Agus Sutanto, Yudi Suyanto, M Suyanto, M Teguh Cahyono Tigus Juni Betri Tika Dedy Prastyo Tri Nugroho, Arief Tukan, Ewaldus Ambrosius Turah Suhono Tutik Maryana Wahyu Sindu Prasetya Widiyanto, Wahyu Wijaya Wijaya, Tri Amri Wing Wahyu Winarno Wira Dimuksa Wiwi Widayani Yetman Erwadi Yulia Rahmi Yuliana Yuliana Yusuf Fadlila Rachman Zakaria, Mohd Hafiz Zul Hisyam