Article Per Year (5 Year)
p-Index From 2019 - 2024
0.882
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Annual Research Seminar PROCEEDING IC-ITECHS 2014 Proceeding of the Electrical Engineering Computer Science and Informatics Sriwijaya Journal of Informatics and Applications Jurnal Generic
M. Fachrurrozi .
Computer Science Faculty, Universitas Sriwijaya
Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Segmentation and Classification Models Validation Area Mapping of Peat Lands as Initial Value of Fuzzy Kohonen Clustering Network Erwin, Erwin; Saparudin, Saparudin; Fachrurrozi, Muhammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (745.773 KB) | DOI: 10.11591/eecsi.v3.1141

Abstract

Ogan Komering Ilir (OKI) is located at the eastern of South Sumatra Province, 2030'-4015' latitude and 104020'-106000' longitude. Digital image of land was captured from Landsat 8 satellite path 124/row 062. Landsat 8 is new generation satellite which has two sensors, Operation Land Manager (OLI) and Thermal Infra-Red Sensor (TIRS). In pre-processing step, there are a geometric correction, radiometric correction, and cropping of the digital images which resulting coordinated geography. Classification uses maximum likelihood estimator algorithm. In segmentation process and classification, grey value spread is into evenly after applying histogram technique. The results of entropy value are7.42 which is the highest of result image classification, then the smallest entropy value in the result of correction mapping are 6.39. The three of them prove that they have enough high entropy value. Then the result of peatlands classification is given overall accuracy value = = 94.0012% and overall kappa value = 0.9230 so the result of classification can be considered to be right.
Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR) Muhammad Fachrurrozi; Saparudin Saparudin; Erwin Erwin; Mardiana Mardiana; Clara Fin Badillah; Junia Erlina; Auzan Lazuardi
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.606 KB) | DOI: 10.11591/ijece.v8i5.pp2812-2817

Abstract

Face recognition system in real time is divided into three processes, namely feature extraction, clustering, detection, and recognition. Each of these stages uses different methods, Local Binary Pattern (LBP), Agglomerative Hierarchical Clustering (AHC) and Euclidean Distance. Multi-face image search using Content Based Image Retrieval (CBIR) method. CBIR performs image search by image feature itself. Based on real time trial results, the accuracy value obtained is 61.64%.  
Peringkasan Teks Berita Berbahasa Indonesia Menggunakan Metode Latent Semantic Analysis (LSA) dan Teknik Steinberger&Jezek Jerry Satiamy Saputra; Muhammad Fachrurrozi; Yunita Yunita
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dokumen berita merupakan dokumen yang memuat berbagai macam informasi. Semakin banyak informasi yang terdapat pada suatu dokumen membuat dokumen tersebut semakin panjang. Membaca keseluruhan dokumen tersebut memakan banyak waktu. Ringkasan dokumen diperlukan untuk memudahkan memahami informasi yang berukuran besar dengan cepat. Peringkasan dokumen secara otomatis merupakan solusi untuk membantu mendapatkan intisari dari dokumen. Pada penelitian ini dilakukan penerapan metode Latent Semantic Analysis dan teknik Steinberger&Jezek yang digunakan untuk peringkasan teks otomatis. Jumlah data uji yang digunakan sebanyak 10 teks berita yang diambil dari data uji penelitian sebelumnya. Hasil penelitian yang telah dilakukan menghasilkan rata-rata recall 0.7027, precision 0.6973, dan f-measure 0.6974.
Penggunaan Media Animasi Untuk Penanaman Pola Berfikir Komputasional Pada Siswa SMA di Palembang Muhammad Fachrurrozi; Novi Yusliani; Osvari Arsalan; Kanda Januar Miraswan; Anna Dwi Marjusalinah
Annual Research Seminar (ARS) Vol 2, No 2 (2016): Special Issue : Penelitian, Pengabdian Masyarakat
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemikiran komputasional, pertama kali dikemukakan oleh Jeannette Wing dalam penelitiannya, merupakan salah satu kemampuan mendasar yang harus dimiliki oleh setiap orang. Kemampuan ini sama pentingnya dengan kemampuan membaca, menulis dan aritmatika yang harus dimiliki oleh setiap orang [1]. Namun, saat ini teknik yang digunakan untuk mengajarkan materi pemikiran komputasional ini masih menjadi kendala. Riset dilakukan terhadap Siswa-siswa SMA di Palembang dengan memberikan permasalahan komputasional. Siswa diajarkan untuk mencari pemecahannya dengan 2 teknik yaitu dengan penyelesaian menggunakan pemrograman dan dengan menggunakan media animasi. Hasilnya, kecenderungan siswa lebih memilih pemecahan masalah menggunakan media animasi disbanding dengan pemrograman. Selain media animasi dirasakan lebih menarik juga teknik ini lebih mudah untuk disampaikan.
Pengoreksian Ejaan Kata Berbahasa Indonesia Menggunakan Algoritma Levensthein Distance Muhammad Omar Braddley; Muhammad Fachrurrozi; Novi Yusliani
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

 Kesalahan penulisan pada dokumen bisa saja terjadi tanpa disengaja, hal ini berpengaruh pada informasi yang didapat oleh pembaca. Sistem pengoreksi ejaan kata secara otomatis mampu mengurangi tingkat kesalahan penulisan. Salah satu metode dalam pengoreksian ejaan kata adalah Approximate String Matching, metode ini menerapkan pendekatan pencarian string. Algoritma Levensthein Distance merupakan salah satu bagian metode Approximate String Matching. Algoritma Levensthein Distance memiliki 3 macam operasi string yaitu penghapusan, penambahan dan pengubahan. Operasi-operasi ini digunakan untuk menghitung jarak antara 2 string, semakin kecil jaraknya maka 2 buah string dikatakan cocok. Pengujian dilakukan dengan 90 data yang terdiri dari 3 skenario yaitu penghapusan, penambahan dan pengubahan karakter. Hasil pengujian akurasi rata-rata sebesar 100% dan waktu 23 mili detik pada operasi penghapusan karakter, hasil 96% dan waktu 5 mili detik pada operasi pengubahan karakter dan hasil 93% dan waktu 88 mili detik pada operasi penambahan.
Questions Classification Software based on Bloom’s Cognitive Levels using Naive Bayes Classifier Method Muhammad Fachrurrozi; Lidya Irfiyani Silaban; Novi Yusliani
IC-ITECHS Vol 1 (2014): Prosiding IC-ITECHS 2014
Publisher : IC-ITECHS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1452.813 KB)

Abstract

Questions Classification is one way to know how the student understanding some lessons. Those questions can be collected and classified based on cognitive Bloom level. Bloom Cognitive Level organized question in groups that represents contents of those questions. Words contained in every question have certain meaning and can be used as base to determine category of question. This study aims to classify amounts of questions based on cognitive Bloom level with Naive Bayes Classifier method. According to Bloom's taxonomy of cognitive level divided into six levels of the Knowledge (C1), Comprehension (C2), Application (C3), Analysis (C4), Synthesis (C5), and Evaluation (C6). In this study, questions classified into 3 classes based on cognitive Bloom level, Knowledge (C1), Comprehension (C2), Application (C3). The amount of collective data used for training process is 240 questions. Result of this study generates accuracy of 75 % from 60 question samples tested. Susceptibility often occured because of same vocabularies from each categories, thus cause mistakes in classification.
Segmentation and Classification Models Validation Area Mapping of Peat Lands as Initial Value of Fuzzy Kohonen Clustering Network Erwin Erwin; Saparudin Saparudin; Muhammad Fachrurrozi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (745.773 KB) | DOI: 10.11591/eecsi.v3.1141

Abstract

Ogan Komering Ilir (OKI) is located at the eastern of South Sumatra Province, 2030'-4015' latitude and 104020'-106000' longitude. Digital image of land was captured from Landsat 8 satellite path 124/row 062. Landsat 8 is new generation satellite which has two sensors, Operation Land Manager (OLI) and Thermal Infra-Red Sensor (TIRS). In pre-processing step, there are a geometric correction, radiometric correction, and cropping of the digital images which resulting coordinated geography. Classification uses maximum likelihood estimator algorithm. In segmentation process and classification, grey value spread is into evenly after applying histogram technique. The results of entropy value are7.42 which is the highest of result image classification, then the smallest entropy value in the result of correction mapping are 6.39. The three of them prove that they have enough high entropy value. Then the result of peatlands classification is given overall accuracy value = = 94.0012% and overall kappa value = 0.9230 so the result of classification can be considered to be right.
Face Detection Using Randomized Hough Transform (RHT) with Various Ellipses Segmentations Muhammad Fachrurrozi; Saparudin Saparudin; Mardiana Mardiana; Desty Rodiah; Winda Agusthia
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Face detection is one of earlier phase in face recognition process. This research aims to get the faces area on digital image without being affected by face orientation, lights condition, background and the expression. The detected face area is usually shaped by a rectangle. Many pixels on the rectangle are not part of face, especially at the four of the image corners. This research use an ellipse as replacement a rectangle. The detected face is shaped by ellipses with various sizes and orientations. The digital image segmentations is used to detect face candidates area. The ellipse is formed by using Randomized Hough Transform (RHT) method, which is influenced by the center point of ellipse candidates. RHT found three random pixels on segmented image. The rate of success of RHT is determined by segmentation results. The research result is tested by using various thresholds, and get the best accuracy at 74.4%. The rate of accuracy is measured by comparing between RHT ellipses shape and circle shape on OpenCV library as ground truth.
Peningkatan Fungsionalitas Perangkat Lunak Melalui Restrukturisasi Data (Studi Kasus: Sistem Informasi Akademik Fakultas Ilmu Komputer Unsri) M. Fachrurrozi
Generic Vol 4 No 1 (2009): Vol 4, No 1 (2009)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Bagian penting di dalam kegiatan akademik di suatu perguruan tinggi. Adanya keinginan untuk meningkatkan fungsionalitas perangkat lunak, berdampak terhadap data yang ada di sistem lama, sehingga data tersebut perlu untuk dipertahankan untuk dapat digunakan di sistem baru nantinya. Salah satu metode yang dapat dipakai adalah Restrukturisasi Data. Restrukturisasi data ini dilakukan dengan beberapa tahapan, yaitu mendeteksi database smells yang ada di sistem lama, rekayasa ulang data berdasarkan database smells yang ditemukan, implementasi hasil yang diperoleh serta melakukan pengujian terhadap data yang telah dipindahkan ke lingkungan DBMS yang baru.
CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING Raihan Mufid Setiadi; Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.53

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Co-Authors Abdiansah Abdiansah Ade Iriani Sapitri Anggun Islami Anna Dwi Marjusalinah Annisa Darmawahyuni Auzan Lazuardi Bambang Tutuko Clara Fin Badillah Desty Rodiah Dewi Chayanti Erwin, Erwin Firdaus Firdaus Jerry Satiamy Saputra Junia Erlina Kanda Januar Miraswan Lidya Irfiyani Silaban Mardiana Mardiana Muhammad Naufal Rachmatullah Muhammad Naufal Rachmatullah Muhammad Omar Braddley Muhammad Rasuandi Novi Yusliani Novi Yusliani Osvari Arsalan Primanita, Anggina Rafly Pakomgan Raihan Mufid Setiadi Saparudin Saparudin Saparudin Saparudin Saparudin, Saparudin Winda Agusthia Yoga Yuniadi Yunita Yunita