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All Journal ELKHA : Jurnal Teknik Elektro Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal CoreIT Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Petir Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Informatika Global Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Ilmu Komputer dan Informatika Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference Scientica: Jurnal Ilmiah Sains dan Teknologi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) INTECH (Informatika dan Teknologi)
M Ezar Al Rivan
Universitas Multi Data Palembang
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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DESAIN MODEL FUZZY-TSUKAMOTO UNTUK PENENTUAN KUALITAS BUAH PEPAYA CALIFORNIA (CARICA PAPAYA L.) BERDASARKAN BENTUK FISIK Al Rivan, Muhammad Ezar; Octavia, Angella; Wijaya, Irvan
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 11 No 1 (2021): Maret 2021
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.805 KB) | DOI: 10.33020/saintekom.v11i1.155

Abstract

Papaya easily found in the local market with a relatively cheap price, adequate nutrient and vitamin content. The quality of California papaya can be measured by size, color and defect. This research discusses the topic of fuzzy model design regarding the measured of the quality of papaya using fuzzy tsukamoto with input variables major axis, minor axis, red and green intensity color, and defect variables along with the output as a result of determining the quality of California papaya. Based on the tests that have been carried out, the results of the quality is 75%.
Ekstraksi Fitur Warna dengan Histogram HSV untuk Klasifikasi Motif Songket Palembang Yohannes, Yohannes; Al Rivan, Muhammad Ezar; Devella, Siska; Meiriyama, Meiriyama
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.8110

Abstract

Palembang Songket is a type of traditional woven cloth that has been registered as Indonesia's intangible cultural heritage since 2013. Palembang Songket has many motifs including Bunga Cina, Cantik Manis, and Pulir. The motifs on Palembang Songket have different meanings which can influence the selling price of the Songket. Recognition and classification of Palembang Songket types and motifs can be done by utilizing computer technology such as digital image processing and machine learning. In this research, the classification of Palembang Songket motifs was carried out using color features with histograms in Hue, Saturation, and Value (HSV) space and the Support Vector Machine (SVM) machine learning algorithm. Testing was carried out on a classification system using 45 test images. The histogram of HSV and SVM methods with the best kernel, namely RBF, were able to classify Palembang Songket motifs with an accuracy of 0.956; precision of 0.94; recall of 0.933; and f1-score of 0.931.
ANALISIS SENTIMEN PADA KOMENTAR INSTAGRAM MENGGUNAKAN GAUSSIAN NAÏVE BAYES Pratama, M Fikri Rizki; Rivan, M Ezar Al
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 3 No. 2 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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

Abstract

Secara umum analisis sentimen adalah cara untuk mengenali opini publik yang dituangkan dalam sebuah teks. Opini memiliki bentuk emosi, evaluasi, kepercayaan, kepuasan dan penilaian. Media Sosial merupakan medium di internet yang memungkinkan penggunanya mempresentasikan dirinya maupun berinteraksi, bekerjasama, saling berbagi, berkomunikasi dengan pengguna lainnya, dan membentuk ikatan sosial secara virtual. Instagram adalah sebuah platform media sosial yang memungkinkan pengguna untuk berbagi foto dan video, serta berinteraksi dengan pengguna lainnya melalui komentar. Pada penelitian ini menggunakan metode klasifikasi naive bayes dengan menggunakan dataset yang berjumlah 400. Dataset dibagi menjadi data training dan data testing dengan perbandingan rasio 80%: 20% . Hasil dari penelitian ini mendapatkan kelas negative menghasilkan nilai precision 82%, Recall 78%, F1-Score 79%. sedangkan untuk kelas positive menghasilkan nilai precision 79%, Recall 82%, F1-Score 80%.
Analisis Sentimen Publik Terhadap Keberadaan Juru Parkir Liar Menggunakan Naïve Bayes Dengan Teknik SMOTE Daniel, Daniel; Saputra, Andreas; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.8154

Abstract

The continuous growth of YouTube is increasingly leveraged by users to convey information, including critiques and suggestions about illegal parking attendants. The method used in this research is data classification using the Naïve Bayes Classifier (NBC). The system is developed using internal data collected from the internet/YouTube to determine whether sentences are positive or negative opinions. This determination is classified as a classification process. The data is processed using SMOTE to balance the dataset, followed by classifying comments into two classes: positive and negative. This classification employs the Naïve Bayes algorithm. This classification provides convenience for users to view both positive and negative opinions. The accuracy test results for the Naïve Bayes method without SMOTE for classification yielded an average of 86.93%, while the accuracy test results for the Naïve Bayes method with SMOTE technique yielded an average of 91.99%.
Pengenalan Wajah Untuk Sistem Absensi Sekolah Menggunakan YOLOv8 Fernando, Fernando; Al Rivan, M Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9652

Abstract

Students' attendance at school is still recorded manually using books. This has weaknesses such as the attendance book being susceptible to damage and loss which can cause loss of important attendance data and requires a long time to recapitulate the results of the attendance recapitulation ahead of the semester increase. Therefore, this research discusses the application of the You Only Look Once (YOLO) method to recognize students' faces because YOLO is a real-time object detection method that is very fast and has high accuracy. The dataset used consists of 1,250 images with 70% train data, 20% valid data, and 10% test data which was trained with epoch 50, epoch 75, and epoch 100 resulting in an accuracy of 100% for each epoch. The model that has been trained can recognize students' faces well and can be applied to computer vision-based software to assist teachers in taking attendance and recapitulating attendance results in a certain period so that it doesn't take a long time to recap.
Implementasi Secure Hash Algorithm-256 dan Advanced Encryption Standard Untuk Verifikasi Tanda Tangan Digital Nadimsyah, Ahmad Zaky; Ezar Al Rivan, Muhammad
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5942

Abstract

Process of traditional document approval carried out across all layers of society as part of a legal system in matters related to work or daily activities that involve legal obligations by all relevant parties is done by using handwritten signatures to bind the intentions and contents of the document with all related parties and provie legal value. With the development of internet access, traditional signatures have started to be abandoned, leading to a shift in document approval infrastructure towards a hybrid system where document signing can be done electronically. This shift has introduced the potential for electronic signature forgery, as there is no validation that can be performed on a digital image of an electronic signature.The ease of scanning someone's traditional signature can potentially be misused by unauthorized parties, so digital signatures have been developed to address this problem. Digital signatures are used to secure messages or documents from unauthorised parties, secure sensitive data, strengthen the signatories' confidence and detect attempted corruption. The solution offered in this project is the development of a software system capable of verifying the authenticity of digital documents so that the documents are not abused and can be used as appropriate using the SHA-256 Hash Function and the AES-256 Encryption Algorithm. The software produced has a satisfaction percentage of 93.71%, so it can be concluded that the applications that have been developed are running well.
Perbandingan Kecepatan Gabungan Algoritma Quick Sort dan Merge Sort dengan Insertion Sort, Bubble Sort dan Selection Sort Muhammad Ezar Al Rivan
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 2 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i2.675

Abstract

Ordering is one of the process done before doing data processing. The sorting algorithm has its own strengths and weaknesses. By taking strengths of each algorithm then combined can be a better algorithm. Quick Sort and Merge Sort are algorithms that divide the data into parts and each part divide again into sub-section until one element. Usually one element join with others and then sorted by. In this experiment data divide into parts that have size not more than threshold. This part then sorted by Insertion Sort, Bubble Sort and Selection Sort. This replacement process can be reduce time used to divide data into one element. Data size and data type may affect time so this experiment use 5 data sizes and 3 types of data. The algorithm dominates in experiment are Merge-Insertion Sort and Merge-Selection Sort.
Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching Ivan Fareza; Rusdie Busdin; Muhammad Ezar Al Rivan; Hafiz Irsyad
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

Sign Language is a way to communicate to people with disabilities. American Sign Language (ASL) is one among other sign languages. Sign language image would be extracted using Edge Oriented Histogram (EOH). In Content-Based Image Retrieval, a feature from query image will be compared to database image to find out the best matching method so three matching methods will be used. The matching methods are Earth Mover Distance, Hausdorff Distance, and Sum of Absolute Difference. The smallest distance shows the strong similarity between query image and database image. The Sum of Absolute Difference is outperformed of other in case the most of relevant image can be retrieved. The order of methods to recognize alphabet (from the best one) is Sum of Absolute Difference following by Earth Mover Distance and Hausdorff Distance. Hausdorff Distance has smallest running time using 4 bin features. Earth Mover Distance has smallest running time using 6 bin features. Sum of Absolute Difference has smallest running time using 9 bin features, so the method can be recommended to recognize ASL.
Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients Muhammad Ezar Al Rivan; Hafiz Irsyad; Kevin Kevin; Arta Tri Narta
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1936

Abstract

Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database. K-Nearest Neighbor use to measure distance between feature train and feature test. There are three distance use in this paper consist of Euclidean Distance, Manhattan Distance and Chebychev Distance. The best result are 0,99 when using Euclidean Distance and Manhattan Distance with k=3 dan k=5
Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan Muhammad Ezar Al Rivan; Mochammad Trinanda Noviardy
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2844

Abstract

Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%. Keywords— Artificial Neural Network; American Sign Language; Histogram of Oriented Gradient; Training Function
Co-Authors Alviando, Muhammad Restu Alvin Setiawan Alwyn Giovri Riyadi Andreas Saputra, Andreas Andree Fendiawati Antony, Felix Arta Tri Narta Arta Tri Narta Awalludin, Nur Bhagaskara Bhagaskara Billy Gunawan Budi, Raden George Samuel Busdin, Rusdie Caroline, Fionna Daniel Daniel Devella, Siska Dicko Dini Amputri Fareza, Ivan Farisi, Ahmad Fendy Novianto Lukman Fernando Fernando, Fernando Gabriela Repca Sung Gasim Giovani Prakasa Gandi Hafiz Irsyad Hafiz Irsyad Hartoyo, Suryanto Ivan Fareza Juliana Nasution Kevin Kevin Kevin kevin Meiriyama, Meiriyama Mochammad Trinanda Noviardy Molavi Arman Monica Rizki Ayustin Muhammad Ricky Nadimsyah, Ahmad Zaky Narta, Arta Tri Nicholas, Nicholas Ningrum Larasati Novan Wijaya Noviardy, Mochammad Trinanda Nur Rachmat Octavia, Angella Orlando Orlando Pratama, M Fikri Rizki Randy Andreo Sonaru Reynald Dwika Prameswara Ricky, Muhammad Rijaya, Rheza RR. Ella Evrita Hestiandari Rusdie Busdin Saputra, Jordi Sehat Martinus Surya Benediktus Siti Nadra Steven Steven Suherman, Jessica Suryanto Hartoyo Suwanto, Elisa Putri Tanzil, William Tegar Juangkara Tommy Saputra Wati, Risha Ambar Wijaya, Irvan Winston Kennedy Wiwik Handayani Yoannita, Yoannita Yohannes Yohannes Yohannes, Ricky