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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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PENGENALAN VARIETAS MANGGA BERDASARKAN BENTUK DAN TEKSTUR DAUN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK Fathorazi Nur Fajri; Purwanto Purwanto; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.62 KB)

Abstract

Pada saat ini mangga Indonesia sangat diminati oleh orang asing terlebih untuk mangga kualitas unggul seperti mangga manalagi dan gadung. Akan tetapi tak jarang masyarakat tidak mengerti atau keliru mengenali varietas mangga yang mereka tanam. Selama ini identifikasi atau pengenalan varietas mangga dilakukan dengan menggunakan mata. Hal ini pun dibutuh keahlian atau pakar dalam membedakan varietas mangga tersebut. Akan tetapi orang yang ahli mempunyai keterbatasan, tidak semua varietas mangga dapat dikenali atau diidentifikasi. Terdapat beberapa usulan model yang telah dilakukan untuk mengindentifikasi mangga dengan citra digital akan tetapi akurasi yang dihasilkan masih kurang yaitu di bawah 80 %. Selain itu masing masing peneliti hanya menggunakan satu fitur citra yaitu fitur tekstur. Penelitian ini mengunakan dataset sebanyak 300 citra daun mangga, 150 citra daun mangga varietas manalagi dan 150 citra daun gadung. Metode yang digunakan pada penelitian ini yaitu Backpropagation Neural Network (BPNN) dengan menggunakan fitur bentuk dan tekstur daun mangga. Model BPNN yang paling optimal pada penelitian ini yaitu menggunakan hidden layer = 19, learning rate = 0.9, momentum = 0.9 dan epoch = 100 dengan hasil root mean squar error (RMSE) = 0.0018. Kemudian hasil dari pengujian menggunakan citra daun mangga menghasilkan tingkat akurasi 96 %.
MIXTURE FEATURE EXTRACTION BASED ON LOCAL BINARY PATTERN AND GREY-LEVEL CO-OCCURRENCE MATRIX TECHNIQUES FOR MOUTH EXPRESSION RECOGNITION Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Yuslena Sari
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 2 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i2.145

Abstract

Some academics struggle to recognize facial emotions based on pattern recognition. In general, this recognition utilizes all facial features. However, this study was limited to identifying facial emotions in a single facial region. In this study, lips, one of the facial features that can reveal a person's expression, are utilized. Using a combination of local binary pattern feature extraction (LBP) and grey level co-occurrence matrix (GLCM) methods and a multiclass support vector machine classification approach for feature extraction in facial images. The concept begins with image segmentation to create an image of a mouth. Experiments were also conducted for various tests, and the outcomes of these experiments revealed a recognition performance of up to 95%. This result was obtained through experiments in which 10% to 40% of the data were evaluated. These findings are beneficial and can be applied to expression recognition in online learning media to monitor the audience's condition directly.
DIABETES MELLITUS ATTRIBUTE CLASSIFICATION USING THE NAIVE BAYES ALGORITHM BASED ON FORWARD SELECTION Dwi Puji Prabowo; Rama Aria Megantara; Ricardus Anggi Pramunendar; Yuslena Sari
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 2 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i2.146

Abstract

Diabetes Mellitus is a chronic condition that frequently results in death. Almost every nation has experienced and contributed to this rise in mortality. Consequently, several researchers are motivated to determine this disease's source and prevent the increase in mortality rates. The research was conducted in the field of informatics in partnership with health professionals to determine the causes of this condition. Many informatics researchers employ machine learning techniques to aid in analyzing existing data. This study suggests feature selection based on forward selection and the naive Bayes classification approach to determine this disease's primary aetiology. The results demonstrate that our proposed strategy can increase the classification accuracy of patients. The performance outcomes improved by 169%. According to this theory, it is also known that the primary cause of this disease is its dependence on body mass index and age. Therefore, additional research must explore these two variables' impact on various other disorders.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN) Muh Nasirudin Karim; Ricardus Anggi Pramunendar; Moch Arief Soeleman; Purwanto Purwanto; Bahtiar Imran
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1317.209-217

Abstract

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Semantic segmentation of pendet dance images using multires U-Net architecture Hendri Ramdan; Moh. Arief Soeleman; Purwanto Purwanto; Bahtiar Imran; Ricardus Anggi Pramunendar
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1316.329-338

Abstract

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
Implementation Of ETL E-Commerce For Customer Clustering Using RFM And K-Means Clustering Farrikh Alzami; Fikri Diva Sambasri; Rifqi Mulya Kiswanto; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p05

Abstract

E-commerce is the activity of selling and buying goods through an online system or online. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One of the things that need to be considered in this business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers so that they can maintain good relations with customers and can increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature. There are also several ETL stages of research that must be carried out, namely taking data from the open public data site (Kaggle), which consist of more than 9 tables (extract), then merging the data to select some data that needs to be used (transform and load), understanding data by displaying it in graphic form, conducting data selection to select features / attributes. which is in accordance with the proposed method, performs data preprocessing, and creates a model to get the cluster. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method Irham Ferdiansyah Katili; Mochamad Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4488

Abstract

Indonesia is a country rich in a variety of regional cultures. Regional airspace needs to be preserved so as not to become extinct. One of them is the local culture of Central Java Province, namely Javanese Character. In this modern era, globalization is growing in every country. The impact of globalization is increasingly widespread and developing in society. One effect of globalization is local people prefer foreign language skills to learn local languages. This study, applies the method of character recognition using a new combination workflow that contains Local Binary Pattern (LBP) and Information Gain. Then compare Support Vector Machine (SVM), k-Nearest Neighbor and Naïve Bayes. The LBP method is used to obtain an image's texture or shape characteristics. Information Gain is used for the feature selection algorithm, whereas SVM, k-Nearest Neighbor and Naïve ayes is used for the classification method. From previous research, the information gain method succeeded in increasing the accuracy by 2%. This research compares the SVM classification with another classification method, and the result shows that our proposed can improve classification performance. The best accuracy result using SVM classification gets 87,86%, at ten folds and cell size 64x64.
BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features Kristhina Evandari; M. Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4743

Abstract

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit Farrikh Alzami; Fikri Diva Sambasri; Mira Nabila; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1524.32-44

Abstract

E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware Fauzi Adi Rafrastaraa; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Etika Kartikadarma; Usman Sudibyo
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.854

Abstract

Malware is a type of software designed to harm various devices. As malware evolves and diversifies, traditional signature-based detection methods have become less effective against advanced types such as polymorphic, metamorphic, and oligomorphic malware. To address this challenge, machine learning-based malware detection has emerged as a promising solution. In this study, we evaluated the performance of several machine learning algorithms in detecting malware and applied Principal Component Analysis (PCA) to the best-performing algorithm to reduce the number of features and improve performance. Our results showed that the Random Forest algorithm outperformed Adaboost, Neural Network, Support Vector Machine, and k-Nearest Neighbor algorithms with an accuracy and recall rate of 98.3%. By applying PCA, we were able to further improve the performance of Random Forest to 98.7% for both accuracy and recall while reducing the number of features from 1084 to 32.
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Azzahra, Tarissa Aura Bastiaans, Jessica Carmelita Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Darmawan, Aditya Aqil Dewi Nurdiyah Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Fikri Diva Sambasri Firmansyah, Muhammad Ilham Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Hasan Asari Haydar, Muhammad Rifqi Fajrul Hendri Ramdan Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Kristhina Evandari Kurnia Prayoga Wicaksono Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Megantara, Rama Aria Mira Nabila Moch Arief Soeleman Mochamad Arief Soeleman Mochamad Hariadi Moh. Arief Soeleman Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muljono, - Muslih Muslih Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Prabowo, D.P. Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ratmana, Danny Oka Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Sri Winarno Stefanus Santosa Sulistyowati, Tinuk Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan