Garuda - Garba Rujukan Digital

Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Gerhana, Yana Aditia; Atmadja, Aldy Rialdy; Padilah, Muhamad Farid
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.

Comparison of search algorithms in Javanese-Indonesian dictionary application Yana Aditia Gerhana; Nur Lukman; Arief Fatchul Huda; Cecep Nurul Alam; Undang Syaripudin; Devi Novitasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.14882

This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n2, Knuth Morris Pratt and Horspool 20n2 each.

Breakdown film script using parsing algorithm Agung Wahana; Diena Rauda Ramdania; Dhanis Al Ghifari; Ichsan Taufik; Faiz M. Kaffah; Yana Aditia Gerhana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14849

Breakdown script is a breakdown of the scenario into parts that describe each detail of the scene for shooting. The scenario is broken down into more detailed parts using the parsing algorithm. The film script used is a script in Bahasa Indonesia. The process starts from the film script file/scenario in FBX format uploaded to the website then is solved using a parsing algorithm into film elements such as cast members, extras, props, costumes, makeup, vehicles, stunts, special effects, music and sound. The results of this breakdown into sheets according to film elements. The purpose of this research is to produce breakdown sheets from film scripts according to film elements. The parsing algorithm test results showed the correct results of 12 scenes out of 19 scenes.

Game and Application Purchasing Patterns on Steam using K-Means Algorithm Aulia, Salman Fauzan Fahri; Gerhana, Yana Aditia; Nurlatifah, Eva
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2214

Online games are visual games that utilize the internet or LAN networks. With the growth of the gaming industry, platforms like Steam offer a wide variety of games, making it challenging for users to decide which game to play. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to address this issue by understanding user preferences. The k-means algorithm clusters game data based on similar characteristics, helping users and developers identify the most popular game types. Data sourced from Kaggle, obtained through the Steam API and Steamspy, consists of 85,103 entries. A normalization process is applied to enhance calculation accuracy. The elbow method determines the optimal number of clusters, resulting in three clusters from the k-means algorithm. The evaluation includes the silhouette coefficient, which measures the proximity between variables, and precision purity, which compares labels by assigning a value of 1 (actual) or 0 (false). The study finds an average silhouette coefficient of 0.345 and a precision purity value of 0.734, indicating that the k-means algorithm performs optimally based on the precision purity metric. The findings reveal that free-to-play games are the most popular among users, while the "Animation & Modelling" category is the most expensive based on price comparisons

Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language Muharam, Aldi Fahluzi; Gerhana, Yana Aditia; Maylawati, Dian Sa'adillah; Ramdhani, Muhammad Ali; Rahman, Titik Khawa Abdul
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.110-121

This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for Indonesians. This research uses the transformer model to develop the proposed Bert2Bert and Bert2Bert+Xtreme models. This research utilizes the Liputan6 data set, which comprises news data along with summary references spanning 10 years from October 2000 to October 2010, and is commonly used in many automatic text summarization studies. The model evaluation results using ROUGE-1, ROUGE-2, ROUGE-L, and BERTScore indicate that the proposed model exhibits a slight improvement over previous research models, with Bert2Bert performing better than Bert2Bert+Xtreme. Despite the challenges posed by limited reference summaries for Indonesian documents, content-based analysis using readability metrics, including FKGL, GFI, and Dwiyanto Djoko Pranowo, revealed that the summaries produced by Bert2Bert and Bert2Bert+Xtreme are at a moderate readability level, meaning they are suitable for mature readers and align with the news portal’s target audience.

Klasifikasi Penyakit Daun Kopi Arabika Berbasis Gambar Menggunakan Model Convolutional Neural Networks DenseNet121 Solehudin, Muhammad Alwy; Gerhana, Yana Aditia; Taufik, Ichsan
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6407

Detection of Arabica coffee leaf diseases is crucial for improving the quality and yield of coffee crops. This study aims to apply the DenseNet121 Convolutional Neural Network model to identify three types of diseases on Arabica coffee leaves, namely Rust, Phoma, and Miner. The data used consists of images of Arabica coffee leaves, which are divided into training, validation, and test sets. The model was trained using the Adamax optimizer with hyperparameters such as a maximum of 30 epochs and a batch size of 32. During training, the model achieved a validation accuracy of 98.86% before being stopped by the early stopping callback at epoch 28 to prevent overfitting. Model evaluation using a confusion matrix resulted in 97% accuracy on the test data, with excellent precision, recall, and F1-score values for most categories, particularly for the Healthy, Miner, and Phoma classes. The Rust class showed lower recall due to data imbalance in the test set. The results of this study demonstrate that the DenseNet121 model is reliable for detecting diseases on Arabica coffee leaves with high accuracy and provides an important contribution to the technology of plant health monitoring, which can assist farmers in early detection and improve coffee crop productivity.

Implementasi Model CNN ResNet50V2 untuk Klasifikasi Pneumonia pada Citra X-Ray Anwar, Muhammad Afian; Gerhana, Yana Aditia; Syaripudin, Undang
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1538

The utilization of technology to build models that can classify pneumonia medical images automatically is needed for early diagnosis. This study aims to implement a Convolutional Neural Network (CNN) model with ResNet50V2 architecture that has been proven to have high accuracy in medical image classification. The model adopts a deep and efficient residual architecture, which facilitates deeper training of the model without suffering from vanishing gradient problem. This study went through four main stages: pneumonia and normal X-ray image data collection, data pre-processing (including set division, transformation, and augmentation), modeling using CNN with hyperparameter tuning, and model evaluation. Evaluation was performed using accuracy, F1-score, and Confusion Matrix metrics. The CNN model with ResNet50V2 as the backbone achieved 97% accuracy, showing excellent performance in differentiating between pneumonia and normal despite a small amount of misclassification. Although this model showed impressive results, challenges such as potential misclassification in cases with unclear or ambiguous images remain. Compared to previous approaches, this model offers advantages in accuracy and processing efficiency thanks to the use of a deeper and more sophisticated ResNet50V2. These advantages are expected to improve the precision of automated diagnosis in future medical applications.

Game and Application Purchasing Patterns on Steam using K-Means Algorithm Aulia, Salman Fauzan Fahri; Gerhana, Yana Aditia; Nurlatifah, Eva
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2214

Online games are visual games that utilize the internet or LAN networks. With the growth of the gaming industry, platforms like Steam offer a wide variety of games, making it challenging for users to decide which game to play. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to address this issue by understanding user preferences. The k-means algorithm clusters game data based on similar characteristics, helping users and developers identify the most popular game types. Data sourced from Kaggle, obtained through the Steam API and Steamspy, consists of 85,103 entries. A normalization process is applied to enhance calculation accuracy. The elbow method determines the optimal number of clusters, resulting in three clusters from the k-means algorithm. The evaluation includes the silhouette coefficient, which measures the proximity between variables, and precision purity, which compares labels by assigning a value of 1 (actual) or 0 (false). The study finds an average silhouette coefficient of 0.345 and a precision purity value of 0.734, indicating that the k-means algorithm performs optimally based on the precision purity metric. The findings reveal that free-to-play games are the most popular among users, while the "Animation & Modelling" category is the most expensive based on price comparisons

Rancangan Bangun Media Pembelajaran Berbasis Multimedia Interaktif Menggunakan Augmented Reality pada Pelajaran Komputer dan Jaringan Dasar di Sekolah SMA-IT Maroko Sidik, Dikdik Firman; Gerhana, Yana Aditia
Jurnal Abdimas Peradaban Vol. 4 No. 1 (2023): Jurnal Abdimas Peradaban
Publisher : Global Writing Academica Researching and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/kmjhed82

Media pembelajaran merupakan salah satu faktor penting dalam proses pembelajaran. Penggunaan media pembelajaran yang tepat dapat meningkatkan efektivitas dan efisiensi pembelajaran. Augmented reality (AR) merupakan salah satu teknologi yang dapat digunakan sebagai media pembelajaran. AR dapat menghadirkan objek virtual ke dalam dunia nyata, sehingga dapat memberikan pengalaman belajar yang lebih interaktif dan menarik. Penelitian ini bertujuan untuk merancang dan menerapkan media pembelajaran berbasis multimedia interaktif menggunakan AR pada mata pelajaran komputer dan jaringan dasar di sekolah SMA-IT Maroko. Media pembelajaran ini menggunakan model pembelajaran Auditory, Intellectually, and Repetition (AIR) untuk meningkatkan pemahaman siswa terhadap materi topologi jaringan. Media pembelajaran ini terdiri dari dua komponen utama, yaitu aplikasi AR dan materi pembelajaran. Aplikasi AR dibuat dengan menggunakan Unity 3D. Materi pembelajaran meliputi penjelasan teori dan contoh-contoh topologi jaringan. Hasil penelitian menunjukkan bahwa media pembelajaran berbasis multimedia interaktif menggunakan AR dapat meningkatkan pemahaman siswa terhadap materi topologi jaringan. Hal ini terlihat dari hasil uji Uji kevalidan dan uji keefektifan melibatkan 1 orang ahli materi, 1 orang ahli media dan siswa kelas X TIK yang dibagi menjadi kelompok kecil berjumlah 3 orang dan kelompok besar berjumlah 20 orang siswa. Instrumen menggunakan angket atau kuesioner. Pengambilan data respon siswa dilakukan pada 20 Agustus 2023 di Sekolah SMK-IT Maroko Cibalong. Hasil rata-rata uji kevalidan pada ahli materi yaitu 92%, hasil rata-rata uji kevalidan pada ahli media yaitu 92% dan, hasil rata-rata uji keefektifan pada siswa yaitu 94,88%.

Implementasi Teknologi Blockchain dalam Pengembangan Aplikasi Web Terdesentralisasi untuk Pengelolaan Data Pos Pelayanan Terpadu: Studi Kasus: Posyandu Mawar Lingkungan Gibug Qomaruddin, Nurhadi; Gerhana, Yana Aditia; Taufik, Ichsan; Slamet, Cepy; Firdaus, Muhammad Deden
ISTEK Vol. 14 No. 1 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i1.2112

The Integrated Service Post (Posyandu) is a community-based health service established by the government, playing an important role in monitoring child health, including efforts to reduce infant and child mortality rates. However, data management at Posyandu is generally still conducted manually using paper-based records, making it prone to data loss and inefficient in terms of access and tracking. One common approach to overcoming these challenges is the use of distributed data systems, which allow data storage and processing to occur across multiple computers in different locations. Nevertheless, many of these systems still rely on centralized servers, making them vulnerable to data breaches and manipulation due to the single point of storage. To address this issue, this research proposes the development of a decentralized web application based on blockchain technology as a solution for secure, transparent, and traceable data management. The application is developed using smart contracts written in Solidity, deployed on the Ethereum blockchain, with Hardhat as the backend framework and React.js as the user interface. The system was developed using a prototyping methodology and evaluated through black-box testing to assess its functional performance. Test results show that the application is capable of managing data effectively, while maintaining a high level of security and transparency. By adopting blockchain technology, the system enhances the effectiveness and efficiency of Posyandu’s data management, while ensuring data integrity and traceability within a decentralized environment.

Title

Found 22 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 22 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 22 Documents
Search