Article Per Year (5 Year)
p-Index From 2020 - 2025
1.037
P-Index
This Author published in this journals
All Journal JOIN (Jurnal Online Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) EXPLORER Intellect : Indonesian Journal of Learning and Technological Innovation
Agung Wahana
Fakultas Teknologi dan Sain, Universitas Islam Negeri Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naïve Bayes Adelia Irawan, Febby; Rialdy Atmadja, Aldy; Wahana, Agung
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1181

Abstract

Bidang perbankan merupakan salah satu yang berkembang dan mengikuti tren digitalisasi. Adanya bank digital merupakan inovasi yang dilakukan pada bidang perbankan dalam memberikan pelayanan dengan menggunakan media elektronik atau digital. Teknologi yang dikembangkan memungkinkan pengguna hanya cukup mengakses transaksi dalam suatu aplikasi dengan bermodalkan smartphone yang didistribusikan melalui Google Playstore. Ulasan-ulasan pengguna (review) pada Google Playstore ini tersedia untuk membantu meningkatkan performa dari aplikasi dan menjadi landasan bagi perusahaan dalam mengembangkan aplikasi perbankan. Akan tetapi, terdapat kendala jika banyaknya ulasan dan sulit untuk memilah dan mengolahnya secara manual sehingga diperlukan analisis sentimen ulasan pengguna pada aplikasi-aplikasi bank digital. Pada penelitian ini analisis sentimen dilakukan dengan menggunakan algoritma Naïve Bayes. Adapun pendekatan metode yang dilakukan dengan menggunakan CRISP-DM sebagai standar yang umum dalam melakukan riset data mining. Hasil dalam penelitian ini menunjukkan bahwa penerapan model klasifikasi dengan menggunakan Algoritma Naïve Bayes dengan data ulasan menghasilkan 46% ulasan positif dan 54% ulasan negatif. Selain itu, nilai akurasi tertinggi dari kinerja algoritma Naïve Bayes dengan menggunakan pembagian data training dan testing dengan persentase 70:30 menghasilkan akurasi yang optimal mencapai 89%.
Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Analisis Sentimen Pengguna Aplikasi Tokopedia Lillah, M. Rival Ridautal Lillah; Maylawati, Dian Sa’adillah; Zulfikar, Wildan Budiawan; Uriawan, Wisnu; Wahana, Agung
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 2 No. 2 (2023): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

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

Abstract

A marketplace is a platform where sellers can come together and sell their goods or services to customers without physical meetings. In the past few decades, marketplaces have become the most popular platform for business sellers to sell their products. Becoming the number 1 marketplace in Indonesia with the most visitors on average is the right marketplace in 2023, namely Tokopedia. However, most people are skeptical of products they have never purchased or used. User reviews play an important role in product marketing, especially on Tokopedia. Reviews help potential customers build trust in the products and services offered by the seller. To analyze reviews quickly and precisely, a sentiment analysis process is needed. Natural Processing Language (NLP) and text mining algorithms are used to classify reviews as positive, or negative. One of the methods used is the K-Nearest Neighbor (KNN) algorithm, which is used to classify Tokopedia user reviews in the Play Store and App Store. The dataset consists of 1000 comment data from the Play Store and 1000 data from the App Store. A total of 2000 comments consisting of 2 labels, namely positive and negative for modeling. Meanwhile, for testing, there were 885,092 comments from the Play Store and 4000 comments from the App Store. Total 889,092 for unlabeled test data. The prediction results on the app store dataset show that there are 97.0% positive label predictions and only 3.0% negative label predictions. Abstrak Marketplace adalah platform tempat penjual dapat berkumpul dan menjual barang atau jasa mereka kepada pelanggan tanpa pertemuan fisik. Dalam beberapa dekade terakhir, pasar telah menjadi platform paling populer bagi penjual bisnis untuk menjual produk mereka. Menjadi marketplace nomor 1 di Indonesia dengan rata-rata pengunjung terbanyak adalah marketplace yang tepat di tahun 2023 yaitu Tokopedia. Namun, kebanyakan orang skeptis terhadap produk yang belum pernah mereka beli atau gunakan. Ulasan pengguna memegang peran penting dalam pemasaran produk, terutama di Tokopedia. Ulasan membantu calon pelanggan membangun kepercayaan terhadap produk dan layanan yang ditawarkan oleh penjual. Untuk menganalisis ulasan dengan cepat dan tepat, diperlukan proses analisis sentimen. Natural Processing Language (NLP) dan algoritma text mining digunakan untuk mengklasifikasikan ulasan sebagai positif, atau negatif. Salah satu metode yang digunakan adalah algoritma K-Nearest Neighbor (KNN), yang digunakan untuk mengklasifikasikan ulasan pengguna Tokopedia di play store dan app store. Dataset terdiri dari 1000 data komentar dari play store dan 1000 data dari app store. Total 2000 komentar yang terdiri dari 2 label yaitu positif dan negatif untuk pemodelan. Sedangkan untuk pengujian 885.092 komentar dari play store dan 4000 komentar dari app store. Total 889.092 untuk data pengujian yang belum dilabeli. Hasil prediksi pada dataset app store menunjukkan terdapat 97,0% prediksi label positif dan hanya 3,0% prediksi label negatif.
IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK USING MOBILENETV2 TO DISTINGUISH HUMAN AND ARTIFICIAL INTELLIGENCE PAINTING Santosa, Dwi Bagia; Wahana, Agung; Uriawan, Wisnu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3827

Abstract

The advancement of artificial intelligence technology has had a significant impact on various fields, including painting. Artificial intelligence is now able to create works of art that resemble paintings produced by humans with a high level of detail and complexity. However, this progress has also created new problems in the world of painting, namely the difficulty in distinguishing between works produced by humans and those created by artificial intelligence. This problem has an impact on the originality of the artwork and has implications for aspects of ethics and creativity. This study aims to develop a deep learning model that can classify human and artificial intelligence paintings, and overcome the challenges in distinguishing between the two. The methodology used is the Cross Industry Standard Process for Data Mining (CRISP-DM), with a dataset consisting of 1,000 painting images. The architecture used is MobileNetV2, implemented using TensorFlow to build a Convolutional Neural Network (CNN). Techniques such as data preparation, data labeling, data splitting, resizing, and data augmentation are applied to improve model performance. Six test scenarios were carried out with variations in the learning rate, number of epochs, and freeze or unfreeze configurations on the base model. The results showed that the best model with a learning rate of 0.0001, base model unfreeze, and 5 epochs managed to achieve an accuracy of 97%, without any indication of overfitting or underfitting. This model was then implemented on an Android application in TFLite format, which can predict image classes with a confidence level of 89.98%.
Klasifikasi Irama Murottal Al-Quran Menggunakan Metode CNN dengan Perbandingan Arsitektur ResNet50 dan VGG16 Agustin, Ilham Rizky; Wahana, Agung; Atmadja, Aldy Rialdy
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6440

Abstract

The understanding of murottal Al-Quran among the Indonesian population remains relatively limited. One contributing factor is the difficulty in distinguishing between different murottal rhythms, which requires specialized expertise. Additionally, traditional murottal learning methods necessitate direct interaction with expert teachers, which is not always accessible to everyone. These challenges highlight the importance of developing technology to assist in identifying murottal rhythms. This study developed a murottal rhythm classification model using Convolutional Neural Networks (CNN) with transfer learning, employing two popular architectures: VGG16 and ResNet50. Audio data were processed using Short-Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction for analysis.The results showed that the ResNet50 architecture with MFCC-extracted data achieved the best performance, with a training accuracy of 92%, validation accuracy of 85%, and testing accuracy of 86%. Additionally, the model achieved precision, recall, and F1-score values of 0.87 and 0.86, indicating strong generalization capabilities. Conversely, the VGG16 architecture with STFT and MFCC-extracted data demonstrated lower accuracy compared to ResNet50. The findings are expected to provide an innovative solution for developing a self-learning system based on technology to facilitate understanding of murottal rhythms in the Al-Quran.
Implementation of the Simple Multi Attribute Rating Technique Method (SMART) in Determining Toddler Growth Wahana, Agung; Alam, Cecep Nurul; Rohmah, Siti Nur
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.634

Abstract

Toddler nutritional status is an important factor in efforts to reduce child mortality. The development of community nutrition can be monitored through the results of recording and reporting of community nutrition improvement programs reflected in the results of weighing infants and toddlers every month at the Pos Pelayanan Terpadu (Posyandu/ Integrated Service Post) , where these efforts aim to maintain and improve health and prevent and cope with the emergence of public health problems, especially aimed at toddlers. However, in carrying out the health service activities of Medical Officers, faced with an important problem that is still difficult in providing information related to the results of monitoring the growth and development of infants, because information on growth and development of infants owned is obtained from the data collection done manually such as; make records and calculations to find out the condition of a toddler declared good, less, or bad. Implementation of the SMART method in Toddler's growth and development, this method can be used based on the weights and criteria that have been determined. The criteria used are based on the Anthropometric index assessment criteria. The results of the analysis are the results of ranking the greatest value to be used as the material in the decision-making process.
Co-Authors Adelia Irawan, Febby Agustin, Ilham Rizky Aldy Rialdy Atmadja Cecep Nurul Alam, Cecep Nurul Lillah, M. Rival Ridautal Lillah Maylawati, Dian Sa’adillah Rialdy Atmadja, Aldy Rohmah, Siti Nur Santosa, Dwi Bagia Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu