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All Journal JOIV : International Journal on Informatics Visualization JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Charity : Jurnal Pengabdian Masyarakat NUMBERS: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Journal of Computer Engineering, Electronics and Information Technology
Devi Aprianti Rimadhani Agustini
Universitas Pendidikan Indonesia
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mathematics Physics Social Sciences Other

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Deteksi Konten Pornografi Menggunakan Convolutional Neural Network Untuk Melindungi Anak Dari Bahaya Pornografi Muhammad Taufik Dwi Putra; Mochamad Iqbal Ardimansyah; Devi Aprianti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4793

Abstract

Education is one thing that must be arranged as early as conceivable in arrange to realize a quality era. When talking about education today, it cannot be separated from technology. Where we can see that technology has been used in various fields. In the field of education, one of them is the use of the internet network. However, the use of this technology has quite a bad side. Especially for elementary-level students or the age of children. That is the bad impact of exposure to pornography. Exposure to pornography is very dangerous and can damage children both psychologically and mentally. Therefore, it is important to minimize the risk of exposure to pornography. To overcome this, there are many methods that can be used. Like detecting pornographic content automatically and blocking it. One technique that can be developed to detect pornographic content is Artificial Neural Networks. However, so that the image input can be handled effectively, the model of the Artificial Neural Network has been varied into a Convolutional Neural Network (CNN) technique. So it has the ability to recognize objects for image data. The model built in this study was trained using a dataset that has been adapted to the definition of pornography in Indonesia. From the tests that have been carried out on the CNN model that was built, the best accuracy rate is 94.24%. in detecting images that fall into the category of pornographic content.
End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study Wirmanto Suteddy; Devi Aprianti Rimadhani Agustini; Anugrah Adiwilaga; Dastin Aryo Atmanto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1293

Abstract

Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work.
PELATIHAN PENERAPAN APLIKASI KIDS NOTE SEBAGAI BUKU PENGHUBUNG DIGITAL DI SEKOLAH Ana Rahma Yuniarti; Devi Aprianti Rimadhani Agustini; Wirmanto Sutedy; Kuswanto Kuswanto; Naufal Nurdiansyah; Aulia Putri Cendekia; Bhima Arya Daniswara
Charity : Jurnal Pengabdian Masyarakat Vol 6 No 1a (2023): Special Issue
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v6i1a.5905

Abstract

Berdasarkan instruksi dari Badan Akreditasi Nasional Pendidikan Anak Usia Dini (BAN-PAUD), salah alat yang harus disediakan oleh pihak sekolah adalah Buku Penghubung, sebagai media untuk monitoring dan pelaporan tumbuh kembang siswa kepada orang tuanya. Sekolah Alam Gaharu (SAG) Bandung telah menggunakan Buku Penghubung, namun sistemnya masih tergolong konvensional. Pada sistem konvensional ini ditemukan permasalahan-permasalahan yang mengakibatkan ketidakselarasan antara wali murid dan guru/fasilitator kelas dalam hal monitoring tumbuh kembang anak, seperti: buku yang rentan rusak atau hilang, kurang privasi, tidak real-time dan tidak dapat mengakomodasi file foto/video kegiatan. Dengan kemajuan teknologi pada era sekarang, memungkinkan dilakukan transformasi buku penghubung berbasis kertas menjadi bentuk aplikasi digital berbasis website maupun Android/iOS. Untuk itu, pada kegiatan pengabdian masyarakat ini dikenalkan sebuah aplikasi Buku Penghubung Digital bernama Kids Note. Pelatihan diberikan kepada 50 peserta yang terdiri dari orang tua/wali murid dan guru/fasilitator di Sekolah Alam Gaharu. Berdasarkan survei yang dibagikan pasca kegiatan, didapatkan hasil bahwa 84% peserta memahami cara penggunaan aplikasi Kids Note dan 92% diantaranya menyatakan aplikasi Kids Note mampu mengakomodasi kebutuhan monitoring dan pelaporan tumbuh kembang anak di SAG.
Perancangan Media Pembelajaran Matematika Berbasis Android Menggunakan MIT App Inventor dan Geogebra pada Materi Kalkulus Devi Aprianti Rimadhani Agustini; Muhamad Rizki Wahyuddin; Rifty Pradana Gunawan
NUMBERS : Jurnal Pendidikan Matematika & Ilmu Pengetahuan Alam Vol. 1 No. 2 (2023): April - Juni
Publisher : CV. ADIBA AISHA AMIRA

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

Abstract

Perkembangan teknologi yang cukup pesat memberi dampak yang besar termasuk dalam dunia pendidikan. Berbagai media pembelajaran kini dikembangkan dengan sentuhan teknologi yang akrab disebut sebagai pembelajaran digital, tidak terkecuali dalam pembelajaran matematika. Salah satu media pembelajaran digital yang banyak dikembangkan adalah media pembelajaran berbasis Android. Pada penelitian ini, aplikasi pembelajaran matematika berbasis Android dibangun menggunakan MIT App Inventor dan Geogebra. Tujuan dari penelitian ini adalah mengembangkan aplikasi android untuk pembelajaran matematika pada materi mengenai kalkulus khususnya kurva ketinggian suatu fungsi dua peubah. Metode yang digunakan dalam penelitian ini melibatkan model pengembangan Multimedia Development  Life Cycle (MDLC) atau metode Luther yang terdiri dari enam tahapan, yaitu tahap konsep, perancangan, pengumpulan materi, pembuatan aplikasi, pengujian dan distribusi. Adapun pengujian perangkat lunak dilakukan melalui metode black-box. Hasil penelitian menunjukkan penggunaan model MDLC dapat digunakan dalam membangun media pembelajaran dengan baik dan sesuai dengan kebutuhan fungsional aplikasi.
Sentiment Analysis of Twitter Users’ Opinion Data Regarding the Use of ChatGPT in Education Jezzy Putra Munggaran; Ahmad Ali Alhafidz; Maulana Taqy; Devi Aprianti Rimadhani Agustini; Munawir Munawir
Journal of Computer Engineering, Electronics and Information Technology Vol 2, No 2 (2023): COELITE: Volume 2, Issue 2, 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/coelite.v2i2.59645

Abstract

This article presents a sentiment analysis of Twitter users' opinions regarding the use of ChatGPT in education. ChatGPT, an AI chatbot developed by OpenAI, has gained significant attention for its ability to provide detailed responses across various knowledge domains. However, concerns have been raised about its occasional inclusion of inaccurate information. This study aims to analyze the sentiment of Twitter users' opinions towards ChatGPT in education and evaluate its accuracy. The sentiment analysis process involves data crawling, labelling, preprocessing, sentiment analysis, and evaluation. Data is collected from Twitter using the RapidMiner Studio tool and labelled as positive or negative sentiment based on the presence of positive or negative words. Preprocessing techniques are applied to standardize and reduce the volume of words in the data. The sentiment analysis classification is performed using machine learning algorithms, specifically Naive Bayes and Support Vector Machine (SVM). The accuracy, precision, and recall of the classification models are evaluated. The sentiment analysis results provide insights into Twitter users' overall sentiment towards ChatGPT in education. This study contributes to understanding Twitter users' opinions and sentiments regarding using ChatGPT in education. The findings can be valuable for educators and policymakers in assessing the potential impact of ChatGPT on academic integrity and the educational landscape.
Analisis Data Review Hotel di Google Maps Melalui Text Mining (Studi Kasus : Kabupanten Bandung) Jorgha Akram Aryadi; Yahya Aliman Aidil Basith; Munawir Munawir; Devi Aprianti Rimadhani Agustini
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 2 (2023): SEPTEMBER 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v7i2.938

Abstract

Penelitian ini bertujuan untuk menganalisis data review hotel di Google Maps melalui text mining di Kabupaten Bandung. Metode text mining digunakan untuk menggali informasi yang terkandung dalam teks ulasan pengguna hotel. Data review hotel diambil dari platform Google Maps dan diolah menggunakan metode Latent Dirichlet Allocation (LDA). Hasil analisis menunjukkan adanya bobot setiap kata dalam ulasan pengguna terkait hotel di Kabupaten Bandung. Temuan ini memberikan wawasan tentang kebersihan, fasilitas, pelayanan, dan pengalaman pengguna yang dapat digunakan sebagai bahan evaluasi dan pengembangan hotel di daerah tersebut. Hasil penelitian didapatkan faktor daya tarik yang paling dominan ialah Kualitas dan Pelayanan (37.60%), kemudian Fasilitas dan Lingkungan (34.54%), dilanjut dengan Aktivitas dan Hiburan (27.85%). Setelah analis LDA, data tersebut diperkuat dengan Analysis of Variance (ANOVA) One Way. Hasil yang diperoleh dari ANOVA adalah p-value bernilai 0.48578981747778355. Dalam kasus ini, p-value (0.48578981747778355) lebih besar dari tingkat signifikansi yang umumnya digunakan (0.05), yang menunjukkan bahwa tidak ada perbedaan signifikan antara kelompok fasilitas dan lingkungan, aktivitas dan hiburan, serta kualitas dan pelayanan pada hotel-hotel yang dianalisis.
Co-Authors Ahmad Ali Alhafidz Ana Rahma Yuniarti Anugrah Adiwilaga Aulia Putri Cendekia Bhima Arya Daniswara Dastin Aryo Atmanto Jezzy Putra Munggaran Jorgha Akram Aryadi Kuswanto Kuswanto Maulana Taqy Mochamad Iqbal Ardimansyah Muhamad Rizki Wahyuddin Muhammad Taufik Dwi Putra Munawir Munawir Naufal Nurdiansyah Rifty Pradana Gunawan Wirmanto Suteddy Yahya Aliman Aidil Basith