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All Journal JOIN (Jurnal Online Informatika) Jurnal Rekayasa Hijau Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat
Setyaningrum, Anisa Putri
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PENGEMBANGAN PEMBELAJARAN TATA SURYA (ASTRA QUEST) BERBASIS KUIS GAME AR UNTUK PANTI ASUHAN PYI YATIM & ZAKAT CAB. CIKUTRA Chazar, Chalifa; Setyaningrum, Anisa Putri; Faa’iz, Prayoga Anwar; Weninggalih, Sanjaya Raga; Wijaya, Freza Taruna Nugraha; Rivano, Kevin Faza
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 10: Maret 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i10.9453

Abstract

Penelitian ini membahas penerapan teknologi Augmented Reality (AR) sebagai media pembelajaran interaktif untuk meningkatkan motivasi belajar anak-anak di Panti Asuhan PYI Yatim & Zakat Cabang Cikutra, Bandung. Metode yang digunakan mencakup perancangan sistem, pengembangan aplikasi kuis berbasis AR, serta implementasi dan evaluasi hasilnya. Aplikasi yang dikembangkan, bernama Astra Quest, mengintegrasikan fitur visualisasi tiga dimensi dan kuis interaktif untuk memperkaya pengalaman belajar. Hasil implementasi menunjukkan bahwa aplikasi ini berhasil meningkatkan keterlibatan dan motivasi belajar anak-anak, dengan pengurus panti mampu melanjutkan program secara mandiri setelah diberikan pelatihan. Uji coba aplikasi membuktikan fungsionalitas yang stabil dan antarmuka yang intuitif, menjadikannya alat bantu pembelajaran yang efektif. Penelitian ini menyimpulkan bahwa teknologi AR berpotensi besar dalam menciptakan pengalaman belajar yang lebih menarik dan efektif, serta merekomendasikan pengembangan fitur tambahan di masa depan untuk memperluas manfaat aplikasi ini.
Klasifikasi Cyberbullying Pada Tweet Bahasa Sunda Dengan Menggunakan Hybrid Learning Model Setyaningrum, Anisa Putri; Nadhif, Muhammad Fahmy
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 9, No 1 (2025)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v9i1.58-69

Abstract

ABSTRAKCyberbullying dalam bahasa Sunda semakin marak di media sosial, dengan kasus seperti penghinaan fisik, body shaming, dan ancaman yang dapat berdampak negatif pada korban. Namun, deteksi otomatis masih menghadapi tantangan, terutama dalam keterbatasan dataset dan efektivitas metode pemrosesan bahasa alami. Penelitian ini bertujuan untuk mengembangkan sistem deteksi cyberbullying bahasa Sunda menggunakan gabungan model stemming dan hybrid learning. Peneliti menerapkan beberapa model machine learning yaitu random forest dan Support Vector Machine (SVM) serta model deep learning yaitu convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM), CNN, dan BiLSTM. Peneliti melakukan eksperimen untuk mengevaluasi kinerja masing-masing model dengan mengukur akurasi dan F1-score. Berdasarkan hasil penelitian, model hybrid learning memperoleh kinerja terbaik dengan akurasi sebesar 97,3% dan F1-score sebesar 97%. Selain itu, waktu pelatihan pada CNN-BiLSTM lebih cepat dibandingkan dengan model lainnya yaitu sekitar 30 detik per epoch.Kata kunci: Bahasa Sunda, Cyberbullying, Hybrid LearningABSTRACTCyberbullying in the Sundanese language is becoming more common on social media, with cases like physical insults, body shaming, and threats that can seriously affect victims. However, detecting it automatically remains challenging, mainly due to limited datasets and the difficulty of processing the language effectively. This study aims to develop a Sundanese cyberbullying detection system using a combination of stemming and hybrid learning models. The researchers applied several machine learning models, namely random forest and Support Vector Machine (SVM), and deep learning models, namely convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM), CNN, and BiLSTM. The researchers conducted experiments to evaluate the performance of each model by measuring the accuracy and F1-score. Based on the results, the hybrid learning model achieved the best performance, with an accuracy of 97.3% and an F1-score of 97%. Besides that, the training time on CNN-BiLSTM is faster than the others which is approximately 30 seconds per epoch.Keywords: Sundanese, Cyberbullying, Hybrid Learning
Implementation of Generative Adversarial Network to Generate Fake Face Image Pardede, Jasman; Setyaningrum, Anisa Putri
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.790

Abstract

In recent years, many crimes use technology to generate someone's face which has a bad effect on that person. Generative adversarial network is a method to generate fake images using discriminators and generators. Conventional GAN involved binary cross entropy loss for discriminator training to classify original image from dataset and fake image that generated from generator. However, use of binary cross entropy loss cannot provided gradient information to generator in creating a good fake image. When generator creates a fake image, discriminator only gives a little feedback (gradient information) to generator update its model. It causes generator take a long time to update the model. To solve this problem, there is an LSGAN that used a loss function (least squared loss). Discriminator can provide astrong gradient signal to generator update the model even though image was far from decision boundary. In making fake images, researchers used Least Squares GAN (LSGAN) with discriminator-1 loss value is 0.0061, discriminator-2 loss value is 0.0036, and generator loss value is 0.575. With the small loss value of the three important components, discriminator accuracy value in terms of classification reaches 95% for original image and 99% for fake image. In classified original image and fake image in this studyusing a supervised contrastive loss classification model with an accuracy value of 99.93%.
Egg Weight Estimation Based on Image Processing using Mask R-CNN and XGBoost Pardede, Jasman; Rawosi, Muhammad Fadlansyah Zikri Akhiruddin; Setyaningrum, Anisa Putri; Milenio, Rizka Milandga; Chazar, Chalifa
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.1004

Abstract

Manually measuring egg weight in the context of livestock and the food industry can pose various problems, including time and labor requirements, the risk of egg damage, consistency and accuracy, and limitations on production scale. To address these issues, an automated egg weight estimation system is essential. This study proposes integrating computer vision and machine learning into a unified workflow that combines segmentation, classification, and regression for practical weight estimation. The proposed pipeline employs Mask R-CNN for egg segmentation, Random Forest (RF) classifier for egg type classification based on color features, and XGBoost for regression using morphological, geometric, color features, and egg type as predictors. The dataset used is 720 images, consisting of 20 eggs (10 chicken and 10 duck), each photographed from 36 rotational angles, and was collected with Ground Truth (GT) weights obtained from a digital scale. Experimental findings show that the RF classifier achieved perfect accuracy (precision, recall, and F1-score = 1.00) in distinguishing chicken and duck eggs. The XGBoost regressor obtained a training performance of MAE = 1.07 g and R² = 0.68, and a validation performance of MAE = 0.23 g and R² = 0.80 under 10-fold grouped cross-validation. Although a Support Vector Regressor baseline reached higher training accuracy (MAE = 0.22 g, R² = 0.96), it failed to generalize on validation (R² 0), highlighting XGBoost’s robustness. The feature importance analysis revealed that there are 4 (four) important features for building an estimation model, namely: Hu moments, eccentricity, elongation, and diagonal length, while color statistics played a complementary role. The novelty of this work lies in combining deep segmentation, color-based classification, and feature-driven regression into a unified framework specifically for egg weight estimation, showing its feasibility as a proof of concept and laying the foundation for future large-scale, calibrated, and externally validated deployment.
Co-Authors Chazar, Chalifa Faa’iz, Prayoga Anwar Jasman Pardede Milenio, Rizka Milandga Nadhif, Muhammad Fahmy Rawosi, Muhammad Fadlansyah Zikri Akhiruddin Rivano, Kevin Faza Weninggalih, Sanjaya Raga Wijaya, Freza Taruna Nugraha