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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal ULTIMATICS Journal of Computer Science and Informatics Engineering (J-Cosine) JURNAL CEMERLANG: Pengabdian pada Masyarakat Jurnal Rekayasa Hijau MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Journal of Applied Data Sciences CONSEN: Indonesian Journal of Community Services and Engagement Jurnal Tera Scientific Journal of Informatics IPSSJ JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Publikasi Teknik Informatika
Jasman Pardede
Program Studi Informatika, Institut Teknologi Nasional Bandung
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IMAGE CAPTIONING MENGGUNAKAN METODE RESNET50 DAN LONG SHORT TERM MEMORY Raka Satria, Marius; Pardede, Jasman
Jurnal Tera Vol 2 No 2 (2022): Jurnal Tera (September 2022)
Publisher : Fakultas Teknik dan Informatika, Universitas Dian Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (465.942 KB)

Abstract

Kesalahpahaman manusia dalam mencari makna arti dari sebuah gambar menimbulkan kebingungan. Hanya karena struktur kalimat atau penggunaan kata bermakna makna lebih dari satu yang biasa disebut ambiguitas. Ambiguitas terjadi apabila arti dari kata, frasa, atau kalimat tidak pasti, maknanya lebih dari satu. Karena adanya keterkatian dengan kecerdasan buatan dalam membantu klasifikasi gambar untuk menghindari ambiguitas, penggunaan Image Captioning dimanfaatkan pada penelitian ini. Image Captioning menghasilkan deskripsi berbahasa alami. Mengambil makna dari sebuah gambar dibutuhkan tingkat pemahaman yang lebih tinggi dari klasfikasi dan detesi gambar. Permasalahan yang muncul dapat diselesaikan dengan penggabungan antara kecerdasan buatan dan jaringan syaraf tiruan. Kedua metode yang digunakan dalam penelitian ini adalah Resnet50 dan Long Short Term Memory. Resnet50 berfungsi untuk klasifikasi gambar dan LSTM jaringan syaraf tiruan untuk generate caption. Penelitian ini menggunakan BLEU scoring satu gram untuk memberi nilai pada caption yang telah dibuat. Score BLEU tertinggi adalah 79,7455% dan akurasi tertinggi yang didapat adalah 85,74% pada 100 epoch.
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.
Folk Games Image Captioning using Object Attention Akbar, Saiful; Sitohang, Benhard; Pardede, Jasman; Amal, Irfan; Yunastrian, Kurniandha; Ahmada, Marsa; Prameswari, Anindya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4708

Abstract

The result of a deep learning-based image captioning system with encoder-decoder framework relies heavily on the image feature extraction technique and the caption-based model. The accuracy of the model is heavily influenced by the proposed attention mechanism. The inability to distinguish between the output of the attention model and the input expectation of the decoder can cause the decoder to give incorrect results. In this paper, we proposed an object-attention mechanism using object detection. Object detection outputs a bounding box and an object category label, which is then used as an image input into VGG16 for feature extraction and into a caption-based LSTM model. The experimental results showed that the system with object attention performed better than the system without object attention. BLEU-1, BLEU-2, BLEU-3, BLEU-4, and CIDER scores for the image captioning system with object attention improved 12.48%, 17.39%, 24.06%, 36.37%, and 43.50% respectively compared to the system without object attention.
Hepatitis Identification using Backward Elimination and Extreme Gradient Boosting Methods Pardede, Jasman; Nurrohmah, Desita
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.302-313

Abstract

Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted. The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated. Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied. Methods: XGBoost classification, capable of handling machine learning problems, was utilized. Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process. Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.64 seconds. This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds. Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.934%, and 98.934%, respectively. Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.   Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search
A Web-Based Information System for Monitoring Food Quality in the Free Nutritious Meal Program (MBG): A Case Study of SDN 173 Neglasari, Bandung PARDEDE, JASMAN; Abdullah, Syadda; Amal M, Ichlasul; Yusuf S, Muhammad; Daffa A R, Muhammad; Ridhwana M, Fadhlan
Integrative Perspectives of Social and Science Journal Vol. 3 No. 03 Maret (2026): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

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

Abstract

The implementation of the Free Nutritious Meal Program (MBG) at SDN 173 Neglasari Bandung faces critical operational challenges regarding food safety assurance, where the monitoring mechanism relying on manual methods and verbal communication has proven ineffective. The limitations of this conventional system led to a high risk of losing student complaint information, a slow response time in reporting spoiled food to catering vendors, and a lack of authentic visual evidence, which weakens the school's position in enforcing provider accountability. To address these vulnerabilities, this study designs a web-based monitoring information system using the Rapid Application Development (RAD) approach, focusing on the transformation of manual recording into a digital format. The implementation results demonstrate a tangible impact experienced by users, where teachers can now report incidents with valid visual evidence in under two minutes, eliminating the issue of lost verbal complaints. Meanwhile, the Principal gains direct access to real-time data for objective vendor evaluation. This transformation has proven to significantly enhance complaint handling responsiveness and student nutrition monitoring transparency compared to the previous manual method.
A BERT-based modular framework for automated English essay scoring via trait analysis Jasman Pardede; Rizka Milandga Milenio; Thalita Zharifa Nathania
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11235

Abstract

Automated essay scoring (AES) systems are commonly implemented using holistic scoring, which limits interpretability and prevents assessment at the writing trait level. As a result, such systems provide limited diagnostic and actionable feedback. To address this limitation, this study proposes a modular trait-based AES framework that separates structure and grammar evaluation while maintaining an integrated scoring mechanism. The proposed framework consists of two modules. The structure module evaluates the ideas, organization, and style traits using a bidirectional encoder representations from transformer-bidirectional long short-term memory (BERT-BiLSTM-Attention) architecture trained on the automated student assessment prize (ASAP) dataset. The grammar module evaluates the Conventions trait by applying a BERT-based grammatical acceptability classifier trained on the Corpus of linguistic acceptability (CoLA) dataset, followed by multinomial logistic regression to convert grammatical patterns into interpretable grammar scores. Experiments were conducted on the ASAP dataset and evaluated using the quadratic weighted Kappa (QWK) metric. The structure module achieved a QWK score of 0.7906 on the test set, while the grammar module obtained a QWK of 0.3923. The integrated holistic score reached a QWK of 0.7847. These results demonstrate that the proposed modular framework improves interpretability and scoring performance, supporting more objective and actionable essay evaluation for formative assessment in English language education.
Improving multilabel classification of hate speech and abusive language in Indonesian using MAML Jasman Pardede; Ghixandra Julyaneu Irawadi; Rizka Milandga Milenio
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27332

Abstract

This study investigates automated multi-label detection of hate speech and abusive language (HSAL) in Indonesian social media, addressing challenges of data imbalance, especially in minority labels. Two training approaches are compared: standard supervised learning and meta-learning using the model-agnostic meta-learning (MAML) algorithm. IndoBERTweet-BiGRU is adopted as the baseline model, while MAML is leveraged to enhance generalization and adaptability with limited training data. Both models are trained on a multilabel dataset with 13 HSAL categories exhibiting highly imbalanced distributions. The best supervised model achieved an F1-Micro of 84.02% and an F1-macro of 77.97%, whereas the best MAML-trained model reached 84.12% and 76.85%, respectively. Although the overall gap is small, MAML demonstrates notable improvements on minority classes such as hate speech (HS) physical, gender, and race, shown through higher F1-score and area under the receiver operating characteristic curve (AUROC) values. These results highlight its strength in low-resource classification settings. This study is limited to Indonesian language and YouTube transcript contexts, and MAML incurs higher training complexity. Cultural and linguistic nuances also present potential bias in real-world use. Despite these constraints, the proposed system offers practical benefits by enabling fine-grained HSAL classification and supporting earlier detection of harmful online content.
Machine Learning Optimization on Social Media Sentiment Data for Data Balance Using N-GRAM Rizka Milandga Milenio; Jasman Pardede; Dea Kurniasih
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 10, No 1 (2026)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v10i1.67-80

Abstract

ABSTRAKKetidakseimbangan kelas merupakan tantangan dalam klasifikasi sentimen pada data media sosial, yang menyebabkan model klasifikasi menjadi bias terhadap kelas mayoritas dan berkinerja buruk pada kelas minoritas. Penelitian ini mengusulkan pendekatan penyeimbangan data berbasis N-Gram untuk mengatasi masalah tersebut dan meningkatkan performa klasifikasi. Tiga model machine learning, yaitu XGBoost, Random Forest, dan Support Vector Machine (SVM), dievaluasi pada dataset yang tidak seimbang maupun seimbang menggunakan akurasi, presisi, recall, dan F1-score sebagai metrik evaluasi. Hasil eksperimen menunjukkan bahwa penyeimbangan data meningkatkan performa semua model tanpa menurunkan kemampuan generalisasi. SVM mencapai performa terbaik pada dataset seimbang dengan akurasi 0,86, presisi 0,87, recall 0,86, dan F1-score 0,86. XGBoost dan Random Forest juga menunjukkan peningkatan performa yang signifikan setelah penyeimbangan, menunjukkan kemampuan yang lebih baik dalam mendeteksi kelas minoritas. Secara keseluruhan, temuan ini menegaskan bahwa pendekatan penyeimbangan data berbasis N-Gram yang diusulkan efektif dalam mengurangi ketidakseimbangan kelas dan meningkatkan ketahanan serta keandalan model klasifikasi sentimen.Kata kunci: klasifikasi sentimen, ketidakseimbangan kelas, n-gram, media sosialABSTRACTClass imbalance is a challenge in sentiment classification of social media data, often causing classification models to be biased toward majority classes and perform poorly on minority classes. This study proposes an N-Gram-based data balancing approach to address this issue and improve classification performance. Three machine learning models, namely XGBoost, Random Forest, and Support Vector Machine (SVM), were evaluated on both imbalanced and balanced datasets using accuracy, precision, recall, and F1-score as evaluation metrics. The experimental results demonstrate that data balancing consistently enhances performance across all models without degrading generalization capability. Among the evaluated methods, SVM achieves the best performance on the balanced dataset, reaching an accuracy of 0.86, precision of 0.87, recall of 0.86, and F1-score of 0.86. XGBoost and Random Forest also show substantial performance improvements after balancing, indicating improved detection of minority sentiment classes. Overall, the findings confirm that the proposed N-Gram-based data balancing approach effectively mitigates class imbalance and improves the robustness and reliability of sentiment classification models.Keywords: Sentiment Classification, Class Imbalance, N-Gram, Social Media
Analisis Sentimen Penanganan Covid-19 Menggunakan Metode Long Short-Term Memory Pada Media Sosial Twitter Pakpahan, Ivan; Jasman Pardede
Jurnal Publikasi Teknik Informatika Vol. 2 No. 1 (2023): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v1i1.767

Abstract

Social media can be used to convey people's aspirations for government policies. Several government policies regarding the regulation of the handling of Covid-19 often elicit responses and criticism from the public, especially on Twitter social media. The aspirations conveyed can contain positive or negative responses. To find out the representation of public sentiment based on these responses, it is necessary to do a sentiment analysis technique. The Long Short-Term Memory (LSTM) method is a deep learning method that can be used in sentiment analysis. LSTM is used because it has the advantage of being able to store large amounts of information in memory cells. Before carrying out classification modeling, the dataset must go through the process of case folding, punctuation removal, normalization, and stopword removal. This aims to ease the training process by eliminating characters or words that are not needed. Next, the word is vectorized using FastText, the goal is to change the string data type to an array vector, so that the word can be processed in the LSTM. The final performance of the model is measured based on the value of precision, recall, accuracy, and f Measure. Based on testing the dropout layer parameters on the hidden layer against 10 fold cross validation, the average accuracy of model testing resulting from all k folds is 72.4%. with the maximum model performance achieved at k fold = 9, when using a dropout layer of 0.4, the values ​​achieved are precision, recall, accuracy, and f measure respectively: 76.74%, 80.49%, 78.31%, 78.57%.
Co-Authors Abdullah, Syadda adlan chosyiyar rochman Afis Siswantini Ahmada, Marsa Akbar, Saiful Al Fauzan, Farel Anugrah Alfyansyah, Rangga Alpriatna Malik, Yuzzar Amal M, Ichlasul Amal, Irfan Ardi Fajar Maulana Asep Nana Hermana B, Mira Musrini Benhard Sitohang Bernovaldy, Muhammad Akbar Caecilia Sri Wahyuning Chazar, Chalifa Daffa A R, Muhammad Darmawan, Dicky Dea Kurniasih Dewi, Renita Dika Prasetia Pamungkas Dina Budhi Utami Dina Budhi Utami, Dina Budhi Dwi Adi Lenggana Putra Dwianto, Rio Ekklesia, Maleakhi Fadhillah Prasetyo, Rachma Fandi Fifi Herni Mustofa Galih Swarghani Ghixandra Julyaneu Irawadi HENDRI HARDIANSAH Hermana, Asep Nana Hilwa Athifah KLEB, SYAFIQ SALIM Luqman Yudhianto Luthfi Athallah, Rifqi Marisa Premitasari, Marisa Miftahuddin, Yusup Milenio, Rizka Milandga Mira Musrini B Muhamad Rifki Pratama Muhammad Akbar Bernovaldy Muhammad Azhari MUHAMMAD FAUZAN RASPATI Muhammad Mulyawan MUHAMMAD RIFALDI BADU Muhammad Zaki Mahran Mufid Noval Rizky Nugraha Nurhasanah, Youllia Indrawaty Nurrohmah, Desita Pakpahan, Ivan Perdinan, Rivan Dio Prameswari, Anindya Putra Riyanto, Aquila Putra, Dwi Adi Lenggana Raka Gemi Ibrahim Raka Satria, Marius Rawosi, Muhammad Fadlansyah Zikri Akhiruddin RAYYAN RAYYAN Renita Dewi Ridhwana M, Fadhlan Rijal, Khairul Rizka Milandga Milenio Rizka Milandga Milenio Rizka Milandga Milenio rochman, adlan chosyiyar Sandia, Muhammad Sakha Satria Darmawan, Kevin Setyaningrum, Anisa Putri Siswantini, Afis Supriyandari, Listy Nuri Swarghani, Galih Thalita Zharifa Nathania Ungkawa, U. Uung Ungkawa Yudhianto, Luqman Yudistira, Agil Yunastrian, Kurniandha Yusuf S, Muhammad