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All Journal Dinamik Semantik Techno.Com: Jurnal Teknologi Informasi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Computer Science and Technology (JACOST) Advance Sustainable Science, Engineering and Technology (ASSET) Journal La Multiapp Community: Jurnal Pengabdian Pada Masyarakat
Ardytha Luthfiarta
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences Other

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Single-Image Face Recognition For Student Identification Using Facenet512 And Yolov8 In Academic Environtment With Limited Dataset Imam Muttaqin, Almas Najiib; Luthfiarta, Ardytha; Nugraha, Adhitya; Salsabila, Pramesya Mutia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.3908

Abstract

Face recognition has become one of the most significant research areas in image processing and computer vision, mainly due to its wide applications in security, identity verification, and human and machine interaction. In this study, FaceNet512 and YOLOv8 models are used to overcome the challenges in face recognition with a limited dataset, which is only one formal photo per individual. The application of image augmentation to the model achieved 90% accuracy and ROC curve of 0.82, while the model without augmentation achieved 89% accuracy and ROC curve of 0.79. FaceNet512 showed superiority in producing more accurate and detailed facial representations compared to other models, such as ArcFace and FaceNet, especially in handling minimal facial variations. Meanwhile, YOLOv8 provides efficient face detection across various lighting conditions and viewing angles. The main challenge in this research is the low quality of the original image, which can reduce the accuracy of face recognition. These results show the great potential of using deep learning-based face recognition systems in the real world, especially for automatic attendance applications in academic environments.
Comparison of IndoNanoT5 and IndoGPT for Advancing Indonesian Text Formalization in Low-Resource Settings Firdausillah, Fahri; Luthfiarta, Ardytha; Nugraha, Adhitya; Dewi, Ika Novita; Hafiizhudin, Lutfi Azis; Mumtaz, Najma Amira; Syarifah, Ulima Muna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4935

Abstract

The rapid growth of digital communication in Indonesia has led to a distinct informal linguistic style that poses significant challenges for Natural Language Processing (NLP) systems trained on formal text. This discrepancy often degrades the performance of downstream tasks like machine translation and sentiment analysis. This study aims to provide the first systematic comparison of IndoNanoT5 (encoder-decoder) and IndoGPT (decoder-only) architectures for Indonesian informal-to-formal text style transfer. We conduct comprehensive experiments using the STIF-INDONESIA dataset through rigorous hyperparameter optimization, multiple evaluation metrics, and statistical significance testing. The results demonstrate clear superiority of the encoder-decoder architecture, with IndoNanoT5-base achieving a peak BLEU score of 55.99, significantly outperforming IndoGPT's highest score of 51.13 by 4.86 points—a statistically significant improvement (p<0.001) with large effect size (Cohen's d = 0.847). This establishes new performance benchmarks with 28.49 BLEU points improvement over previous methods, representing a 103.6% relative gain. Architectural analysis reveals that bidirectional context processing, explicit input-output separation, and cross-attention mechanisms provide critical advantages for handling Indonesian morphological complexity. Computational efficiency analysis shows important trade-offs between inference speed and output quality. This research advances Indonesian text normalization capabilities and provides empirical evidence for architectural selection in sequence-to-sequence tasks for morphologically rich, low-resource languages.
Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model Cahya, Leno Dwi; Luthfiarta, Ardytha; Krisna, Julius Immanuel Theo; Winarno, Sri; Nugraha, Adhitya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.290-298

Abstract

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%.
Sistem Rekomendasi Pembelian Smartphone berbasis Algoritma K-Means dan Singular Value Decomposition Zuhdiansyah, Ivan; Luthfiarta, Ardytha
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 1 (2024): April 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i1.2024.45-53

Abstract

Perkembangan teknologi informasi yang pesat, memberi dampak pada ketesedian informasi yang berlimpah. Hal ini menjadikan suatu masalah yang disebut kelebihan informasi, menyebabkan pengguna internet sulit memahami dan membuat keptusan. E-commerce merupakan salah satu yang terdampak dari kelebihan informasi, dengan banyaknya produk dan pengguna baik dari penjual maupun pembeli yang ada. Sistem rekomendasi adalah bagian penting dari e-commerce yang menjadi salah satu cara menangani kelebihan informasi, dengan memberikan rekomendasi produk kepada pembeli agar membantu menentukan pilihan. Dalam sistem rekomendasi memiliki permasalahan scalability, dimana banyaknya produk yang tersedia membuatnya menjadi tidak efektif dan efisien dalam memberikan rekomendasi kepada pembeli. Maka, penelitian ini mengusulkan metode sistem rekomendasi yang dikombinasikan teknik clustering. Menggunakan algoritma K-Means untuk mengelompokkan produk, kemudian algoritma Singular Value Decomposition (SVD) untuk membuat rekomendasi di dalam cluster yang terbentuk. Hasil keluaran model yaitu, rekomendasi produk dan prediksi rating yang diberikan pembeli dari produk yang direkomendasikan. Evaluasi model mendapatkan nilai dbi sebesar 0,703 untuk clustering, nilai rata-rata terbaik MAE 0.8150 dan RMSE 1.1781 untuk rekomendasi yang dihasilkan. Kesimpulan yang didapat bahwa metode ini dapat menangani masalah scalability dan memberikan rekomendasi yang akurat dengan nilai evaluasi yang lebih baik dibandingkan penelitian sebelumnya.
Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models Krisna, Julius Immanuel Theo; Luthfiarta, Ardytha; Cahya, Leno Dwi; Winarno, Sri; Nugraha, Adhitya
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18228

Abstract

Emotions are one of the reactions of human when they receive physical or verbal action. Every human action is based on emotion. Every opinion expressed in the comments column also contains the author's emotions. This research aims to classify five emotions, Marah, Takut, Senang, Cinta, and Sedih and evaluate the performance of three commonly used optimizer, Adam, RMSProp, and Nadam. The processed data used IndoBERT model for Indonesian text classification. The research purpose to search the best optimizer for text classification. The result shows classification used Adam Optimizer 90,21%, RMSProp Optimizer 82.11, and Nadam Optimizer 88.61%. The Adam optimizer applied to the IndoBERT model yielded the best results. This shows a significant improvement from previous studies, which had emotion classification.
The Effect of LAB Color Space with NASNetMobile Fine-tuning on Model Performance for Crowd Detection Rafid, Muhammad; Luthfiarta, Ardytha; Naufal, Muhammad; Al Fahreza, Muhammad Daffa; Indrawan, Michael
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17821

Abstract

In the COVID-19 pandemic, computer vision plays a crucial role in crowd detection, supporting crowd restriction policies to mitigate virus spread. This research focuses on analyzing the impact of using the RGB LAB color space on the performance of NASNetMobile for crowd detection. The fine-tuning process, involving freezing layers in various NASNetMobile base model variations, is considered. Results reveal that the model with LAB color space outperforms model with RGB color space, with an average accuracy of 94.68% compared to 94.15%. From all the test iterations, it was found that the highest performance for the NASNetMobile model occurred when freezing 10% of the layers from the back for both model LAB and RGB color spaces, with the LAB color space achieving an accuracy of 95.4% and the RGB color space achieving an accuracy of 95.1%.
Optimasi Logistic Regression untuk Deteksi Serangan DoS pada Keamanan IoT Primadya, Nauval Dwi; Nugraha, Adhitya; Luthfiarta, Ardytha; Fahrezi, Sahrul Yudha
Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.1065

Abstract

Keamanan perangkat Internet of Things (IoT) merupakan prioritas utama karena potensi risiko kerusakan perangkat dan kebocoran data yang dapat berdampak serius. Perangkat IoT telah membawa manfaat signifikan ke berbagai sektor, seperti kesehatan, transportasi, dan industri, namun tingkat serangan terhadapnya terus meningkat. Dalam mengatasi tantangan ini, pendekatan machine learning digunakan dengan memanfaatkan dataset CIC IOT ATTACKS 2023 dari University of New Brunswick. Untuk menghasilkan data yang berkualitas, dilakukan random undersampling untuk mengatasi ketidakseimbangan data, dan seleksi fitur menggunakan Recursive Feature Elimination untuk mendapatkan fitur terbaik. Pemilihan Logistic Regression sebagai algoritma pemodelan dipilih dengan pertimbangan yang matang. Logistic Regression dipilih karena kemampuannya memberikan interpretasi yang jelas terhadap kontribusi relatif setiap fitur terhadap prediksi keamanan perangkat IoT. Selain itu, model ini efisien secara komputasional, mengatasi ketidakseimbangan data, dan tahan terhadap overfitting, yang semuanya merupakan faktor krusial dalam konteks keamanan IoT. Hasil penelitian menunjukkan bahwa penggunaan Logistic Regression bersamaan dengan seleksi fitur memberikan tingkat akurasi tertinggi mencapai 97%, dengan waktu pemrosesan yang efisien sekitar 11 detik. Dari hasil ini, dapat disimpulkan bahwa kombinasi teknik random undersampling dan seleksi fitur menggunakan Recursive Feature Elimination secara positif memengaruhi akurasi pada model Logistic Regression, menjadikannya pilihan yang sesuai untuk meningkatkan keamanan perangkat IoT.
OPTIMIZING BUTTERFLY CLASSIFICATION THROUGH TRANSFER LEARNING: FINE-TUNING APPROACH WITH NASNETMOBILE AND MOBILENETV2 Putri, Ni Kadek Devi Adnyaswari; Luthfiarta, Ardytha; Putra, Permana Langgeng Wicaksono Ellwid
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1583

Abstract

Butterflies play a significant role in ecosystems, especially as indicators of the state of biological balance. Each butterfly species is distinctly different, although some also show differences with very subtle traits. Etymologists recognize butterfly species through manual taxonomy and image analysis, which is time-consuming and costly. Previous research has tried to use computer vision technology, but it has shortcomings because it uses a small distribution of data, resulting in a lack of programs for recognizing various other types of butterflies. Therefore, this research is made to apply computer vision technology with the application of transfer learning, which can improve pattern recognition on image data without the need to start the training process from scratch. Transfer learning has a main method, which is fine-tuning. Fine-tuning is the process of matching parameter values that match the architecture and freezing certain layers of the architecture. The use of this fine-tuning process causes a significant increase in accuracy. The difference in accuracy results can be seen before and after using the fine-tuning process. Thus, this research focuses on using two Convolutional Neural Network architectures, namely MobileNetV2 and NASNetMobile. Both architectures have satisfactory accuracy in classifying 75 butterfly species by applying the transfer learning method. The results achieved on both architectures using fine-tuning can produce an accuracy of 86% for MobileNetV2, while NASNetMobile has a slight difference in accuracy of 85%.
Accelerating Classification For Iot Attack Detection Using Decision Tree Model With Gini Impurity Tree-Based Feature Selection Technique Dzaki, Muhammad Hafizh; Nugraha, Adhitya; Luthfiarta, Ardytha; Riyanto, Azizu Ahmad Rozaki; Novandian, Yohanes Deny
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.3817

Abstract

The Internet of Things (IoT) continues to expand rapidly, with the number of connected devices expected to reach billions in the near future. However, it makes IoT devices prime target for cyber-attack. Therefore, an effective Intrusion Detection System (IDS) is required to detect these attacks swiftly and accurately. This study aims to build a machine learning-based IDS to effectively detect attack on IoT network using the CIC IoT 2023 dataset. The dataset contains over 46 million data rows with 48 features, covering 33 attack types and 1 benign class. To address the dataset's complexity and enhance processing efficiency, feature selection technique was applied. Six feature selection techniques from three categories – Filter-based, Wrapper-based, and Hybrid methods – were evaluated to produce the best feature subset. Each subset was tested using a Decision Tree algorithm. Then, the model performance calculated based on accuracy, computational time, as well as macro-precision, -recall, and -F1-score. The results demonstrate that the three best feature selection from each category – Mutual Information, Genetic Algorithm, and Gini Impurity Tree-based – improved training time by average different 55 seconds from 148 seconds, which speed up by 63.06% without sacrificing accuracy. The Gini Impurity Tree-based algorithm proved to be the most efficient, producing the smallest feature subset, which is 10 features, faster processing times, which is 40 seconds, and shallower tree’s depth, which is 64 level from 73 level. In conclusion, feature selection not only enhances computational efficiency but also simplifies tree’s shape without sacrificing the accuracy of detection.
Performance Comparison of IoT Classification Models using Ensemble Stacking and Feature Importance setiawan, nabila putri; Nugraha, Adhitya; Luthfiarta, Ardytha; Mulyana, Yudha
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4673

Abstract

Internet of Things (IoT) security is becoming a top priority as the number of connected devices increases online. This research utilizes the CIC IoT ATTACK 2023 dataset from the University of Brunswick, which consists of 46 million data on various types of attacks on IoT devices, such as DDoS, DoS, Brute Force, Spoofing, and Mirai attacks. To address the imbalance in the dataset, a random undersampling technique is applied to ensure the machine learning model is not biased towards the majority class. The ensemble learning approach was chosen due to its ability to combine the strengths of multiple algorithms, thus improving accuracy and stability in detecting complex IoT attacks. The algorithms used include gradient boosting, bagging, voting, and stacking. In particular, the stacking model, which combines the bagging classifier and gradient boosting, achieved the highest accuracy of 93%. Although the accuracy of the stacking model decreased to 92.4% after feature selection, the precision, recall, and F1-score remained high at 92.0. In addition, the computation time was also reduced from 2111.69 seconds to 1208.27 seconds. These findings indicate that ensemble learning approaches and feature selection techniques have great potential in improving IoT security, providing more reliable and efficient threat detection solutions.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adhitya Nugraha Affandy Affandy Al Fahreza, Muhammad Daffa Althoff, Mohammad Noval Aris Febriyanto Aryanti, Firda Ayu Dwi Astuti, Yani Parti Bagus Dwi Satya, Mohammad Wahyu Basiron, Halizah Cahya, Leno Dwi Catur Supriyanto Catur Supriyanto Defri Kurniawan Dhita Aulia Octaviani Dzaki, Azmi Abiyyu Dzaki, Muhammad Hafizh Edi Faisal Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fahreza, Muhammad Daffa Al Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Fairuz Dyah Esabella Farandi, Muhammad Naufal Erza Farsya, Nabila Zibriza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Hafiizhudin, Lutfi Azis Haresta, Alif Agsakli Harun Al Azies Hasan Shobri Heru Lestiawan Huda, Alam Muhammad Ika Novita Dewi Imam Muttaqin, Almas Najiib Indrawan, Michael Irham Ferdiansyah Katili Ivan Zuhdiansyah Julius Immanuel Theo Krisna Junta Zeniarja Krisna, Julius Immanuel Theo L. Budi Handoko Leno Dwi Cahya Maharani, Zahra Nabila Mahardika, Pramesthi Qisthia Hanum Md. Mahadi Hasan, Md. Mahadi Michael Indrawan Muhammad Daffa Al Fahreza Muhammad Jamhari Muhammad Naufal, Muhammad Muhammad Rafid Mulyana, Yudha Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nauval Dwi Primadya Nisa, Laila Rahmatin Novandian, Yohanes Deny Octaviani, Dhita Aulia Primadya, Nauval Dwi Putra, Permana Langgeng Wicaksono Ellwid Putri, Ni Kadek Devi Adnyaswari Rafid, Muhammad Ramadhan Rakhmat Sani Rismiyati Rismiyati Riyanto, Azizu Ahmad Rozaki Sahrul Yudha Fahrezi Salsabila, Pramesya Mutia Satya, Mohammad Wahyu Bagus Dwi Setiawan, Dicky setiawan, nabila putri Soeroso, Dennis Adiwinata Irwan Sri Winarno Sri Winarno Suprayogi Suprayogi Suryaningtyas Rahayu Syarifah, Ulima Muna Utomo, Danang Wahyu Wibowo Wicaksono Wibowo Wicaksono Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine Zuhdiansyah, Ivan