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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering Jurnal INFOTEL
Ema Rachmawati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Vegetation classification algorithm using convolutional neural network ResNet50 for vegetation mapping in Bandung district area Rina Pudji Astuti; Ema Rachmawati; Edwar Edwar; Simon Siregar; Indra Lukmana Sardi; Arfianto Fahmi; Yayan Agustian; Agus Cahya Ananda Yoga Putra; Faishal Daffa
JURNAL INFOTEL Vol 14 No 2 (2022): May 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i2.756

Abstract

Bandung District is one of crop provider for West Java Province. About 31.158,22 ha is used for crop. However, some of them are not maintained well due to lack of vegetation map information. Local authority has tried to map the vegetation in their area by using free license satellite images, and aerial images from Unmanned Aerial Vehicle (UAV). Despite both images being able to provide large plantation area images, both are unable to classify the vegetation type in those images. Telkom University with Bandung Agriculture Regional Office (Dinas Pertanian Kabupaten Bandung) has conducted joint research to develop algorithm based on 50-layer residual neural network (ResNet50) to classify the vegetation type. The input is of this algorithm is primarily aerial images are captured from different type, height, and position of crops. Seven different ResNet50 configurations have been set and simulated to classify the crop images. The result is the configuration with resized images, employing triangular policy of cyclic learning rate with rate 1.10−7 – 1.10−4 comes out as the best setup with more than 95% accuracy and relatively low loss.
Klasifikasi Gender Berdasarkan Citra Wajah Menggunakan Vision Transformer Ganjar Gingin Tahyudin; Ema Rachmawati; Mahmud Dwi Sulistiyo
eProceedings of Engineering Vol 10, No 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Gender seseorang dapat dilihat salah satunya secara visual berdasarkan citra wajah manusia. Selain itu, dengan kemajuan teknologi saat ini, komputer juga dapat melakukan klasifikasi gender berdasarkan data yang dilatih. Proses klasifikasi gender menggunakan komputer dapat diaplikasikan terhadap berbagai sektor seperti industri atau pemerintahan. Pada penelitian sebelumnya, terdapat berbagai metode konvensional yang digunakan untuk melakukan klasifikasi citra, khusus klasifikasi gender berdasarkan citra wajah, namun sebagian besar tidak melakukan Cross-Dataset Evaluation untuk melakukan uji performa terhadap model yang dihasilkan. Tugas akhir ini akan membahas bagaimana melakukan klasifikasi gender berdasarkan citra wajah menggunakan metode Vision Transformer menggunakan dataset AFAD sebagai dataset training dan melakukan Cross-Dataset Evaluation terhadap model yang dihasilkan menggunakan dataset UTKFace. Model yang dibangun berhasilkan mendapatkan akurasi validasi sebesar 0,9676 dan akurasi testing sebesar 0,9661 pada pengujian training atau Same-Dataset serta mendapatkan akurasi 0,8174, Precision 0,8188, Recall 0,8189, dan F1 Score sebesar 0,8189 pada pengujian Cross-Dataset Evaluation.Kata kunci- transformer, vision transformer, gender classification, image processing, computer vision.
Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns Grandhys Setyo Utomo; Ema Rachmawati; Febryanti Sthevanie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4798

Abstract

The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a precision value of 94%.
Pendampingan Implementasi Materi Berpikir Komputasional di Mata Pelajaran Informatika Pada Kurikulum Merdeka Tingkat SMP di Wilayah Kabupaten Bandung Ema Rachmawati; Fazmah Arif Yulianto; Lidya Ningsih
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.4899

Abstract

In the era of globalization and digitalization, mastery of Computational Thinking (CT) has become crucial in computer science education, especially at the junior high school level. This mentoring program is designed to bridge the gap between the evolving curriculum demands and teachers' readiness to implement them, with a specific focus on CT content in the Informatics subject for junior high schools. The activities involve teaching technical aspects of informatics and providing a deep understanding of computational thinking as an essential tool for developing students' analytical, creative, and problem-solving abilities. By enhancing the capabilities of junior high school informatics teachers, this program aims to improve the quality of education and equip teachers with relevant skills to teach CT material to students, thereby contributing to community empowerment through the overall enhancement of digital literacy.
Leveraging Sarcasm Text Detection in Meme Polarity Classification Zeva Patu Assyadid; Ema Rachmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7516

Abstract

Memes are a popular internet format and spread quickly across many social media platforms. People can express their ideas, criticisms, interests, or aversions through memes. But in some cases, other people may interpret memes differently and feel bad about it. This variation in meme interpretation is a challenge in sentiment analysis, as a meme can be judged negative or positive by different individuals. Therefore, there is a need for an automated system that can consistently predict the sentiment polarity of memes. A meme is multimodal content that could consist of visual and textual components, which is suitable for a sentiment polarity analysis study. To model a system that effectively leverages multimodal features, the model needs to understand the meme features. This study proposes a joint deep learning model—BERT and Densenet121—that concatenates text, image, and cluster features based on the extracted face encodings. To assess the context of the texts better, BERT was trained with a sarcasm dataset. The widely used ‘SemEval 2020 Task 8: Memotion Analysis dataset’ was used in this study due to its comprehensive annotation of meme-based sentiment and sarcasm, which aligns with this study’s approach. The result demonstrates that the model achieved 0.3738 Accuracy (+2.52%) and 0.3735 Weighted F1 (+1.04%), while maintaining competitive Macro F1 (0.3047). This result shows effective sarcasm adaptation on imbalanced datasets, with improved ability to detect positive and neutral sentiments and reduce sarcastic false negatives compared to the base model. This highlights the effectiveness of integrating sarcasm detection into the model framework for robust sentiment classification in memes.
Advancing Vehicle Logo Detection with DETR to Handle Small Logos and Low-Quality Images Rifky Fahrizal Ubaidillah; Mahmud Dwi Sulistiyo; Gamma Kosala; Ema Rachmawati; Deny Haryadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6236

Abstract

Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed for vehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used.
Co-Authors Abdul Khaliq Al Bari Achmad Hussein Sundawa Kartamihardja Ade Romadhony Agung Toto Wibowo Agus Cahya Ananda Yoga Putra Amin Nur Arfianto Fahmi Arifudin Achmad Bambang Pudjoatmodjo Bedy Purnama Deny Haryadi Dodi Wisaksono Sudiharto Edwar Esa Prakasa Faishal Daffa Fat'hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Febryanti Sthevanie Gamma Kosala Ganjar Gingin Tahyudin Grandhys Setyo Utomo Haddad Alwi Yafie Hamad Fauzi Jessar Indra Lukmana Sardi Iping Supriana Iping Supriana Suwardi Jondri Jondri Kurniawan Nur Ramadhani Lidya Ningsih Mahmud Dwi Sulistiyo Masayu Leylia Khodra Maula Ilma Ahgnia Dwi Anjani Muhammad Arzaki Muhammad Rayhan Subhi Naufal Harsa Pratama Prawita, Fat’hah Noor Putu Harry Gunawan Rifky Fahrizal Ubaidillah Rimba Whidiana Ciptasari Rina Pudji Astuti Rini Shintawati Selly Meliana Simon Siregar Supriana, Iping Yayan Agustian Zeva Patu Assyadid