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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Emerging Science Journal Syntax Literate: Jurnal Ilmiah Indonesia Gema Wiralodra Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Engineering, Mathematics and Computer Science Journal (EMACS) Parameter: Journal of Statistics Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Indonesia Sosial Teknologi Jurnal Penelitian Pendidikan Indonesia Teknologi : Jurnal Ilmiah Sistem Informasi
Abba Suganda Girsang, Abba Suganda
Bina Nusantara University, Jakarta, Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Waste Pollution Classification in Indonesian Language using DistilBERT Nursandi, Bambang; Girsang, Abba Suganda
Gema Wiralodra Vol. 15 No. 1 (2024): Gema Wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v15i1.645

Abstract

In Indonesia, waste pollution poses pressing environmental and health challenges, making accurate classification vital for targeted mitigation efforts. DistilBERT emerges as a streamlined counterpart to the acclaimed BERT architecture, designed to mirror BERT's advanced linguistic comprehension but with reduced computational demands. By leveraging the essence of transfer learning, DistilBERT benefits from a wealth of information obtained from extensive textual datasets, positioning it as an ideal choice for scenarios marked by limited data accessibility. In our research, we adopted DistilBERT to address the niche challenge of classifying waste types using a constrained dataset derived from Twitter conversations in Indonesian language—a medium notorious for its concise and often ambiguous content. Notwithstanding the dataset's restricted scope and the noise inherent to Twitter, DistilBERT demonstrated an astounding efficacy, registering a precision rate of 98%. This outcome accentuates DistilBERT's capability to navigate and discern complex textual nuances even in data-restricted environments and further highlights the significance of transfer learning in contemporary natural language processing challenges, especially in contexts as critical as Indonesia's waste management efforts
Performance Analysis of RabbitMQ and Nats Streaming for Communication in Microservice Agung Nur Aprianto; Abba Suganda Girsang; Yulianto Nugroho; Widjaya Kumala Putra
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 14 No 1 (2024): January
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v14i1.4498

Abstract

In this research, performance testing is performed between the two message brokers, which commonly used in the enterprise, namely RabbitMQ and Nats Streaming. REST is a method that implements the HTTP protocol requests to access and use data. REST is one of the synchronous style methods in microservice, the other style is asynchronous that can be implemented through message broker. REST can be used for communication between services in microservice, since it is using HTTP protocol, the performance will degrade when the amount of request is abundant and less reliable due to its synchronous communication. By using a message broker as the medium of communication between services in microservice, each connected service will not rely on each other and will make the message delivery more guaranteed. By reason, this research will implement a message broker for inter process communication (IPC) in microservice. Today there are many message brokers developed by various companies or communities. In this research, we do experiments with both message brokers. The three aspects will be tested, they are throughput, latency by number of messages and latency by message size. The performance will be evaluated the architecture model that act as producer and consumer. The model is one producer and consumer service. The service will be deployed on docker container
Utilization of geocoding for mapping infrastructure impacts and mobility due to floods in indonesia based on twitter analytics Taufiq, Muhammad Imam; Girsang, Abba Suganda
JPPI (Jurnal Penelitian Pendidikan Indonesia) Vol. 10 No. 3 (2024): JPPI (Jurnal Penelitian Pendidikan Indonesia)
Publisher : Indonesian Institute for Counseling, Education and Theraphy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/020244467

Abstract

Flooding, a frequent natural disaster in Indonesia, is caused by several factors such as high-intensity rainfall, climate change, inadequate drainage and urban infrastructure challenges, impacting communities, infrastructure and economic activities. The lack of accurate and centralized data hinders government efforts to identify affected areas and respond effectively. Named Entity Recognition (NER), a machine learning-based information extraction tool, offers the potential for geocoding flood-related data from social media, such as Twitter. The purpose of this research is to develop a Named Entity Recognition (NER)-based model to extract location information from Twitter and visualize flood impacts through geocoding. The method used is a combination of Qualitative Analysis with Machine Learning and Geospatial Analysis to assess flooding impacts using Twitter data. Initially, a qualitative analysis of tweets extracts flood-related keywords to identify patterns. Then, Named Entity Recognition (NER) identifies locations, which are converted into geographic coordinates through geocoding for map visualization. The results show that location extraction from flood-related tweets using the Named Entity Recognition (NER) model and geocoding produces very useful and accurate data. About 50% of the flood-related tweets included location tokens, which shows the importance of geographic information in understanding the impact of disasters. The location extraction process using the NER model proved to be effective, although there were some discrepancies between the extracted location tokens and the actual geographic data, especially at the more detailed location level. However, the evaluation results show that 99.5% of the extracted locations correspond to valid locations, especially in the Indonesian region. This shows that the use of the NER model and geocoding is highly effective in analyzing flood impacts and provides significant benefits in disaster management and geospatial analysis based on social media data.
Improved Watermarking Performance in Color Images through a hybrid of DWT-DCT Integration for Copyright Protection Pandusarani, Gagas; Girsang, Abba Suganda
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i4.15164

Abstract

The era of big data has changed the view of data, data can now be considered as data assets. The definition of data assets is data that has rights (exploration rights, use rights, and ownership rights). Some experts believe that data assets have value at their core, the value contained can be in the form of information owned by data assets. Sharing digital data is growing every day as more people access the Internet quickly. Unauthorized individuals can easily access multimedia, such as text, images, video and audio. This research focuses on digital image watermarking to ensure security and copyright protection. This scheme uses a watermarking technique based on the hybrid of the two transformation domains (frequency domain), Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). The digital image is processed into several parts by the DWT, then the watermark is embedded into one of the parts in the frequency domain using the DCT transformation, then the parts are combined again in the DWT. The experimental results show that the watermarked images achieve the highest PSNR result was 45.2719 dB, and the lowest was 43.3194 dB. an average PSNR value of 44.1254 dB, by testing ten color image datasets sourced from the SIPI-USC image database.
Classification of Indonesia False News Detection Using Bertopic and Indobert Prisscilya, Veren; Girsang, Abba Suganda
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 8 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i8.1310

Abstract

In the current global era, the development of technology and information is very rapid, so it is very easy to get information/news from the internet. Because of the ease of getting this information, there is a lot of circulating fake news (hoaxes), the news is not filtered so anyone can spread news that is not clear in content. This can lower a person's credibility in the professional world, cause division, threaten physical and mental health, and can also result in material losses. Based on this, to stop the spread of hoaxes is to detect them as early as possible and block them. This detection can use deep learning methods which are also one of the architectures of transformers, namely a combination of BERTopic which is used to find important words from the news narrative, then the words are combined into the narrative and classified using Indo Bidirectional Encoder Representation from Transformer (IndoBERT). For experiments, the author uses a dataset taken from the kaggle.com website entitled Indonesia False News (HOAX) dataset. This study uses a learning rate of 1e-5, a batch size of 16 and using 5 epochs so that the f1-Score results are 92% for validation data and 91% for testing data.
Optimizing diplomatic indexing: full-parameter vs low-rank adaptation for multi-label classification of diplomatic cables Nurlaila, Dela; Girsang, Abba Suganda
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p274-282

Abstract

Accurate classification of diplomatic cables is crucial for Mission’s evaluation and policy formulation. However, these documents often cover multiple topics, hence a multi-label classification approach is necessary. This research explores the application of pre-trained language models (CahyaBERT, IndoBERT, and MBERT) for multi-label classification of diplomatic cable executive summaries, which align with the diplomatic representation index. The study compares full-parameter fine-tuning and low-rank adaptation (LoRA) techniques using cables from 2022-2023. Results demonstrate that Indonesian-specific models, particularly the IndoBERT, outperform multilingual models in classification accuracy. While LoRA showed slightly lower performance than full fine-tuning, it significantly reduced GPU memory usage by 48% and training time by 69.7%. These findings highlight LoRA’s potential for resource-constrained diplomatic institutions, advancing natural language processing in diplomacy and offering pathways for efficient, real-time multi-label classification to enhance diplomatic mission evaluation.
Co-Authors Achmad Abimanyu Adi Setya Agung Nur Aprianto Agus Susanto Ali, Ichsan Andrew, Peter Andri Wijaya Anggara, Nelsen Anita Sindar Ros Maryana Sinaga Ardhito, Danar Aurelia, Miracle Budi Juarto Burhanudin Burhanudin Chu-Sing Yang Chun-Wei Tsai Danar Ardhito Dela Nurlaila Dewangga, Firdaus Angga Febryan Grady Gagas Pandusarani, Gagas I Dewa Bagus Gde Khrisna Jayanta Nugraha Imran, Bahtiar Indrabudhi Lokaadinugroho Jimmy Jimmy Joel Rizky Wahidiyat Ko-Wei Huang Lawijaya, Viriyaputra Lawysen, Lawysen Monica, Sheila Nasari, Maulin Naufal, Muhammad Alwan Novika, Andien Dwi Nursandi, Bambang Priccilia, Shania Prisscilya, Veren Putra, Anugrah Dwiatmaja Ricky Eka Putra Rifqi Ramadhani Almassar Stefani Agusta Suharjito Suharjito, Suharjito Syamsul Arifin Taufiq, Muhammad Imam Tedjasulaksana, Jeffrey Junior Tjeng Wawan Cenggoro Widjaya Kumala Putra Yefferson, Danny Yongky Yulianto Nugroho