Posts by Collection

Realtime BISINDO Recognition

Published: September 18, 2025

Real-time Indonesian Sign Language (BISINDO) recognition powered by YOLOv11 and deployed with Streamlit. This app runs directly in the browser using WebRTC webcam integration.

Face Recognition Using Information Retrieval

Published: September 18, 2025

A facial recognition system using deep features and FAISS for fast similarity search. Powered by VGG16 embeddings and FAISS indexing for efficient face retrieval.

Sentiment Analysis on Indonesia Presidential Inauguration

Published: September 18, 2025

A sentiment analysis project on Indonesian social media during the presidential inauguration period using IndoBERT and tweet scraping.

Kuala Inspirasi Website

Published: September 18, 2025

A modern, responsive website for Kuala Inspirasi — a non-profit community focused on social and educational initiatives based in Kuala Tungkal, Jambi.

Peringkas Teks Otomatis Bahasa Indonesia secara Abstraktif Menggunakan Metode Long Short-Term Memory

Published in eProceedings of Engineering, 2021

Salah satu topik dalam bidang Natural Language Processing (NLP) yang cukup menantang adalah peringkas teks otomatis. Dalam praktiknya peringkas teks otomatis terbagi menjadi dua pendekatan, yaitu ekstraktif dan abstraktif. Pendekatan abstraktif dinilai lebih baik karena cara kerjanya mendekati cara kerja manusia ketika meringkas teks atau yang disebut parafrase. Metode yang digunakan pada penelitian ini adalah Long Short-Term Memory (LSTM) yang mana metode tersebut telah sukses melakukan peringkasan dalam Bahasa Inggris. Dataset yang digunakan adalah kumpulan artikel berita media daring Bahasa Indonesia. Hasil terbaik yang didapatkan pada pengujian dengan metode LSTM menggunakan metode evaluasi ROUGE-1 adalah 0.13846. Kata kunci: peringkas teks otomatis, abstraktif, Bahasa Indonesia, long short-term memory, ROUGE Abstract One topic about natural language processing that is quite challenging is automatic text summarization. Automatic-text-summarization is practically divided into two kinds of approach, namely extractive and abstractive. Abstractive-approach is considered better since it resembles how humans work in terms of text summarizing or paraphrasing. A method used in this study is Long Short-Term Memory (LSTM) which has succeeded to summarize texts in English. Datasets that have been used are a number of online news articles in Bahasa Indonesia. The best result gained using LSTM based on the ROUGE-1 evaluation is 0.13846.

Recommended citation: Saputra, M. A., & Al Maki, W. F. (2021). Peringkas Teks Otomatis Bahasa Indonesia secara Abstraktif Menggunakan Metode Long Short-Term Memory. eProceedings of Engineering, 8(2).
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Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds

Published in Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2024

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology.

Recommended citation: Saputra, M. A., & Rakun, E. (2024). Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds. Indonesian Journal of Electrical Engineering and Computer Science, 36(3), 1583-1593. https://doi.org/10.11591/ijeecs.v36.i3.pp1583-1593
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Deep Learning and Explainable AI for Accurate and Interpretable Software Defect Prediction

Published in The 2024 International Conference on Data Engineering and Enterprise System (ICDEES 2024), 2024

Software defect prediction is an essential aspect of software development process as defects significantly impact software reliability and usability. Several methods have been studied to predict the defects in software, but most of the time, the prediction result lack of transparancy about which features actually contributes to the prediction results. In this study, we adopt the Convolutional Neural Network (CNN) model for defect prediction, then apply the Local Interpretable Model-Agnostic Explanations (LIME) to gain interpretability on the prediction result. In order to address the data imbalance issue, we apply the Synthetic Minority Over-sampling Technique (SMOTE). Experiments on the NASA Promise repository datasets (CM1, JM1, KC1, KC2, and PC1) shows that the model achieve accuracy ranging from 81% to 92% across the dataset. Furthermore, through the LIME analysis, some metrics such as Lines of Code (LoC) and Effort (e) give more substantial influence in the defect predictions.

Recommended citation: Saputra, M. A., Lumban Gaol, F., Soeparno, H., & Arifin, Y. (2024). Deep learning and explainable AI for accurate and interpretable software defect prediction. In Proceedings of The 2024 International Conference on Data Engineering and Enterprise System (The ICDEES 2024). Yogyakarta, Indonesia, November 29-30, 2024.
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Deep Learning and Explainable AI for Accurate and Interpretable Software Defect Prediction

Published: November 30, 2025

I had the opportunity to attend and present my research paper, “Deep Learning and Explainable AI for Accurate and Interpretable Software Defect Prediction”, at the 2024 International Conference on Data Engineering and Enterprise Systems (ICDEES), held in Yogyakarta on November 29-30, 2024.

Agile Software Development

Undergraduate course, BINUS University, School of Computer Science, 2025

Software Engineering

Undergraduate course, BINUS University, School of Computer Science, 2025

Algorithm and Programming

Undergraduate course, BINUS University, School of Computer Science, 2025