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All Journal CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer)
Ho, Patricia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Other

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Real-Time Monitoring System and IOT Smart Parking Booking (Case study: One Hotel in Tangerang) Nadeak, Yogi Valentino; Ho, Patricia; Masriah, Masriah
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol 8, No 2 (2024)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/crc.v8i2.18020

Abstract

Since cars and motorbikes are the most popular personal mobility, public spaces must always provide enough parking for every visitor. However, occasionally there are more parking spaces available than there are guests, which makes it difficult for visitors to find an empty spot and takes a while. It can be found in the case study conducted in a hotel in Tangerang. This case study informs the development of an Internet of Things (IoT) smart parking system that uses Wi-Fi and the MQTT communication protocol to monitor user behavior, identify available parking spaces, and recognize vehicle entry. The ESP8266 microcontroller serves as the sensor node. Other sensors that are used include the RFID RC-522 sensor to determine how long a car user has been in the area, the Servo SG92R infrared (IR) sensor to detect the presence of a vehicle, and the HC-SR04 ultrasonic sensor to detect parking spaces. The outcomes showed that the Thingsboard platform's monitoring capability and the ability to reserve particular parking spaces using an MIT App Inventor app proved the smart parking system's successful implementation.
LSTM-Based Hand Gesture Recognition for Indonesian Sign Language System (SIBI) on Affix, Alphabet, Number, and Word Ho, Patricia; Santoso, Handri
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9607

Abstract

Sign language plays a critical role in enabling communication for the Deaf and hard-of-hearing community in Indonesia, yet there remains a significant gap in technological support for recognizing the official Indonesian sign language, Sistem Isyarat Bahasa Indonesia (SIBI). This study presents a deep learning-based hand gesture recognition system for SIBI, focusing on four primary gesture categories: affix, alphabet, number, and word. A large and diverse dataset of 21,351 videos was collected, covering 18 affix, 26 alphabet, 35 number, and 29 word classes. Hand keypoints were extracted using MediaPipe Holistic, and a bidirectional long short-term memory (BiLSTM) model was trained using 5-fold stratified cross-validation. The model achieved high recognition performance in the alphabet, number, and word categories, with mean test accuracies of 93.94%, 91.48%, and 92.41%, respectively, and slightly lower performance in the affix category at 68.17%. The affix category posed particular challenges due to subtle hand shape differences and high variability between signers, while the alphabet category consistently showed the highest accuracy due to its distinct and static handshapes. Evaluation metrics, including precision, recall, F1-score, and confusion matrix analysis, provided further insights into model strengths and limitations. Overall, the study demonstrates the effectiveness of LSTM models for sequential hand gesture recognition in SIBI and highlights areas for future improvement, such as handling non-manual features and improving generalization across signers.
Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data Lijaya, Hendra; Ho, Patricia; Santoso, Handri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2383

Abstract

In the digital era, banks face challenges in delivering fast, accurate, and efficient customer service, especially for frequently asked simple questions. This study evaluates the effectiveness of three open-source Large Language Models (LLMs), namely Gemma2-9B-Sahabat-AI, Qwen2.5-14B-Instruct, and Mistral-Nemo-Instruct in supporting a Retrieval-Augmented Generation (RAG) question-answering system for the banking sector. Using 12,000 synthetic billing documents indexed with intfloat/multilingual-e5-large-instruct embeddings (1024 dimensions), model performance was assessed via semantic similarity metrics, LLM-as-a-Judge scores (GPT-4o-mini and Gemini 2.0 Flash), and human validation Gemma2-9B-Sahabat-AI achieved the highest semantic similarity score (0.9627), followed by Mistral (0.9614) and Qwen2.5 (0.9284). In LLM-as-a-Judge evaluations, Qwen2.5 ranked highest on GPT-4o-mini (92.2), while Gemma2 led under Gemini 2.0 Flash (88.4). Human evaluators gave perfect scores for factual questions (1–10), but all models struggled with arithmetic in question 13. Gemma2’s average response time was 41 seconds, faster than Qwen2.5’s 72 seconds and Mistral’s 48 seconds, confirming Gemma2’s balanced performance in accuracy, speed, and computational efficiency. These findings underscore the potential of locally operated open-source LLMs for banking applications, ensuring privacy and regulatory compliance. However, limitations include reliance on synthetic data, a narrow question set, and lack of user diversity. Future research should involve broader queries, real user testing, and numeric reasoning modules to ensure robust and scalable deployment in real-world banking customer service environments.
Real-Time Monitoring System and IOT Smart Parking Booking (Case study: One Hotel in Tangerang) Nadeak, Yogi Valentino; Ho, Patricia; Masriah, Masriah
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol. 8 No. 2 (2024)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/crc.v8i2.18020

Abstract

Since cars and motorbikes are the most popular personal mobility, public spaces must always provide enough parking for every visitor. However, occasionally there are more parking spaces available than there are guests, which makes it difficult for visitors to find an empty spot and takes a while. It can be found in the case study conducted in a hotel in Tangerang. This case study informs the development of an Internet of Things (IoT) smart parking system that uses Wi-Fi and the MQTT communication protocol to monitor user behavior, identify available parking spaces, and recognize vehicle entry. The ESP8266 microcontroller serves as the sensor node. Other sensors that are used include the RFID RC-522 sensor to determine how long a car user has been in the area, the Servo SG92R infrared (IR) sensor to detect the presence of a vehicle, and the HC-SR04 ultrasonic sensor to detect parking spaces. The outcomes showed that the Thingsboard platform's monitoring capability and the ability to reserve particular parking spaces using an MIT App Inventor app proved the smart parking system's successful implementation.
Co-Authors Lijaya, Hendra masriah masriah, masriah Nadeak, Yogi Valentino Santoso, Handri Siti Helmyati