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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Terpadu Tematik : Jurnal Teknologi Informasi Komunikasi JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga)
Martin Clinton Tosima Manullang, Martin Clinton Tosima
Program Studi Sistem Komputer, Fakultas Teknik, Universitas Diponegoro
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Pendeteksian Pelanggaran Pada Penyebrangan Jalan Menggunakan Single-Shot Detector Pada ESP32 Novaldi, Fahri; Amrulloh, Iqbal; Wisesa, I Wayan Wiprayoga; Manullang, Martin Clinton Tosima
TEMATIK Vol 9 No 2 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i2.997

Abstract

Tingginya jumlah kendaraan bermotor dan pertumbuhannya di kota-kota besar, serta tingginya angka pelanggaran membuat identifikasi pelanggaran terhadap pengendara kendaraan bermotor menjadi sulit, terutama dalam hal pengendara yang berhenti di marka persimpangan jalan (zebra cross). Pemanfaatan teknologi computer vision diharapkan dapat membantu mengidentifikasi pelanggaran dengan mengenali objek berupa kendaraan bermotor yang terdapat pada area visual yang tertangkap kamera. Sistem menggunakan metode pendeteksi single-shot detector dari model yang dilatih dan diimplementasikan pada perangkat keras ESP32. Sistem yang dikembangkan tidak hanya berupa perangkat keras tetapi juga perangkat lunak antarmuka yang dapat digunakan untuk mengkonfigurasi dan menentukan region yang diinginkan. Dua macam pengujian dilakukan, empat pengujian dalam skenario real time dan 20 pengujian secara offline menggunakan dataset Pedestrian Traffic Light. Seluruh keadaan pada skenario real-time dapat dideteksi dengan tepat. Sementara itu, eksperimen offline menggunakan dataset dari Dataset Pedestrian Traffic Light menghasilkan akurasi 96,78%.
Adapting remote photoplethysmography for Indonesian subjects: an examination of diverse rPPG techniques Aprini, Istighfariza; Manullang, Martin Clinton Tosima
JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Vol. 3 No. 3: September 2023
Publisher : Jurusan Teknik Elektro, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jitel.v3.i3.2023.165-180

Abstract

Vital sign measurements are essential in intensive care patients, such as in the ICU or emergency department, and also for newborns or prenatal babies. The duty nurse usually monitors these vital signs by manually writing down the patient's condition on a large piece of paper in front of the patient's room. The lack of nurses can hinder the process of monitoring patient vital signs. However, since the COVID-19 pandemic, people have limited contact with their surroundings, making measuring vital signs with contact uncomfortable and unhygienic. The typical non-contact method for measuring heart rate is the remote photoplethysmography (rPPG) technique. In this study, we proposed to assess the performance of various rPPG algorithms on the Indonesian subjects dataset. The algorithms used are CHROM, GREEN, ICA, LGI, PBV, PCA, and POS on 70 pieces of data. Based on the test results with three types of evaluation metrics, namely MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and Bland Altman, it is found that the measurement results with the best performance POS algorithm with a low prediction error rate with the resulting MAE value of 2.59 and RMSE of 4.65.
Implementasi Penghitung Laju Respirasi pada Sistem Polisomnografi menggunakan Mikrofon dan Arduino Nano Manullang, Martin Clinton Tosima; Resfita, Nova
Jurnal Teknologi Terpadu Vol 7 No 1: Juli, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i1.295

Abstract

Sleep apnea is a severe sleep disorder leading to severe threats such as heart attacks, strokes, diabetes, kidney failure, hypertension, etc. Not only is the diagnosis of sleep apnea a challenging measure, but it also requires a high cost of equipment, the limitations of available tools, and becomes a complicated diagnosis operated personally at home. Using the microphone embedded in the Arduino Nano, a system to measure the respiratory rate develops as a minor part of the sleep apnea diagnostic system using polysomnography. A filtering system is attached to eliminate noise and environmental consequences around the observation site. This prototype evaluates by comparing the output value with the manual calculation of the respiratory rate. Of the trials executed, the achieved system accuracy in counting the respiratory rate is above 93%, meaning that this prototype system is ideal as a method of measuring the respiratory rate.
Comparative Analysis of CNN, Transformers, and Traditional ML for Classifying Online Gambling Spam Comments in Indonesian Manullang, Martin Clinton Tosima; Rakhman, Arkham Zahri; Tantriawan, Hartanto; Setiawan, Andika
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.v9i3.9468

Abstract

The rise of user-generated content on social media and live-streaming platforms has intensified the spread of spam, particularly online gambling (Judi Online) promotions, which remain prevalent in Indonesian comment sections. This study investigates the effectiveness of various machine learning (ML) and deep learning (DL) approaches in classifying such spam content in Bahasa Indonesia. We compare five models: Support Vector Machine (SVM), Random Forest (RF), a CNN-based model, IndoBERT, and a custom lightweight transformer model named Wordformer. While IndoBERT achieves the highest performance across all metrics, it comes with high computational demands. Wordformer, in contrast, delivers a strong balance between accuracy and efficiency, outperforming traditional models while being significantly more lightweight than IndoBERT. Wordformer achieved 0.9975 accuracy and macro F1-score, surpassing SVM (0.9578) and Random Forest (0.9729), while maintaining a significantly smaller model size and fewer multiply-add operations. An extensive ablation study further explores the architectural and training design choices that influence Wordformer’s performance. The findings suggest that lightweight transformer models can offer practical, scalable solutions for spam detection in low-resource language settings without the need for large pretrained backbones.
Co-Authors Amrulloh, Iqbal Andika Setiawan, Andika Aprini, Istighfariza Hartanto Tantriawan I Wayan Wiprayoga Wisesa Novaldi, Fahri Rakhman, Arkham Zahri Resfita, Nova