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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Media Informatika Instal : Jurnal Komputer Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Journal of Artificial Intelligence and Digital Business West Science Interdisciplinary Studies West Science Interdisciplinary Studies Journal of Biomedical and Techno Nanomaterials
Justam, Justam
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Development of a Motor Vehicle Rearview Image Pattern Recognition System for Detection of Traffic Flow Violations on One-Way Roads: Image processing Sitti Mawaddah Umar; Justam, Justam
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.6052

Abstract

This study aims to detect traffic violations, specifically motorcycles riding against the flow on one-way roads, by utilizing computer vision technology to recognize the rearview patterns of vehicles. The method employed involves applying the deep learning model Faster-RCNN for object detection, using image data captured from an IP camera mounted on a pole at a height of 2.5 meters with a 45-degree tilt angle to optimally monitor vehicles from behind. Image labeling was performed using the LabelImg application, while model training and classification were conducted using the TensorFlow framework. The developed system achieved a detection accuracy of 88%, demonstrating the effectiveness of this approach in identifying motorcycles violating traffic direction. These findings highlight the potential of computer vision as an automatic and real-time solution for traffic monitoring, which can help reduce dangerous violations and enhance road safety. Therefore, this research contributes significantly to the development of more advanced and efficient traffic violation detection systems.
Klasifikasi Buah Kopi dengan Visi Komputer pada Kecepatan Konveyor Diva, Clara; justam, justam
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2490

Abstract

Kopi merupakan komoditas pertanian penting, namun proses sortasi manual seringkali tidak efisien. Pemanfaatan visi komputer dapat meningkatkan efisiensi dan akurasi, tetapi kecepatan konveyor seringkali menyebabkan gambar menjadi kabur (motion blur), yang menghambat identifikasi kematangan. Penelitian ini bertujuan untuk mengembangkan sistem sortasi yang akurat, bahkan saat buah kopi bergerak cepat, untuk meningkatkan kualitas produk. Metode yang digunakan adalah deteksi objek menggunakan YOLOv4, yang kemudian dilanjutkan dengan perbaikan gambar menggunakan Wiener filter dan Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk mengatasi efek blur. Ekstraksi fitur dilakukan dengan ruang warna RGB, dan Support Vector Machine (SVM) digunakan untuk klasifikasi tingkat kematangan. Hasil penelitian menunjukkan bahwa pada kecepatan 35 rpm, baik Wiener filter maupun kombinasi Wiener filter dan CLAHE memberikan akurasi tertinggi sebesar 85.82%. Namun, seiring dengan peningkatan kecepatan konveyor, akurasi sistem menurun secara signifikan. Pada kecepatan 60 rpm, akurasi dengan Wiener filter turun menjadi 40.51%, sementara kombinasi Wiener filter dan CLAHE hanya mencapai 7.69%. Meskipun demikian, penelitian ini membuktikan bahwa visi komputer dapat menjadi solusi efektif untuk sortasi kopi, meskipun perlu adanya optimalisasi lebih lanjut untuk kecepatan yang lebih tinggi.
Intelligent System for Coffee Bean Roast Level Classification Using Electronic Nose and Artificial Neural Network Justam; Batti, Sartho; Erlita; Fanani mz, Luqman; Sibiti, Milda
Bahasa Indonesia Vol 16 No 05 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i05.340

Abstract

Roasted coffee beans release gaseous compounds, primarily carbon dioxide (CO₂). Coffee comes in various types, including Robusta, Arabica, Excelsa, Tubruk, Latte, and Luwak. However, this study focuses only on Robusta and Arabica coffee. Each roast level of coffee beans has its own distinct aroma, necessitating a fast and accurate method to differentiate them. Therefore, this research aims to classify coffee bean roast levels based on their aroma profiles. The dataset for classifying Robusta and Arabica coffee roast levels was obtained from data collection using a miniature Electronic Nose system. A total of 900 data samples were collected, with 720 samples used for training and 180 samples used for testing. This study employs an Artificial Neural Network (ANN) with an Electronic Nose for classification. The True Positive (TP) results obtained for each coffee roast level are 44 for Light roast, 55 for Medium roast, and 57 for Dark roast. The classification accuracy achieved in determining the roast level of coffee beans is 86%.
Development of an Electrical Monitoring and Control System Using a Google Assistant-Based Smart Plug Hasanuddin, M. Hasanuddin; Justam; Batari Tammaka, Arifah
Bahasa Indonesia Vol 16 No 06 (2025): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i06.344

Abstract

The increasing electricity consumption in the household sector impacts energy efficiency and quality. This research develops an Internet of Things (IoT)-based smart plug to monitor current and voltage, control power via Google Assistant, and detect power outages. The system utilizes current and voltage sensors that transmit data to a dashboard via Blynk Cloud, with an ESP32 microcontroller as the main hub. Power control is performed through Google Assistant and the Blynk dashboard, while power outage notifications are sent via email based on relay voltage values. Test results show that the system accurately monitors current and voltage, including during smartphone battery charging. Power control via the Blynk application has an average delay of 0.18 seconds, whereas control via Google Assistant shows an average delay of 3.4 seconds for activation and 5.26 seconds for deactivation. The system also successfully detects power outages and sends real-time notifications. With these features, the smart plug enhances the efficiency and intelligent management of household electricity consumption.
AI-Powered Digital Histopathology: Predicting Immunotherapy Response Using Deep Learning Judijanto, Loso; Chai, Som; Pong, Ming; Justam, Justam; Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
Co-Authors Abdul Malik Andi Saenong Ardiansyah, Muh. Batari Tammaka, Arifah Batti, Sartho Chai, Som Diva, Clara ERLITA Erlita, Erlita Fitri, Nurul Fitriani Fitriani Hasanuddin, M. Hasanuddin Hijrawati Husni Mubarak Idris, Mochammad Agus Jamilah, Nur Judijanto, Loso Khalid Ilyas, Irsyad Kurniawan, Fahmi Lewa, Revah Oktria Luqman Fanani Mz M. Hasanuddin Mangellak, Deo Muchta, Ardiansyah AR. Muchtar, Ardiansyah AR. Nampira, Ardi Azhar Nurjanah, Titin Pong, Ming Rahmah Rahmah Ramadani, Aisya Ramba, Jousadrah Sibiti, Milda Sitti Mawaddah Umar Takbir, Muh. Nashir Yuyun, Yuyun