Article Per Year (5 Year)
p-Index From 2021 - 2026
1.713
P-Index
This Author published in this journals
All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Media Informatika Instal : Jurnal Komputer Journal of Artificial Intelligence and Digital Business West Science Interdisciplinary Studies West Science Interdisciplinary Studies Journal of Biomedical and Techno Nanomaterials
Justam, Justam
Unknown Affiliation
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Development of a Student Attendance System Based on Face Recognition at SMKN 1 Luwu Utara Malik, Abdul; Justam; M. Hasanuddin; Kurniawan, Fahmi; Ardiansyah, Muh.; Fitri, Nurul
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.339

Abstract

The face recognition-based attendance system is an innovative solution to improve the efficiency and accuracy of student attendance at SMKN 1 Luwu Utara. This study aims to develop and implement a Face Recognition-based attendance system using the Convolutional Neural Network (CNN) algorithm. The research method used is an experimental method with stages of needs analysis, system design, implementation, and system testing. The results show that this attendance system has an accuracy rate of up to 95% in recognizing students' faces, thus reducing the risk of attendance fraud. Additionally, the system provides a seamless and automated way of tracking attendance, eliminating manual errors and reducing administrative workload. The implementation of this system has also received positive responses from the school due to its ease of use and effectiveness in recording attendance data in real-time. The system's ability to integrate with existing school databases further enhances its practicality and usability
Sentiment Analysis of Indonesian Society Towards the Merdeka Belajar Policy on Twitter Social Media Justam; Muchtar, Ardiansyah AR.; Kurniawan, Fahmi; Erlita; Hijrawati
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.341

Abstract

The Ministry of Education and Culture (Kemendikbud) announced the Merdeka Belajar policy in early 2020 as an effort to reform the national education system. This policy has generated various responses from the public, including both support and criticism. Twitter, as one of the most widely used social media platforms, has become a primary medium for the public to express their opinions, feedback, and perspectives on this policy. Sentiment analysis can be utilized to identify and classify public opinions embedded in Twitter posts to understand societal responses toward the Merdeka Belajar policy. This study aims to develop a sentiment analysis model for the Merdeka Belajar policy using a Convolutional Neural Network (CNN) algorithm. The model is designed to classify sentiments into three categories: positive, negative, and neutral. Additionally, this study applies hyperparameter tuning to optimize the model’s performance in sentiment classification. Hyperparameter tuning is conducted to determine the best parameter combination to enhance the model's accuracy. The results indicate that the developed model achieves a sentiment classification accuracy of 82.54%.
Application of Histogram of Oriented Gradients (HOG) for Evaluating Students' Visual Attention in Online Learning via Zoom Justam; Muchta, Ardiansyah AR.; Khalid Ilyas, Irsyad; Erlita; Ramadani, Aisya
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.343

Abstract

Online learning is one of the implementations of distance education that continues to develop, particularly with the utilization of video conferencing applications such as Zoom Meeting. This study aims to analyze the effectiveness of using Zoom Meeting in online learning by focusing on detecting students' attention levels based on blink analysis. In this research, an eye detection module was developed using the Dlib Model and Facelandmark, with the Histogram of Oriented Gradients (HOG) method as a feature extraction technique. Blink analysis was conducted to determine the blink ratio, which serves as an indicator of an individual's attention level. Generally, attention levels can be identified through blinking patterns, where fatigue or lack of focus is reflected in higher blink frequency. The study results show that the developed system can identify an individual's focus level with a highest accuracy of 95.56% in tests with three subjects, while the lowest accuracy was 72.24% in tests with 16 subjects. Based on the analysis of blink frequency during learning sessions using Zoom Meeting, it can be concluded that the average student focus level remains within the normal range.
Bibliometric Review on Infrastructure Monitoring with IoT Judijanto, Loso; Justam, Justam; Nampira, Ardi Azhar
West Science Interdisciplinary Studies Vol. 3 No. 06 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i06.2013

Abstract

The integration of the Internet of Things (IoT) into infrastructure monitoring has transformed how built environments are observed, maintained, and managed. This study conducts a comprehensive bibliometric review to map the research landscape, thematic trends, and collaboration patterns in the domain of IoT-based infrastructure monitoring. Using data retrieved from the Scopus database (2010–2024) and analyzed through VOSviewer, the study identifies key research clusters, influential authors, prolific countries, and the evolution of core topics over time. Results show that the research focus has shifted from basic sensor deployment and data acquisition to advanced topics such as machine learning, edge computing, data privacy, and cybersecurity. India, China, and the United States emerge as leading contributors, with dense global collaboration networks. The study highlights both the maturity of core research areas and the emergence of new directions such as blockchain integration and privacy-preserving infrastructure systems. These findings provide valuable insights for academics, policymakers, and practitioners aiming to enhance infrastructure resilience and efficiency through IoT technologies.
Bibliometric Review on Infrastructure Monitoring with IoT Judijanto, Loso; Justam, Justam; Nampira, Ardi Azhar
West Science Interdisciplinary Studies Vol. 3 No. 06 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i06.2013

Abstract

The integration of the Internet of Things (IoT) into infrastructure monitoring has transformed how built environments are observed, maintained, and managed. This study conducts a comprehensive bibliometric review to map the research landscape, thematic trends, and collaboration patterns in the domain of IoT-based infrastructure monitoring. Using data retrieved from the Scopus database (2010–2024) and analyzed through VOSviewer, the study identifies key research clusters, influential authors, prolific countries, and the evolution of core topics over time. Results show that the research focus has shifted from basic sensor deployment and data acquisition to advanced topics such as machine learning, edge computing, data privacy, and cybersecurity. India, China, and the United States emerge as leading contributors, with dense global collaboration networks. The study highlights both the maturity of core research areas and the emergence of new directions such as blockchain integration and privacy-preserving infrastructure systems. These findings provide valuable insights for academics, policymakers, and practitioners aiming to enhance infrastructure resilience and efficiency through IoT technologies.
Optimasi Kinerja Sensor Ultrasonik pada Prototype Sistem Monitoring Slot Parkir Fitriani, Fitriani; Saenong, Andi; Idris, Mochammad Agus; Justam, Justam
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.8066

Abstract

The growth of private vehicles in urban areas poses significant challenges in managing parking spaces efficiently and accurately. The Internet of Things (IoT) technology offers a promising solution for developing an automated parking slot monitoring system based on sensors. This study aims to optimize the performance of ultrasonic sensors by evaluating two different installation positions: at the rear (Scenario A) and above (Scenario B) the parking slot. The research was conducted using an experimental approach with a miniature prototype, where data were collected and evaluated based on detection accuracy. The results showed that Scenario B achieved an accuracy of 85%, whereas Scenario A reached only 55%, with a 30% accuracy difference. These findings indicate that sensor placement greatly influences system performance in detecting the presence of vehicles. This study provides a significant contribution to the development of more accurate and efficient IoT-based automated parking systems.
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.
Co-Authors Abdul Malik Andi Saenong Ardiansyah, Muh. Batari Tammaka, Arifah Batti, Sartho Chai, Som Diva, Clara ERLITA Fitri, Nurul Fitriani Fitriani Hasanuddin, M. Hasanuddin Hijrawati Idris, Mochammad Agus Judijanto, Loso Khalid Ilyas, Irsyad Kurniawan, Fahmi Luqman Fanani Mz M. Hasanuddin Muchta, Ardiansyah AR. Muchtar, Ardiansyah AR. Nampira, Ardi Azhar Pong, Ming Ramadani, Aisya Sibiti, Milda Sitti Mawaddah Umar