Article Per Year (5 Year)
p-Index From 2021 - 2026
4.14
P-Index
This Author published in this journals
All Journal Jurnal Teknologi dan Manajemen Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining ILKOM Jurnal Ilmiah Jurnal Mantik Journal Dynamic SainT BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Dedikasi Jurnal Pengabdian kepada Masyarakat Jurnal Minfo Polgan (JMP) Journal of Innovation Research and Knowledge INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY J-Icon : Jurnal Komputer dan Informatika TONGKONAN: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
Juprianus Rusman
Program Studi Teknik Informatika, Universitas Kristen Indonesia Toraja, Tana Toraja, Sulawesi Selatan
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Transportation Veterinary Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Classification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural NetworkClassification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural Network Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rusman, Juprianus; Upa, Samrius; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4781

Abstract

Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.
Pest Detection on Green Mustard Plants Using Convolutional Neural Network Algorithm Arifin, Nurhikma; Rachmini, Siti Aulia; Rusman, Juprianus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.30953

Abstract

The productivity of mustard greens is vulnerable to pests and diseases that can threaten the yield and quality of the harvest. This study aims to detect pests on green mustard plants using the Convolutional Neural Network (CNN) method. The dataset used in this research consists of 450 images, with 225 images of pest-infested mustard greens and 225 images of healthy mustard greens. These 450 datasets are divided into 400 training data and 50 testing data. The testing was conducted fifteen times using CNN architectures with 2, 3 and 4 convolutional layers, having filter numbers of (64,32) (64, 32, 16) and (64, 32, 16, 8) respectively, and learning rates ranging from 0.1 to 0.00001 with the Adam optimizer. Based on the testing results of the learning rate and the number of layers, it was found that a learning rate of 0.001 provided the best performance with the highest accuracy and the lowest loss, especially in the model with 3 layers (64, 32, 16), which achieved an accuracy of 94% and a loss of 24.92%. A learning rate that is too high (0.1) or too low (0.00001) results in poor performance and instability, with low accuracy and high loss. Therefore, selecting the appropriate learning rate is crucial to achieving optimal results in model training.
LEAF DISEASE DETECTION IN TOMATO PLANTS USING XCEPTION MODEL IN CONVOLUTIONAL NEURAL NETWORK METHOD Arifin, Nurhikma; Maratuttahirah; Juprianus Rusman; Muhammad Furqan Rasyid
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1926

Abstract

This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each category having 100 images. In the model training phase using the fit method in TensorFlow, 17 epochs were performed to teach the model to recognize patterns in tomato leaf disease images in the training dataset. The model testing results on 30 tomato leaf images showed an accuracy rate of 85.84%. This result indicates a positive indication that the developed CNN model performs well in detecting and classifying tomato leaf conditions. Thus, this research can contribute to improving the understanding and management of leaf diseases in tomato plants to support more productive and sustainable agriculture.
Enhancing Tourist Experiences in North Toraja through K-Means Clustering-Based Recommendation System Srivan Palelleng; Eko Suripto Pasinggi; Juprianus Rusman
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25597

Abstract

Background: North Toraja in South Sulawesi, Indonesia, is a culturally rich region with high tourism potential due to its unique traditions. The government has invested in infrastructure to boost tourism and regional income (PAD), but has insufficiently used information systems for promotion. An innovative system that can assist tourists in navigating the diverse attractions in North Toraja based on their interests needs to be developed. Objective: This research aims to develop a recommendation system for tourist attractions in North Toraja using K-means Clustering and the Similar Characteristics Method. Methods: We used Orange Data Mining to perform K-means clustering, and then used similarity-based methods to determine the closeness of characteristics among attractions. The system analyzes based on the fields of cultural, geographical, facility, and landscape features, resulting in four distinct clusters. The clusters were defined as three tourist attractions in cluster C1, eleven in C2, four in C3, and fourteen in C4. We also developed a system interface that allows travelers to input preferences, view personalized recommendations, and access detailed information. The system's novelty lies in its specific application of K-Means Clustering to leverage these local attributes for granular categorization for effective promotion of North Toraja's diversity. Conclusion: Our approach effectively groups attractions with similar characteristics, enhancing exploration based on user interests. The high altitude and similar geographical features of North Toraja result in attractions that share natural characteristics, making this system an advancement in technology-driven tourism solutions. 
Improved Coffee Beans Detection Using Contrast Limited Adaptive Histogram Equalization and Unsharp Masking Srivan Palelleng; Nugra Tasik Allo; Juprianus Rusman
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3120.351-362

Abstract

Contrast Limited Adaptive Histogram Equalization (CLAHE) is widely used to enhance image contrast, but it has limitations in improving edge sharpness and may amplify noise. Meanwhile, Unsharp Masking (USM) is effective for edge enhancement, yet it is less optimal when applied to low-contrast images. To address these weaknesses, this study proposes a hybrid image enhancement method combining CLAHE and USM for coffee beans quality analysis. The dataset consists of four classes: Biji-Kopi Normal (normal), Biji-Kopi Pecah (broken), Biji-Kopi Hitam (black), and Biji-Kopi Berlubang (hollow). All images were trained at a resolution of 512×512 pixels for 2000 iterations without transfer learning to ensure fair and unbiased evaluation. Histogram-based analysis demonstrates significant improvements after enhancement, including a 50.19% increase in mean intensity, a 217.9% increase in variance, a 78.38% rise in standard deviation, and a 22% increase in skewness, along with a 37.42% reduction in kurtosis. Performance evaluation using YOLOv4-Tiny shows that the proposed method improves AP50 from 96.51 to 97.7 and AP75 from 59.91 to 66.24, while AP95 remains unchanged at 0.01. The most notable improvements are observed at AP70 to AP90, indicating that the hybrid approach not only enhances classification performance but also strengthens object localization accuracy, making it effective for coffee bean defect detection.
Monitoring Kelembapan Tanah Tanaman Cabai Katokkon Dengan Konsep Wireless Sensor Network Samrius Upa'; Juprianus Rusman; Amsal Tandi Pakendek
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16351

Abstract

Kelembapan tanah yang tepat berperan signifikan dalam budidaya cabai karena secara langsung memengaruhi pertumbuhan dan hasil panen tanaman. Namun, metode pengamatan manual yang selama ini digunakan sering kali menyebabkan kesalahan dalam penyiraman, baik karena penilaian visual yang kurang akurat maupun ketergantungan pada estimasi waktu penyiraman sebelumnya. Penelitian ini bertujuan untuk mengembangkan sistem monitoring kelembapan tanah yang lebih akurat dan efisien menggunakan teknologi Wireless Sensor Network (WSN). Sistem ini memanfaatkan lima node sensor yang terhubung dalam topologi mesh, dengan protokol MQTT untuk memastikan komunikasi data yang efisien. Setiap sensor dirancang untuk mengukur tingkat kelembapan tanah secara real-time dan mengirimkan data tersebut ke gateway. Gateway kemudian meneruskan data ke server ThingSpeak melalui koneksi Wi-Fi untuk pemrosesan dan visualisasi. Hasil pengujian sistem berdasarkan parameter Quality of Service (QoS) dan standarisasi TIPHON menunjukkan packet loss sebesar 3.0% dalam kategori “memuaskan, delay 358ms dalam kategori "cukup memuaskan", dan throughput 0.00279 kbps (dalam kategori "kurang memuaskan". Meskipun hasil throughput belum optimal, sistem ini tetap memberikan solusi yang efektif bagi petani untuk memantau kelembapan tanah secara real-time dan nirkabel. Dengan optimalisasi lebih lanjut, sistem ini memiliki potensi untuk meningkatkan kinerja secara signifikan dan mendukung pengelolaan pertanian yang lebih efisien.
Co-Authors Adha, Muhammad Sofwan Alexander Te’dangan Amarya Sula Amelia, Nerlys Amsal Tandi Pakendek Arifin, Nurhikma Damayanti, Irene Devi Eko Suripto Pasinggi Ferayanti Boas Gallaran Garonga, Melki Gidion A.N. Pongdatu Gidion Aryo Nugraha Pongdatu Haryati, Berlian Zetikarya Insani, Chairi Nur Irene Devi Damayanti Maratuttahirah Matarru, Sandri Merliana Michael, Aryo Milasari Milasari Muhammad Furqan Rasyid Nerlys Amelia Nofrianto Pasae Nugra Tasik Allo Padang, Semuel Yacobus Pallu, Seltiani parea russan rangan Pongdatu, Gideon A.N. Rachmini, Siti Aulia Reni Lolotandung Romiger Rongko, Yunita Toding Samrius Upa' Samrius Upa’ Sandri Matarru Srivan Palelleng Suryaningsih Patandung Trivena, Trivena Upa, Samrius Utama, Muhammad Surya Alif