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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Media Sisfo Systematics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Jurnal Computer Science and Information Technology (CoSciTech) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Komtekinfo Jurnal Informatika Ekonomi Bisnis Journal Medical Informatics Technology
Jufriadif Na`am, Jufriadif
Universitas Putra Indonesia YPTK
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

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Tuberculosis Extra Pulmonary Bacilli Detection System Based on Ziehl Neelsen Images with Segmentation Bob Subhan Riza; Jufriadif Na'am; Sumijan Sumijan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1939

Abstract

Tuberculosis Extra Pulmonary (TBEP) is one of the infectious diseases that can cause death. The bacterium Mycobacterium tuberculosis is the cause of this disease. Patients suffering from this disease must be treated quickly. Currently, patients need a long time and a large cost in detecting the bacteria that cause this disease. The technique used is to take the patient's lung fluid by biopsy and given Ziehl Neelsen chemical dye and then observed using a microscope. This study aims to help detect bacteria quickly and precisely by processing the image produced by the microscope. The technique used is to develop the segmentation method. The segmentation process carried out is to develop a Hue Saturation Value (HSV) color space transformation technique with Active Contour, Edge Detection, and Otsu techniques. The images used in this research are 51 images taken from H. Adam Malik Hospital, Medan and have been validated by an expert. Of the several segmentation methods used in this study, the maximum or best result in detecting Tuberculosis Extra Pulmonary (TBEP) bacilli is the Otsu method. So the method developed is very helpful in accelerating the detection of TBEP.
Segmentation in Identifying the Development of Ground Glass Opacity on CT-Scan Images of the Lungs Na`am, Jufriadif
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.1

Abstract

Ground Glass Opacity (GGO) in the image of the lungs is an object that is white in color. The image was recorded using a Computerized Tomography Scan (CT-Scan). This object has very similar color features to other objects in the lung image, making it very difficult to identify precisely. Likewise by observing the development of this object every time from recording continuously. This study aims to segment the GGO on CT-Scan images that are examined repeatedly due to an increase in complaints against patients. The processed image is an image of the lungs from the CT-Scan equipment. Patients were recorded twice at different time intervals. The processed image is an axial slice of the data cavity as a whole, totaling 12 images for each patient in each recording. The tool used for recording is a CT-Scan with the General Electric (GE) brand model D3162T. The method used is parallel processing with a combination of Image Enhancement techniques, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify the development of the GGO pixel size well, where the increasing number of patient complaints, the larger the GGO area. The extent of development of GGO is irregular with respect to time and examination. Each patient experienced an expansion of GGO by an average of 0.54% to 1.89%. This study is very good and can correctly identify ARF, so it can be used to measure the level of development of ARF in patients with accuracy.
Classification of Myopia Levels using Deep Learning Methods on Fundus Image Bismi, Waeisul; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.8

Abstract

Disorders of the eye or also known as eye disease is a condition that can affect vision for some people in their lifetime. There are 40 types of eye disorders or eye diseases, one of which is Myopia. Myopia is a visual disturbance that causes objects that are far away to appear blurry, but there is no problem seeing objects that are near. Myopia or nearsightedness is also known as minus eye. From this description, it is very important to conduct research in detecting eye diseases before the increase in eye minus and blindness. This study aims to classify myopic eye disease using the Deep Learning method with several different architectures, namely the VGG16, VGG19 and InceptionV3V3 models. Where the first is to distinguish normal and abnormal while the other is to classify with Augmented myopia image dataset and non augmented myopia image dataset obtained from the Retinal Fundus Multi-Disease Image Dataset (RFMID). In the implementation of the Deep Learning method using 20 Epochs. The results of the accuracy of the classification of eye diseases using the non augmented myopia image dataset are 66.0% for the VGG16 architectural model, then 95.99% for the VGG19 architectural model and 93.99% for the InceptionV3 architectural model and the accuracy results using the Augmented myopia image dataset are 97.53% for the VGG16 architectural model, 97.53% for the VGG19 architectural model and 99.50% for the InceptionV3 architecture model.
Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis Saut Parulian, Onesinus; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 2 No. 3, September 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i3.40

Abstract

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Dwiza Riana; Jufriadif Na'am; Saputri, Daniati Uki Eka Saputri; Sri Hadianti; Faruq Aziz; Suryadi Putra Liawatimena; Alya Shafra Hewiz; Dika Putri Metalica; Teguh Herwanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5420

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.
Adaptive AI for the King of Diamonds Game: A Bayesian Approach to Imperfect Information and 0.8-Average Dynamics Hamzah, Nasir; Na`am, Jufriadif
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1212

Abstract

This research delves into the algorithmic complexities of the King of Diamonds game from Alice in Borderland II, a unique variant of the Keynesian Beauty Contest. This game features imperfect information, dynamic player elimination, and a critical rule where the objective is to choose a number closest to 80% of the average of all chosen numbers. We propose and evaluate a Bayesian Learning Agent designed to adapt its strategy against diverse opponents. The BLA employs Bayesian inference to dynamically update its beliefs about opponent behaviors, integrating these predictions into a Keynesian Beauty Contest decision-making framework. Through extensive simulations, the BLA consistently demonstrates superior performance. For instance, in games against four random opponents, the BLA achieved a survival rate of 67.00%, significantly outperforming the random players' combined 33.00% survival rate, and consistently maintained an average absolute distance to the target of 10.59 units across rounds. Notably, against four naive Fifty players, the BLA achieved a 100.00% survival rate with an extremely low average distance of 0.08 units, concluding games in a single round. Furthermore, the study provides a specialized algorithmic analysis for the game's challenging two-player endgame, where it exhibited a 1.30% draw rate in relevant scenarios. Our findings offer novel insights into designing adaptive AI agents for complex, imperfect information games with unique convergence dynamics, extending the understanding of computational strategies in evolving competitive environments.
Adaptive AI for the King of Diamonds Game: A Bayesian Approach to Imperfect Information and 0.8-Average Dynamics Hamzah, Nasir; Na`am, Jufriadif
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025): Accepted
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1212

Abstract

This research delves into the algorithmic complexities of the King of Diamonds game from Alice in Borderland II, a unique variant of the Keynesian Beauty Contest. This game features imperfect information, dynamic player elimination, and a critical rule where the objective is to choose a number closest to 80% of the average of all chosen numbers. We propose and evaluate a Bayesian Learning Agent designed to adapt its strategy against diverse opponents. The BLA employs Bayesian inference to dynamically update its beliefs about opponent behaviors, integrating these predictions into a Keynesian Beauty Contest decision-making framework. Through extensive simulations, the BLA consistently demonstrates superior performance. For instance, in games against four random opponents, the BLA achieved a survival rate of 67.00%, significantly outperforming the random players' combined 33.00% survival rate, and consistently maintained an average absolute distance to the target of 10.59 units across rounds. Notably, against four naive Fifty players, the BLA achieved a 100.00% survival rate with an extremely low average distance of 0.08 units, concluding games in a single round. Furthermore, the study provides a specialized algorithmic analysis for the game's challenging two-player endgame, where it exhibited a 1.30% draw rate in relevant scenarios. Our findings offer novel insights into designing adaptive AI agents for complex, imperfect information games with unique convergence dynamics, extending the understanding of computational strategies in evolving competitive environments.
Optimizing Image Quality for Dog Skin Disease Diagnosis: Bacterial, Fungal, and Hypersensitivity Cases with MATLAB Puspitaningtyas, Mery Oktaviyanti; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.54

Abstract

Skin diseases in dogs, such as hypersensitive dermatitis, fungal infections, and bacterial dermatoses, present diverse clinical signs that complicate diagnosis in veterinary practice. This study employs MATLAB as an image-processing tool to enhance diagnostic accuracy through a structured pipeline. A dataset of 500 canine skin images obtained from Kaggle was processed using enlargement, histogram equalization, Gaussian filtering, and Sobel convolution. These methods improved image quality by enhancing contrast, reducing noise, and clarifying lesion boundaries. The experimental results demonstrate that the processed images allow veterinarians to more easily detect key diagnostic features, including changes in lesion texture, color, and shape. Enhanced visual clarity supports faster identification of disease patterns and reduces diagnostic ambiguity in clinical settings. This study highlights the potential of MATLAB-based image processing as an effective decision-support tool for veterinary dermatology, enabling quicker and more reliable treatment planning. Future work may integrate deep learning classification to further automate disease recognition.
The Concept of Green Human Resource Management in Industry Adif, Riandy Mardhika; Na`am, Jufriadif; Nazir, Novizar
AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Vol. 4 No. 1 (2020)
Publisher : Asia Pacific Network for Sustainable Agriculture, Food and Energy (SAFE-Network)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/ajarcde.v4i1.35

Abstract

The integration of environmental management into Human Resource Management (HRM) is called Green HRM. There is a growing need for the application of Green HRD in industry. The objective of this review is to explore green human resource management practices of organizations in the industry based on the existing literature. Based on this review, it is concluded that by understanding and increasing the scope and depth of green HRM practices, organizations can improve their environmental performance in a more sustainable manner than before. The green HRM practices are more powerful tools in making organizations and their operations in industry green. The green performance, green behaviors, green attitude, and green competencies of human resources can be shaped and reshaped through the adaptation of green HRM practices.
Co-Authors Adiddo Restiady ADIF, RIANDY MARDHIKA Agung Ramadhanu Agus Perdana Windarto AHMADI Ainul Mardiah Alya Shafra Hewiz Apriade Voutama Asep Kurniawan Bob Subhan Riza Bob Subhan Riza Catur Suharinto Catur Suharinto Dika Putri Metalica Dodi Andre Putra Dolly Indra Dwiza Riana Elfina Novalia Elvin Syahrin Eri Prasetyo Wibowo, Eri Prasetyo Faruq Aziz Feriantano Sundang Pranata Feriantano Sundang Pranata Gunadi Bin Senitio Gunadi Nurcahyo Gunadi Widi Nurcahyo Iman Priyadi Irawadi Putra Johan Harlan Johan Harlan Johan Harlan Julius Santony Julius Santony Julius Santony Julius Santony Kurnia, Hezy Larissa Navia Rani Mardison Mardison Maulia Rahman Mesran, Mesran Montesna Muhammad Abrar Masril Mutiana Pratiwi Nasir Hamzah Novizar Nazir Nur, Rofil M Puspitaningtyas, Mery Oktaviyanti Raju Wandira Rakhmad Kuswandhie Riandy Mardhika Adif Romi Hardianto Rosda Syelly Sahari Sahari Saputri, Daniati Uki Eka Saputri Sarifuddin Madenda Sarjon Defit Saut Parulian, Onesinus Sri Hadianti Sugiarti, Sugiarti Sumidjan Sumidjan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Suryadi Putra Liawatimena Syafri Arlis Teguh Herwanto Waeisul Bismi Wanto, Anjar Yanto Saputra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yundari, Yundari