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All Journal Scientific Journal of Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo JOURNAL OF INFORMATICS AND COMPUTER SCIENCE INCODING: Journal of Informatics and Computer Science Engineering Mejuajua Journal of Renewable Energy, Electrical, and Computer Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Al'Adzkiya International of Computer Science and Information Technology Journal

Muhathir, Muhathir

Unknown Affiliation

Author-ID : 1475892

Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Other

Published : 22 Documents Claim Missing Document

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Articles

Title

CLASSIFICATION OF FACIAL EXPRESSIONS USING SVM AND HOG Tanjung, Juliansyah Putra; Muhathir, Muhathir
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.

MOTION MONITORING SYSTEM BASED ON IOT Susilawati, Susilawati; Sembiring, Zulfikar; Muhathir, Muhathir
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Every object is always moving, and movement of an object can occur at any time. The value of the motion which produced from an object can be very small to very large, and the impact of the movement can be at risk until very risky. For this reason, the movement of objects at risk must be observed whenever changes and observations for data retrieval can be done remotely. The purpose of this research to design an Internet of Things (IoT) devices that can observe and detect changes in the motion of an object. The device is designed to be small, around 44 x 48 millimeters with very low power consumption. The design phase begins with recording motion data using the MPU6050 accelerometer sensor as a motion detector, arduino nano as a control device, WiFi ESP8266 as a communication medium for sending data from a receiver apllication motion data with UDP protocol. The test results show that this device is very sensitive to detect changes in the motion and angle of X, Y and Z of an object.

THE E-BUSINESS COMMUNITY MODEL IS USED TO IMPROVE COMMUNICATION BETWEEN BUSINESSES BY UTILIZING UNION PRINCIPLES Fauzi, Fauzi; Al-Khowarizmi, Al-Khowarizmi; Muhathir, Muhathir
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Business is an interpersonal and organizational activity that involves the process of selling, purchasing both goods and services with the aim of making a profit. But to get a large profit, it takes many partners who have a high desire to move forward. Information technology provides services for business people so that media information is available as a sign of obstacles. In addition it is necessary to do modeling where the process of communication between businesses running on information technology has a different profit from the business being run. Thus the union has the principle of kinship and has the principle of profitability divided by the amount of contribution given so that the creation of a model in electronic business (e-business) in the hope of having a family principle that is able to provide special profits for businesses other than the profits that run on certain businesses.

Analysis of Combined Contrast Limited Adaptive Histogram Equalization (CLAHE) and Median Filter Methods for Enhancement of CCTV Screenshot Image Quality Noor, Fredy; Muhathir, Muhathir; Fadlisyah, Fadlisyah; Syahputra, Dinur
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.14016

The quality of CCTV images often deteriorates due to poor lighting, low-quality cameras, and noise, hindering effective security analysis. This study aims to assess the combined effect of Contrast Limited Adaptive Histogram Equalization (CLAHE) and median filtering on improving the quality of CCTV screenshot images by enhancing contrast and reducing noise. Using a quantitative approach, four low-quality CCTV images were processed with CLAHE to improve contrast, followed by median filtering to reduce noise. Image quality was evaluated using two metrics: Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Results showed that CLAHE significantly improved image contrast, with MSE values ranging from 17.7513 to 159.092 and PSNR from 39.4809 to 47.1987. After applying the median filter, MSE values decreased to 12.1238–22.1747, and PSNR increased to 34.7288–37.3442, indicating noise reduction. The combination of CLAHE and median filter showed even better results, with MSE values ranging from 0.000993935 to 0.00508972, and PSNR ranging from 71.1032 to 78.1966. This combination significantly improved the quality of the CCTV screenshots, making them more suitable for security and forensic analysis. The findings suggest that CLAHE and median filtering can effectively enhance image clarity. Future studies should focus on optimizing these techniques for various lighting conditions and exploring other methods to address extreme noise levels in CCTV images

Sensitivity of Weather Forecast Analysis in Comparison of Fuzzy Time Series And Artificial Neural Network Methods Fitra , Akbario; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14428

This research aims to produce a comparative level of sensitivity accuracy between fuzzy time series and artificial neural network methods in weather forecasting. The background to the problem identified is that weather conditions are always changing, so a system development is needed to help obtain accuracy values from weather forecasts by paying attention to the sensitivity of the comparison results between the two methods. The research results show that the Artificial Neural Network is effective in providing weather forecast values according to existing datasets, while the Fuzzy Time Series is able to produce sensitivity accuracy values based on existing datasets. This research also reveals that both methods are quite good in determining accuracy results on weather forecast sensitivity to meet user needs. The conclusion of this research is that both methods can provide the right solution for the development of a weather forecasting system that can be used by users.

Enchancing Brain Tumor Disease Classification via SqueezeNet Architecture Integrated with Group Convolution Gultom, William; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14552

Brain tumor classification using MRI images is a major challenge in medical image processing, particularly when facing imbalanced data between classes. This imbalance often leads to model bias toward the majority class and reduces sensitivity to the minority class—patients with tumors. This study aims to analyze the impact of applying Group Convolution techniques to the VGG19 and SqueezeNet architectures to enhance both computational efficiency and classification accuracy. A quantitative experimental approach was employed, implementing Convolutional Neural Networks (CNNs) using the PyTorch framework. The dataset includes two classes, “Yes” (with tumor) and “No” (without tumor), organized into Train, Validation, and Test folders. The models were evaluated by comparing the performance of standard architectures with modified versions integrating Group Convolution. Experimental results show that SqueezeNet with Group Convolution achieved up to 90% accuracy, outperforming the original model. Additionally, the model exhibited significantly improved sensitivity to the minority class, indicating better performance under imbalanced conditions. These findings suggest that Group Convolution enhances not only computational efficiency but also classification capability. Therefore, this technique is applicable in developing automated diagnostic systems. Future research is encouraged to combine Group Convolution with methods such as attention mechanisms to achieve more optimal and reliable classification results.

Improving the Accuracy of Coffee Leaf Disease Detection Using Squeezenet and Simam Fadli, MHD. Fajar Alry; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14557

Early detection of coffee leaf diseases such as leaf rust and Phoma is essential due to its direct impact on crop productivity and quality. Recent studies have shown that lightweight CNN architectures like SqueezeNet are effective for deployment on resource-constrained devices, though they still face limitations in classification accuracy for complex disease types. This study aims to improve the accuracy of coffee leaf disease classification by integrating the SqueezeNet architecture with the SimAM attention module, which enhances feature representation without significantly increasing model complexity. A quantitative experimental approach was used, employing an open-source dataset of coffee leaf images that was augmented and categorized into three classes: healthy leaves, leaf rust, and Phoma. The models were evaluated using accuracy, precision, recall, and F1-score metrics. Results show that integrating SimAM into SqueezeNet increased the model’s accuracy from 81% to 84%. The most significant improvements were observed in the leaf rust and Phoma classes, with F1-scores rising from 0.70 to 0.79 and from 0.73 to 0.76, respectively. Additionally, the AUC score improved to 0.91. These results demonstrate that SimAM integration effectively enhances classification performance, though challenges remain in distinguishing classes with visually similar features. Further research is recommended to implement more aggressive data augmentation and regularization techniques to improve model generalization.

Application of MobileNetV2 Architecture with SIMAM for Automatic Detection of Diseases on Mango Leaves Simanjuntak, Juan; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14612

Early detection of diseases in mango plants is crucial for improving crop yields and reducing economic losses for farmers. This study proposes the use of the MobileNetV2 architecture integrated with the Simple Attention Module (SIMAM) to enhance the accuracy of disease detection on mango leaves. MobileNetV2 was chosen for its computational efficiency, particularly on mobile devices, while SIMAM was utilized to strengthen the model’s focus on important visual features that represent disease symptoms on the leaves. The dataset used in this research consists of 3,000 images of mango leaves categorized into three classes: Capnodium, Colletotrichum, and Healthy Leaves. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the MobileNetV2 + SIMAM model achieved high performance, with an accuracy of 0.9833, precision of 0.9841, recall of 0.9833, and F1-score of 0.9833. With its combination of computational efficiency and high classification accuracy, this model has strong potential for implementation in mobile applications to assist farmers in detecting mango leaf diseases quickly, accurately, and practically in the field.

Co-Authors Al Khowarizmi Albar, Rizka Andre Hasudungan Lubis Dina Maizana Dwipayana, Mahendar Fadli, MHD. Fajar Alry Fadlisyah Fadlisyah Fauzi Fauzi Fitra , Akbario Gultom, William Habib Satria Idrus, Syed Zulkarnain Syed Juliansyah Putra Tanjung Maghfirah Maghfirah Marpaung, Febriady Moerni, Saufa Yardha Muhammad Fadlan Siregar Mungkin, Moranaim Nasution, Annisa Noor, Fredy Nurul Khairina Pariyandani, Ayu Purba, Sentia Ovania Rambe, Yunita Syafitri Rizki Muliono Sibarani, Theofil Tri Saputra Simanjuntak, Juan Siregar, Erlina Sitopu, Essay Puspita Susilawati, Susilawati Syahputra, Dinur Syuhada, Rahmad Tampubolon, Moulando Tika Ermita Wulandari Wanti, Eka Pirdia Wibawa, M. Bayu Yahya, Yanawati Yahya, Yanawati Binti Yopan Rahmad Aldori Zebua, Meniati Zuhar Musliyana, Zuhar Zulfikar Sembiring

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