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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 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
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Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Other

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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

Abstract

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
Analysis K-Nearest Neighbors (KNN) in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction Muhathir, Muhathir; Sibarani, Theofil Tri Saputra; Al-Khowarizmi, Al-Khowarizmi
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 3, No 2 (2022)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v1i1.11

Abstract

Pulmonary tuberculosis is an infectious disease caused by Microbacterium tuberculosis, which is one of the lower respiratory tract disease, which is largely in the pulmonary tissue of the lung infection and then undergoes a process known as the primary focus of Ghon. Because the disease is difficult and takes a long time to decide the patient is affected by the disease Tuberkolusis, then the detection of the patient affects Tuberkolusis by utilizing the K-NN method as a classification and HOG as feature extraction. Results of the classification of positive diagnosis with a total of 234 samples from 330 samples or successfully recognizable Sebasar 70.90%, while the classification result is a negative diagnosis with the amount of 240 samples from 330 samples or successfully identified by 72.72%. The results of the study showed the image classification of the X-ray Set Tuberculosis using the method K-NN and HOG feature with cross-validation 5 folds with 71.81% accuracy. Keyword : tuberculosis, K-NN, HOG.
PERANCANGAN SISTEM INFORMASI ABSENSI KARYAWAN BERBASIS WEB PADA PT DOTRI GADAI JAYA Zebua, Meniati; Muhathir, Muhathir
Jurnal Teknologi Terapan and Sains 4.0 Vol 4 No 2 (2023): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v4i2.11542

Abstract

Dalam era perkembangan teknologi informasi komputer yang pesat, kebutuhan akan informasi menjadi lebih mudah diperoleh melalui berbagai kemudahan yang ditawarkan. Peranan komputer dalam pengolahan data telah menjadi sangat penting dalam menyelesaikan berbagai masalah, karena kecepatannya yang tinggi dalam pemrosesan data dan mampu mempermudah pekerjaan manusia. PT Dotri Gadai Jaya sebagai perusahaan pergadaian swasta menghadapi masalah dalam proses absensi karyawan menggunakan sistem sidik jari. Sering terjadi kegagalan dalam mengidentifikasi sidik jari, penarikan data manual yang merepotkan, dan rekapitulasi data yang dilakukan secara manual tanpa adanya informasi secara real-time yang rentan dimanipulasi. untuk mengatasi permasalahan tersebut, diperlukan pengembangan sebuah sistem absensi berbasis web. Sistem ini diharapkan dapat membantu karyawan dalam melakukan absensi secara efektif dan memudahkan pihak perusahaan dalam melakukan rekapitulasi absensi dengan lebih baik. Dengan adanya sistem absensi berbasis web ini, diharapkan PT Dotri Gadai Jaya dapat mengoptimalkan penggunaan teknologi informasi komputer untuk meningkatkan efisiensi dan produktivitas dalam pengelolaan data karyawan dan proses absensi secara keseluruhan
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Pembuatan Sistem Absensi Siswa Praktek Kerja Lapangan (PKL) Berbasis Web di CV Sae Akademi Digital Medan Syuhada, Rahmad; Muhathir, Muhathir
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.729

Abstract

The manual attendance system used in CV SAE Akademi Digital Medan in recording the attendance of Field Work Practice (PKL) students has several shortcomings, such as lack of efficiency, vulnerability to recording errors, and difficulty in real-time monitoring. This research aims to design and implement a more modern and efficient web-based attendance system. The research methods used include needs analysis, system design, development, implementation, and testing. The system is designed using photo upload technology to validate student attendance based on time and location digitally. The study results show that this web-based attendance system has succeeded in increasing the efficiency, accuracy, and transparency of student attendance management. In addition, the dashboard monitoring feature makes it easier for supervisors to monitor student attendance in real time. This system is expected to not only be a solution for CV SAE Digital Academy Medan but also a model for implementing a modern attendance system in other educational institutions.
Inovasi Teknologi Hijau Untuk Masa Depan Dan Peran Keteknikan Dalam Industri Berkelanjutan Di SMA Negeri 1 Tebing Tinggi Rahman Aldori, Yopan; Moerni, Saufa Yardha; Syafitri Rambe, Yunita; Susilawati, Susilawati; Ermita Wulandari, Tika; Muhathir, Muhathir; Maghfirah, Maghfirah
Mejuajua: Jurnal Pengabdian pada Masyarakat Vol. 5 No. 1 (2025): Agustus 2025
Publisher : Yayasan Penelitian dan Inovasi Sumatera (YPIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/mejuajuajabdimas.v5i1.267

Abstract

Permasalahan lingkungan global yang semakin kompleks mendorong perlunya inovasi teknologi hijau sebagai solusi menuju masa depan yang berkelanjutan. Pendidikan memainkan peran strategis dalam membentuk kesadaran generasi muda terhadap pentingnya menjaga lingkungan dan mengembangkan teknologi ramah lingkungan. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di SMA Negeri 1 Tebing Tinggi dengan tujuan utama untuk meningkatkan pemahaman siswa mengenai konsep teknologi hijau serta memperkenalkan peran keteknikan dalam mendukung industri berkelanjutan. Metode pelaksanaan kegiatan dilakukan secara edukatif dan partisipatif melalui penyampaian materi interaktif, diskusi kelompok, serta sesi tanya jawab yang mendorong keterlibatan aktif peserta. Materi yang disampaikan meliputi pengenalan teknologi hijau, studi kasus penerapannya di berbagai sektor industri, dan peran teknik dalam mendukung transformasi industri yang lebih ramah lingkungan. Hasil kegiatan menunjukkan antusiasme tinggi dari para siswa, yang ditunjukkan melalui keterlibatan aktif selama diskusi dan refleksi positif terkait peningkatan pengetahuan mereka tentang teknologi hijau. Selain memberikan dampak pada siswa, kegiatan ini juga memotivasi pihak sekolah untuk mulai mempertimbangkan integrasi tema lingkungan ke dalam kegiatan belajar mengajar, baik itu dalam bentuk tugas ataupun praktik langsung penggunaan teknologi hijau. Untuk rencana jangka panjang, kegiatan ini dapat menjadi inisiator kerjasama antara tim pengabdian dengan pihak sekolah dalam pelaksanaan praktik penggunaan teknologi hijau di sekolah.
RANCANG BANGUN SISTEM INFORMASI PENDAFTARAN PELATIHAN PADA DINAS KOMUNIKASI, INFORMATIKA DAN PERSANDIAN KABUPATEN PIDIE Muhathir, Muhathir; Payana, Mahendar Dwi; Albar, Rizka; Wibawa, M. Bayu; Musliyana, Zuhar
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 10, No 1 (2024): April 2024
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v10i1.3889

Abstract

Dinas Komunikasi, Informatika dan Persandian (Diskominfo dan Sandi) Kabupaten Pidie menyelenggarakan berbagai pelatihan untuk meningkatkan kompetensi aparatur sipil negara (ASN) dan masyarakat. Proses pendaftaran pelatihan yang manual menimbulkan keterlambatan dan inefisiensi. Jurnal ini membahas rancangan bangun sistem informasi pendaftaran pelatihan berbasis web untuk Diskominfo dan Sandi Kabupaten Pidie. Sistem ini bertujuan mempermudah pendaftaran peserta, mengoptimalkan pengelolaan data pendaftaran, dan meningkatkan transparansi informasi pelatihan.Kata kunci: Sistem informasi, Pendaftaran pelatihan, PHP, MySQLThe Department of Communication, Information and Statistics (Diskominfo dan Sandi) in Pidie Regency offers various training programs to enhance the skills of civil servants (ASN) and the public. The current manual registration process, however, leads to delays and inefficiencies. This journal explores the design and development of a web-based training registration information system for Diskominfo dan Sandi. This system aims to streamline participant registration, optimize the management of registration data, and improve the transparency of training information. Keywords: Information system, Training registration, PHP, MySQL
Co-Authors Al Khowarizmi Albar, Rizka Andre Hasudungan Lubis Dina Maizana Dwipayana, Mahendar Fadli, MHD. Fajar Alry Fadlisyah Fadlisyah Fauzi Fitra , Akbario Gultom, William Habib Satria Idrus, Syed Zulkarnain Syed Juliansyah Putra Tanjung Maghfirah Maghfirah Marpaung, Febriady Muhammad Fadlan Siregar Mungkin, Moranaim Nasution, Annisa Noor, Fredy Nurul Khairina Pariyandani, Ayu Purba, Sentia Ovania Rambe, Yunita Syafitri Rizki Muliono Saufa Yardha Moerni Sibarani, Theofil Tri Saputra Simanjuntak, Juan Siregar, Erlina Syahputra, Dinur Syuhada, Rahmad Tika Ermita Wulandari Wanti, Eka Pirdia Wibawa, M. Bayu Yahya, Yanawati Binti Yopan Rahmad Aldori Zebua, Meniati Zuhar Musliyana, Zuhar Zulfikar Sembiring