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All Journal ComEngApp : Computer Engineering and Applications Journal Scientific Journal of Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknoinfo Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Bahan dan Barang Teknik JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Teknik Informatika C.I.T. Medicom Pelita Masyarakat Madaniya Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Community Empowerment INCODING: Journal of Informatics and Computer Science Engineering J-SAKTI (Jurnal Sains Komputer dan Informatika) Mejuajua Jurnal Sisfotek Global Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Jurnal Ilmu Komputer dan Teknologi Informasi Al'Adzkiya International of Computer Science and Information Technology Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Muhathir, Muhathir
Universitas Medan Area
Religion Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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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
Analisis Fungsi Aktivasi pada Algoritma Backpropagation dalam Pengenalan Aksara Batak Toba Esrayanti Simanjuntak; Nurul Khairina; Zulfikar Sembirirng; Rizki Muliono; Muhathir Muhathir
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 8 No. 2 (2023): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v8i2.331

Abstract

Indonesia merupakan salah satu Negara Asia yang memiliki suku dan budaya yang beragam. Suku Batak merupakan suku yang ada di daerah Sumatera Utara. Suku ini terbagi menjadi beberapa jenis berdasarkan wilayahnya. Suku Batak Toba memiliki bahasa daerah yang sangat unik dan sistem tulisan yang berbeda. Aksara Batak Toba sering digunakan dalam upacara keagamaan dan peristiwa penting. Dalam penelitian ini, peneliti akan melakukan studi tentang aksara Batak Toba. Peneliti akan menganalisis Algoritma Backpropagation dalam pengenalan aksara Batak Toba dengan variasi fungsi aktivasi. Data input berupa file citra yang akan melalui tahap preprocessing, diikuti dengan ekstraksi fitur, normalisasi, pelatihan, dan pengujian pola aksara Batak Toba. Pada proses pelatihan dan pengujian pola, peneliti akan menggunakan data latih yang terdiri dari beberapa jenis aksara dan melakukan beberapa kali pengujian dengan jumlah epoch yang bervariasi, yaitu 150, 300, 450, 600, 750, 900, 1050, dan 1200 epoch. Dari hasil pengujian yang dilakukan, diperoleh hasil akurasi tertinggi pada dua jenis fungsi aktivasi, khususnya pada epoch ke-1050. Akurasi pada fungsi aktivasi Sigmoid Bipolar mencapai 80,53% dan pada fungsi aktivasi Sigmoid Biner mencapai 78,95%.
Enhancing Oil Palm Leaf Disease Classification using a Pruned SqueezeNet Architecture Nugraha Rahmadan Diyanto; Muhathir; Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16524

Abstract

The SqueezeNet architecture is known to be effective but possesses a considerable number of parameters, which can be optimized using pruning—a compression technique that significantly reduces model parameters without sacrificing accuracy. This research aims to apply the L2-Norm based pruning method to the SqueezeNet architecture and compare its performance (accuracy and efficiency) against the default SqueezeNet model for classifying four classes of oil palm leaf diseases. The study used a primary dataset of 4,000 images, divided into training (70%), validation (20%), and testing (10%) sets. The SqueezeNet architecture was pruned using L2-Norm structured pruning with a uniform distribution at rates from 10% to 50%, followed by fine-tuning. The results show that the default SqueezeNet achieved 97.50% accuracy with 724,548 parameters. Significantly, a 10% pruning rate actually increased the accuracy to a high of 99.25% while simultaneously reducing the parameters to 579,036. Overly aggressive pruning, such as 40%, drastically decreased accuracy to 93.25%. It is concluded that the 10% pruning rate is the most optimal, proving that this method not only makes SqueezeNet lighter but also more effective. This 10% pruned model is highly suitable for application implementation due to its enhanced efficiency. Future research is recommended to validate these findings using a more diverse dataset and to test the model on actual edge devices.
Rancang Bangun Sistem E-Katalog Toko Bangunan Berbasis Web pada Platform KatalogQu di PT Era Cipta Digital Fauzy, Muhammad; Muhathir
Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 3 No. 1 (2026): Maret
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jikti.v3i1.1841

Abstract

The advancement of technology has driven the use of social media to promote products, including in the building materials industry. To address this, the author designed a website e-catalog as a solution for promoting building materials products. This website serves as a sub-website of the KatalogQU platform at PT. Era Cipta Digital, featuring a ready-to-sell template that can be customized through an admin dashboard. Store owners can adjust the website's appearance according to their needs. The site displays building materials products with various features and appealing layouts. The E-Catalog system is built using the Laravel framework, with key features such as CRUD for products and categories, appearance settings, and catalog content management. The outcome is a sub-website that can be used by various building material stores, making it easier for store owners to promote and sell their products online. This website allows building material stores to increase product visibility and expedite transactions, providing store owners with a more efficient way to run their business.
Pruning-Based ShuffleNetV1 Optimization for Plant Disease Image Classification and Web-Based System Prototype Implementation: Indonesia Taufik Ismail Simanjuntak; Muhathir Muhathir; Fadlisyah Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.15374

Abstract

Diseases and pest infestations on tea plants can significantly reduce the quality and quantity of production, necessitating an early detection system based on artificial intelligence. Although deep learning architectures are capable of providing high accuracy, their large model size remains a major constraint for deployment on resource-limited devices. This study aims to compress the ShuffleNet V1 architecture using an L2-Norm based structured pruning method for the classification of six classes of tea leaf conditions utilizing a dataset from Mendeley Data. The model evaluation is carried out using the 5-Fold Cross Validation method with a fine-tuning process for 5 epochs to restore the network representation capacity after pruning. The experimental results demonstrate that the application of structured pruning successfully reduces the total parameters and computational operations significantly without sacrificing model performance. The compressed model is able to maintain an optimal accuracy reaching up to 99% across various pruning scale scenarios from 10% to 50%, while simultaneously providing a noticeable inference speedup. In the final stage, the best compressed model file is integrated into a web interface program, enabling users to perform tea leaf disease classification practically and responsively directly through a web browser. This research proves that the combination of ShuffleNet V1 and structured pruning can produce a highly lightweight yet accurate model for web implementation needs
Co-Authors Al Khowarizmi Albar, Rizka Amri Ismail Tumanggor Andre Hasudungan Lubis Arief Goeritno Aripin Rambe Ayu Pariyandani Azmi, Fadhillah Cahyo Hasanudin Cut Lika Mestika Sandy Cut Try Utari Deti Indah Kiranti Diah Ayu Larasati Dian Ifantiska Dina Maizana Dinur Syahputra Dwipayana, Mahendar Effiati Juliana Hasibuan Eka Pirdia Wanti Ellis Susmawati Esrayanti Simanjuntak Essay Puspita Sitopu FADHILLAH AZMI Fadli, MHD. Fajar Alry Fadlisyah Fadlisyah Fadlisyah Fadlisyah Fadlisyah Fauzi FAUZI . Fitra , Akbario Gultom, William Habib Satria Hashina Qiamu Mumtaziah Hayani Wulandari Idrus, Syed Zulkarnain Syed Indra Muda Insidini Fawwaz Ira Safira Ira Safira Juliansyah Putra Tanjung Karynda Natalie Theofilus Leonardi Paris Hasugian M. Hamdani Santoso Maghfirah Maghfirah Mahardika Abdi Prawira Tanjung Mahmudah Salwa Gianti Marpaung, Febriady Melisah Melisah Merri Hafni Moulando Tampubolon Muchammad Takdir Sholehati Muhammad Fadlan Siregar Muhammad Fauzy Mungkin, Moranaim N P Dharshinni Nadzifah Nadzifah Napisah, Napisah Nasution, Annisa Neneng Yulia Barky Noor, Fredy Nugraha Rahmadan Diyanto Nurul Khairina Pariyandani, Ayu Purba, Sentia Ovania Rambe, Yunita Syafitri Reydo Trisno Pangestu Reyhan Achmad Rizal Rifa Alia Syahidah Rizki Muliono Saufa Yardha Moerni Sibarani, Theofil Tri Saputra Simangunsong, Roy Candra Simanjuntak, Juan Siregar, Erlina Sri Juwita Sri Wahyuni Subairi Subairi Susilawati Susilawati Syahputra, Dinur Syahputra, Dinur Syifaul Fuada Syuhada, Rahmad Taufik Ismail Simanjuntak Taufik Ismail Simanjuntak Theofil Tri Saputra Sibarani Tika Ermita Wulandari Wahyu Hidayah Wanti, Eka Pirdia Wibawa, M. Bayu Yahya, Yanawati Binti Yanawati Yahya Yopan Rahmad Aldori Yuhefizar Yuhefizar Zebua, Meniati Zuhar Musliyana, Zuhar Zulfikar Sembiring Zulfikar Sembirirng