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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal CoreIT ILKOM Jurnal Ilmiah Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat The Indonesian Journal of Computer Science Research
Umar, Fitriyani
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Nursing Other

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Classification of Herbal Leaves using EfficientNetB0 Alda, A. Nurul Aisya; Indra, Dolly; Umar, Fitriyani
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38017

Abstract

The identification of herbal leaves remains a challenging task due to the high morphological and visual similarity among commonly used species, which often leads to misclassification when performed manually. This study addresses the challenge of identifying herbal leaves, namely Sauropus androgynus, Moringa oleifera, Orthosiphon aristatus, Syzygium polyanthum, and Piper betle, which are often difficult to distinguish due to high morphological and visual similarity.The proposed approach utilizes the EfficientNetB0 Convolutional Neural Network architecture and employs a two-stage fine-tuning strategy, combined with data augmentation, to enhance generalization performance. A total of 500 manually collected leaf images were used for training, resized to 224×224 pixels, and augmented through rotation and flipping. Model optimization was performed using the Adam and SGD optimizers. The trained model was evaluated on 235 previously unseen external images to assess robustness. The experimental results demonstrate that the proposed model achieved an overall classification accuracy of 88.94%, with particularly strong performance on leaf classes exhibiting distinctive morphological features, such as Orthosiphon aristatus, which obtained an F1-score of 0.96. However, the model exhibited limitations in distinguishing visually similar classes, especially between Moringa oleifera and Sauropus androgynus, both of which possess compound leaf structures, and performance degradation was observed under varying illumination conditions and complex backgrounds. The novelty of this study lies in the application of an EfficientNetB0-based fine-tuning strategy for multi-class herbal leaf classification using a limited, manually collected dataset, demonstrating its potential for deployment in mobile or other resource-constrained environments to support fast and reliable herbal plant identification.
Pengembangan Prototype Sistem Deteksi Pemilik Kendaraan Roda Empat Berbasis Internet of Think Mude, Muh Aliyazid; Umar, Fitriyani
The Indonesian Journal of Computer Science Research Vol. 5 No. 1 (2026): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i1.243

Abstract

Masalah efisiensi pada identifikasi kendaraan secara manual telah diidentifikasi sebagai kendala utama dalam sistem keamanan. Sebuah perancangan sistem deteksi pemilik kendaraan berbasis Automatic Number Plate Recognition dan Internet of Things yang mengacu pada standar International Organization for Standardization / International Electrotechnical Commission 30141 telah dikembangkan dalam penelitian ini. Fokus utama riset dilakukan terbatas pada tahap perancangan prototipe menggunakan emulator fritzing guna memvalidasi arsitektur sistem secara virtual. Hasil perancangan menunjukkan bahwa berhasil divisualisasikan dengan tingkat akurasi logika pada 5 domain  pada ISO/EIC 130141 yakni device layer, gateway & network, data management, application layer dan domain business layer Disimpulkan bahwa model perancangan pada emulator   fritzing ini layak dan dijadikan acuan awal dalam pengembangan sistem keamanan kendaraan sebelum dilakukan implementasi pada perangkat fisik sehingga untuk pengembangan selanjutnya disarankan menggunakan elumator lainya agar ada gambaran yang jelas penggunaan tools berbasis IoT
Fourier Descriptor on Lontara Scripts Handwriting Recognition Umar, Fitriyani; Darwis, Herdianti; Purnawansyah, Purnawansyah
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1040.193-200

Abstract

Hal yang kritis dalam proses pengenalan pola adalah ekstraksi fitur. Merupakan suatu metode untuk mendapatkan ciri-ciri suatu citra (image) sehingga dapat dikenali satu sama lain. Pada penelitian ini, metode deskriptor Fourier digunakan untuk mengekstraksi pola aksara Lontara yang terdiri dari 23 huruf. Deskriptor Fourier adalah metode yang digunakan dalam pengenalan objek dan pemrosesan citra untuk merepresentasikan bentuk batas segmen citra. Pengenalan karakter dilakukan dengan menggunakan jarak Euclidean dan Manhattan. Hasil pengujian menunjukkan bahwa tingkat pengenalan tertinggi mencapai akurasi 91,30% dengan menggunakan koefisien Fourier sebesar 50. Pengenalan huruf menggunakan Manhattan dan Euclidean cenderung sama atau menghasilkan akurasi yang cenderung serupa. Akurasi tertinggi dicapai saat menggunakan Manhattan sebesar 91,30%.
Analysis of Stroke Classification Using Random Forest Method Banjar, Muhammad Firdaus; Irawati, Irawati; Umar, Fitriyani; Hayati, Lilis Nur
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1252.186-193

Abstract

Stroke is a disease in which the sufferer experiences or experiences a rupture of a blood vessel in the brain so that the brain does not get a blood supply that provides oxygen. Patients who suffer from stroke will experience cognitive disorders ranging from decreased consciousness, visuospatial disorders, non-verbal learning disorders, communication disorders, and reduced levels of patient attention. Data from the World Stroke Organization shows that there are 13.7 million new stroke cases every year, and about 5.5 million deaths occur due to stroke. This research aims to analyze the attributes of any variables that affect the classification of strike disease and to test the performance of stroke classification in the form of accuracy, precision, recall, and f-measure. The method used is a random forest using a tree, namely 50, 100, 200, and 500. The classification of stroke is divided into stroke and no stroke. The data used is 5110, divided into 70% training data and 30% testing data. The results showed that the performance of a random forest using 100 trees was better than using 50, 200, and 500 trees, with an accuracy value of 86.82%, a precision of 15.76%, a recall of 38.15%, and an f1-score 22.30% after doing SMOTE.
K-Means and K-Medoid in Clustering Analysis of Network Congestion Level Darwis, Herdianti; Purnawansyah, Purnawansyah; Umalekhoa, Alfi Syahrin; Adnan, Adam; Salim, Yulita; Umar, Fitriyani; Raja, Roesman Ridwan; Fajar AR, Muh. Aqil
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2083.323-335

Abstract

This research investigates the application of clustering techniques to network congestion data at Universitas Muslim Indonesia, employing a hybrid metric approach based on packet loss and delay. The study utilized two algorithms, K-Means and K-Medoid, applied in a semi-supervised scenario to group 255,147 network data points into 3, 4, and 5 clusters, considering 10 principal variables. During the pre-processing phase, data cleansing was conducted to address missing values, followed by normalization to standardize the scale of numerical variables, thereby preparing the data for the clustering process. Model validation was performed using four cluster evaluation methods: Gap Statistic, Davies-Bouldin Index, and Elbow Method. The evaluation results indicate that both algorithms were capable of forming valid and reliable clusters. However, the K-Means algorithm demonstrated superior performance compared to K-Medoid, particularly when utilizing three Quality of Service variables: throughput, packet loss, and delay. In this configuration, K-Means yielded more stable clusters, a clearer separation between clusters, and a more structured visualization. Consequently, K-Means is considered more optimal for classifying network congestion levels and presents an effective approach for network data segmentation
Co-Authors Adnan, Adam Aini, Atifa Alda, A. Nurul Aisya Alfiyyah, Nurul Andi Sani Astuti, Wistiani Banjar, Muhammad Firdaus Basri, Haerunnisa Darwis, Herdianti Diani, Fitri Dolly Indra Dwiyanti, Sri Ulfa Fajar AR, Muh. Aqil Harlinda, Harlinda Herdianti Herdianti Hidayat, Muh Wahyu Irawati Irawati Istiqamah, Nurul lilis nurhayati Mude, Muh Aliyazid Nur Asmi Nur Rahmah Nurul, Sarah Fila Purnawansyah Purnawansyah Purnawansyah Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Suyuti, Sartika Syahrir, Alma Umalekhoa, Alfi Syahrin Veithzal Rivai Zainal