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All Journal Journal Of Artificial Intelligence And Software Engineering Jurnal Teknologi Rekayasa Informasi dan Komputer International Journal of Applied Artificial Intelligence and Robotics (IJAIC)
Muhammad Khadafi
Politeknik Negeri Lhokseumawe
Computer Science & IT

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Aplikasi Manajemen Literasi Membaca dan Rekomendasi Buku Berbasis Android Menggunakan Metode Content-Based Filtering Sharhan Anhar; Muhammad Khadafi; Mustainul Abdi
Journal of Artificial Intelligence and Software Engineering Vol 4, No 1 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i1.5400

Abstract

Membaca buku adalah sebuah kegiatan yang dapat membantu kita untuk menambah pengetahuan dan wawasan. Minat baca di Indonesia saat ini cenderung rendah, yang disebabkan oleh beberapa faktor seperti diantaranya kurangnya waktu luang, kurangnya referensi buku untuk menentukan buku yang ingin dibaca, dan semakin canggih kemajuan teknologi yang menyebabkan orang lebih suka menghabiskan waktu untuk bermain handphone. Untuk menyelesaikan permasalahan tersebut dapat dilakukan dengan menggunakan aplikasi manajemen literasi membaca dan rekomendasi buku berbasis Android. Tujuan dari aplikasi ini adalah untuk memudahkan dalam mengelola kegiatan membaca serta memberikan rekomendasi buku yang bisa dibaca selanjutnya dan mengingatkan pengguna untuk membaca buku. Metode yang digunakan pada sistem ini adalah Content-Based Filtering. Dengan adanya aplikasi ini, pengguna bisa dengan mudah men-track progress membaca buku. Penerapan algoritma Content-Based Filtering pada aplikasi ini dapat memberikan buku-buku rekomendasi yang relevan dengan pengguna berdasarkan pengujian kuesioner yang dilakukan pada 10 responden dengan nilai rata-rata 3.92 dari skala hingga 5. Dengan hasil tersebut algoritma Content-Based Filtering dapat digunakan untuk memberikan rekomendasi buku.  Abstract Reading books is an activity that can help us to increase knowledge and insight. Reading interest in Indonesia today tends to be low, which is caused by several factors such as lack of free time, lack of book references to determine which books to read, and increasingly sophisticated technological advances that cause people to prefer to spend time playing cellphones. To solve these problems, it can be done by using an Android-based reading literacy management and book recommendation application. The purpose of this application is to make it easier to manage reading activities and provide recommendations for books that can be read next and remind users to read books. The method used in this system is Content-Based Filtering. With this application, users can easily track the progress of reading books. The implementation of the Content-Based Filtering algorithm in this application can provide recommended books that are relevant to users based on questionnaire testing conducted on 10 respondents with an average value of 3.92 on a scale of up to 5. With these results the Content-Based Filtering algorithm can be used to provide book recommendations.
Online Warehouse Application at PT. PLN (Persero) ULP Lhokseumawe City Yusma Subhina; Salahuddin Salahuddin; Muhammad Khadafi
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 8, No 2 (2025): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v8i2.7456

Abstract

PT PLN (Persero) ULP Lhokseumawe City is one of the State-Owned Enterprises engaged in the provision of electricity for distribution to the community. PT PLN (Persero) ULP Lhokseumawe City has a warehouse that serves as a material storage facility, functioning as a transaction location for incoming and outgoing materials, as well as for monitoring material stocks. The warehouse serves as a storage facility for materials required by all companies. Materials are tools used to meet the needs of workers and the community in supporting the distribution of electricity. Without materials, activities related to electricity cannot be carried out; therefore, it is necessary to have information about material stocks to ensure the smooth operation of a job. At the PLN ULP Lhokseumawe City office, material data collection is still conducted manually using notes or transaction recording books. If you pay attention, the manual system is not safe, because if the data is lost it cannot be recovered. Additionally, materials can be shipped from the warehouse without the supervisor's knowledge, resulting in material stock reports that do not accurately reflect the material stock in the warehouse. Material inventory is a common issue that arises when material stocks run low and technical officers require materials to support the distribution of electricity to customers. In the warehouse, some materials are rarely issued by warehouse officers, resulting in a significant accumulation of unused materials. Therefore, an application is needed to control materials in the PT PLN ULP Lhokseumawe City warehouse based on the web. The purpose of creating this application is to enable the monitoring, management, and control of materials in the warehouse, allowing for the printing of reports based on the materials stored. This can facilitate the supervision of the release of goods that are not carelessly issued and technical officers are not short of material stock. These reports will later be compiled by the TUG (Warehouse Administration) as determined by PT.PLN (Persero).
Finger Bone Fracture Classification in X-ray Images Using Convolutional Neural Network with Transfer Learning Putri Nabila; Muhammad Khadafi; Hendrawaty Hendrawaty; Juneidy Juneidy
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 2 (2026)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i2.10595

Abstract

Finger Bone Fractures are among the most common injuries and are generally diagnosed through X-ray imaging. However, the diagnostic process is still largely performed manually by radiologists or physicians, requiring a high level of expertise and potentially leading to interpretation errors, particularly in cases involving subtle fractures. Therefore, an intelligent system is needed to assist medical personnel in detecting fractures more quickly and accurately. This study aims to develop a web-based system for classifying finger Bone Fractures from X-ray images using a Convolutional Neural Network (CNN) with a Transfer Learning approach based on the EfficientNet-B0 architecture. The datasets used in this study are obtained from FracAtlas and HBFMID, which contain finger X-ray images. The research methodology consists of dataset verification, image preprocessing, model training and testing using EfficientNet-B0, and performance evaluation based on Accuracy, precision, recall, F1-score, and the Confusion Matrix. The developed system also provides features for user management, X-ray image upload, examination history, prediction visualization using Gradient-weighted Class Activation Mapping (Grad-CAM), and diagnosis verification by physicians. The expected outcome of this study is a classification model capable of accurately detecting finger Bone Fractures and a web-based system that can assist radiographers and physicians in the diagnostic process. Furthermore, this research is expected to improve the efficiency of radiological examinations and support faster, more objective clinical decision-making.Keywords: EfficientNet-B0, Finger Fracture Detection, X-ray Image, Deep Learning, Web-Based System.
Design and Development of an Acehnese Batik Motif Classification System Based on Convolutional Neural Network and Explainable AI Reyhan Putra Syahmi; Muhammad Khadafi; Azhar Azhar; Syarifah Annisa Salsabila
International Journal of Applied Artificial Intelligence and Robotics Vol. 2 No. 2 (2026): Vol 2 No 2 (November 2026)
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v2i2.28

Abstract

Batik Aceh is an Indonesian intangible cultural entity characterized by high visual morphological complexity and historical depth. Conventional motif identification processes face limitations due to observer subjectivity, high spectral variance, and the intricate geometric structures composing the ornaments. Therefore, this study aims to design an automated digital image classification architecture utilizing the Convolutional Neural Network (CNN) algorithm via VGG16 implementation empowered by Transfer Learning based on ImageNet pre-trained weights. To demystify the deep learning black-box nature, the computational ecosystem is integrated with Explainable AI using Gradient-weighted Class Activation Mapping (Grad-CAM) for visual inference transparency, alongside an interactive Virtual Try-On module deployed on the Streamlit web platform. The classification scope encompasses four distinct classes: Bukan Batik (Non-Batik), Pinto Aceh, Pucok Reubong, and Rencong. The dataset comprises 700 balanced images (175 per class) sourced from the Roboflow public repository and partitioned into 80% training (560 images), 10% validation (68 images), and 10% testing (72 images). The model was compiled with the Adam optimizer (learning rate 10⁻⁴), Categorical Cross-Entropy loss, and supervised by dynamic callbacks (EarlyStopping and ReduceLROnPlateau). Empirical evaluation on 72 unseen test images yielded an overall accuracy of 97.00%, an average precision of 97.50%, a recall of 97.00%, and an F1-score of 97.00%. Specifically, the Pinto Aceh and Pucok Reubong motifs achieved a perfect 100% score across all metrics, while Rencong reached a recall of 88.89%. Comprehensive Black-Box testing across 10 functional interface scenarios and White-Box code logic verification demonstrated 100% operational reliability, confirming that the proposed system is highly robust for digital cultural heritage preservation.
Co-Authors Azhar Azhar Hendrawaty Juneidy Juneidy Mustainul Abdi Putri Nabila Reyhan Putra Syahmi Salahuddin Salahuddin Sharhan Anhar Syarifah Annisa Salsabila Yusma Subhina