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All Journal Jurnal Minfo Polgan (JMP) Hello World Journal of Artificial Intelligence and Engineering Applications (JAIEA) Tarbiyah bil Qalam : Jurnal Pendidikan Agama dan Sains Hanif Journal of Information Systems AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal Tsabit
Al-Khowarizmi
Universitas Muhammadiyah Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Library & Information Science Mathematics Social Sciences Other

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Analisis Kinerja Algoritma FPM Dalam Mengidentifikasi Pola Pembelian Impulsif Pada Shopee Berdasarkan Fenomena Fomo Mirza Rian Arief Lubis; Al-Khowarizmi
Hello World Jurnal Ilmu Komputer Vol. 4 No. 4 (2026): Edisi Januari
Publisher : Ilmu Bersama Center

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Abstract

Penelitian ini menganalisis kinerja algoritma Frequent Pattern Mining (FP-Growth) dalam mengidentifikasi pola pembelian impulsif pada platform Shopee yang dipicu oleh fenomena Fear of Missing Out (FOMO) di kalangan generasi milenial dan Gen-Z. Penelitian menggunakan pendekatan deskriptif kuantitatif dengan menganalisis data transaksi 50 pengguna yang melakukan minimal 6 transaksi dalam dua minggu terakhir dengan menggunakan fitur paylater. Data dianalisis menggunakan algoritma FP-Growth dengan nilai minimum support 20%, minimum confidence 20%, dan lift ratio >1. Hasil penelitian menunjukkan bahwa: (1) FP-Growth berhasil mengidentifikasi pola pembelian impulsif dengan confidence tertinggi sebesar 91,7% pada aturan Otomotif → Pakaian. (2) Kategori Pakaian mendominasi pola pembelian dan muncul dalam 35 transaksi. (3) Teridentifikasi pola asosiasi yang kuat dan kompleks, termasuk hubungan asimetris seperti Kesehatan → Kecantikan dengan confidence 78,6%. Penelitian ini menyimpulkan bahwa FP-Growth efektif dalam mengungkap pola pembelian impulsif yang dipengaruhi FOMO dan paylatter, serta dapat menjadi dasar bagi pengembangan strategi pemasaran yang lebih terarah di platform e-commerce.
Sentiment Analysis of Pre-Loved Shoe Product Sales Based on X Reviews with a Comparison of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Algorithms Setyo Harry Nugroho; Al-khowarizmi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1773

Abstract

The rapid growth of social media enables consumers to express opinions about products openly, including preloved shoes. These reviews are crucial as they can influence purchase intentions and brand perception. This study aims to analyze user reviews on the X (Twitter) platform using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. A total of 1,005 reviews were collected, then preprocessed and balanced into 738 data consisting of positive and negative sentiments. The results show that SVM achieved an accuracy of 68%, while LSTM obtained 61.49% in its best configuration. Thus, SVM demonstrates better efficiency in classifying simple text, whereas LSTM requires more complex parameters to achieve optimal performance. This research is expected to serve as a reference for utilizing sentiment analysis to support business decision-making in the preloved product market.
Rancang Bangun Game Zombie Menggunakan Kodular Berbasis Android Indah Purnama Sari; Al-Khowarizmi; Oris Krianto Sulaiman; Dicky Apdilah
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13622

Abstract

Game Zombie merupakan game yang menggunakan komputer untuk melakukan simulasi menembak. Dalam penelitian ini, dilakukan implementasi game tembak dengan Kodular. Kodular memungkinkan untuk membuat game tembak dengan mudah dengan menggunakan antarmuka drag- and-drop yang intuitif. Metode yang digunakan meliputi pembuatan konsep game, implementasi, pengujian, dan evaluasi. Tujuan dari penelitian ini adalah untuk membuat sebuah game zombie online berbasis android yang menarik, menyenangkan, dan memberikan pengalaman bermain yang memuaskan. Hasil dari penelitian ini adalah sebuah game zombie berbasis android dengan fitur seperti kontrol, karakter, senjata, dan musik. Game ini diimplementasikan dengan platform kodular dan diuji untuk mengevaluasi kinerjanya. Hasilnya menunjukkan bahwa game zombie online berbasis android yang dibangun telah mencapai tujuan yang ditetapkan.
Ai-Based Web Application Design For Photographic Image Quality Optimization Through Digital Image Filtering Method Ibnu Pribudianto; Al-Khowarizmi
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit64

Abstract

Image quality in photography is often disrupted by factors such as poor lighting or incorrect focus, resulting in blurry images. This study aims to enhance image sharpness using digital image filtering methods, namely Unsharp Masking, High-Pass Filtering, and Sobel Filter. These methods are tested to evaluate their effectiveness in clarifying image details. The study also develops a web-based application powered by AI to help users edit images without requiring technical skills. A quantitative experimental method with a descriptive approach is used, and evaluation is conducted using PSNR, SSIM, and user questionnaires. The results show that the application of sharpening methods can significantly improve the quality of photographic images, and integration into a web platform provides easy access for the general public. This application is expected to be a practical solution for photographers, editors, and general users to obtain high-quality images efficiently and quickly.
Design And Construction Of Parking Lot Security System Using Internet Of Things And RFID Technology In Megaland Housing Complex Riza Salma; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 1 (2025): VOLUME 2, NO 1: JUNE 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i1.76

Abstract

The parking lot security system is a very important aspect in maintaining the security and comfort of residents in a housing complex. This thesis aims to design and build a parking lot security system using Internet of Things (IoT) and Radio Frequency Identification (RFID) technology at the MegaLand housing complex. This system integrates IoT devices to monitor and control vehicle entry and exit access, and uses RFID technology to identify each vehicle that has a parking access permit. The use of this technology is expected to increase the efficiency and effectiveness of parking lot management, reduce the risk of theft, and make it easier for residents to access the parking area. The results of implementing this system show a significant improvement in the security and parking management aspects of the MegaLand Housing Complex.
Application In Distinguishing Artificial Intelligence-Makened Images And Original Images With Visual Feature Extraction Using Ensemble Learning Algorithm Moh Hafiz Naufal; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.174

Abstract

The development of generative Artificial Intelligence (AI) technology enables computer systems to produce highly realistic images that closely resemble real photographs. This condition creates challenges in distinguishing AI-generated images from real images visually. This research aims to develop an image classification system capable of distinguishing AI-generated images and real images using visual feature extraction and ensemble learning algorithms.The research method consists of several stages including image preprocessing by resizing images to 256 × 256 pixels, visual feature extraction including RGB color histogram, grayscale intensity distribution, texture features using Gray Level Co-occurrence Matrix (GLCM), and edge features using the Canny Edge Detection method. The extracted features are then used as input for several classification algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest. Furthermore, model combination is performed using an ensemble learning method with a hard voting technique.The experimental results show that the Random Forest model achieved an accuracy of 65.71%, while the ensemble learning method achieved an accuracy of 65.00% with an F1-score of 0.6918. The developed system is also implemented as a web-based application using the Streamlit framework, allowing users to upload images and obtain prediction results directly. The results indicate that the combination of visual feature extraction and ensemble learning can be used as an approach to help identify AI-generated images and real images.
Code Plagiarism Detection Using Graphic Neural Network Based On Abstract Syntax Tree Fitra Affandi Hasibuan; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.177

Abstract

Code plagiarism is a common issue in education and software development, which is difficult to detect accurately using text-based approaches. Conventional methods such as Term Frequency–Inverse Document Frequency (TF-IDF) and cosine similarity tend to focus only on token similarity, making them less effective in handling structural changes in code. Therefore, this study aims to develop a structure-based code plagiarism detection system using Abstract Syntax Tree (AST) and Graph Neural Network (GNN). The proposed method involves parsing source code into AST, representing it as a graph, and processing it using a GNN model in a pairwise scheme. In addition, a comparison is conducted with a baseline method based on TF-IDF and cosine similarity to evaluate model performance. The dataset used consists of both synthetic and real data, which are divided into training and testing sets. The results show that the GNN model achieves excellent performance with an accuracy of 0.9946, precision of 0.9949, recall of 0.9974, and F1-score of 0.9962, while the baseline method only achieves an accuracy of 0.7392 and a recall of 0.6343. These results indicate that the GNN model is more effective in detecting plagiarism, especially in handling structural code modifications. Therefore, it can be concluded that the structure-based approach using AST and GNN outperforms text-based approaches in code plagiarism detection.
Penerapan Metode Content and Language Integrated Learning (CLIL) dalam Pembelajaran untuk Mengatasi Hambatan Bahasa Indonesia di Saengsattha School, Thailand M.Rafi; Al-Khowarizmi
Tarbiyah bil Qalam : Jurnal Pendidikan Agama dan Sains Vol. 10 No. 1 (2026): Vol X Edisi I 2026
Publisher : Sekolah Tinggi Ilmu Tarbiyah Al-Bukhary Labuhanbatu

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Abstract

This study investigates the application of Content and Language Integrated Learning (CLIL) to overcome Indonesian language barriers at Saengsattha School in Southern Thailand, where multilingual students encounter academic vocabulary limitations and speaking anxiety. The objective is to evaluate CLIL's effectiveness in enhancing elementary students' language proficiency. Employing a quasi-experimental one-group pre-test post-test design, the population comprised Prathom 4-6 students, with a purposive sample of 45 (Prathom 4: 13, Prathom 5: 15, Prathom 6: 17). Written tests assessed basic vocabulary, pronouns, and simple sentences, analyzed through descriptive statistics (means, differences, percentages). Results indicate scores improved from 56.2 (pre-test) to 75.8 (post-test), a 34.9% increase. The conclusion affirms CLIL effectively supports contextual language mastery and reduces affective barriers in multilingual settings.
Development of an Android-Based Plant Care Simulation Game Using the Finite State Machine Method and Time-Based Growth Algorithm M Rayyan Al Fariz Prasetya; Al-Khowarizmi
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit77

Abstract

The advancement of digital technology has created new opportunities for delivering educational content in an interactive manner, particularly on the topic of plant growth cycles, which are often difficult to understand theoretically through traditional learning methods. This study aims to develop an Android-based plant care simulation game utilizing the Finite State Machine (FSM) method and the Time-Based Growth algorithm, which can serve as an alternative medium for both entertainment and education. The FSM method is implemented to manage structured transitions between plant growth phases, while the Time-Based Growth algorithm regulates the duration of each growth stage based on time and in-game interactions, such as watering and fertilizing. The development process follows the Multimedia Development Life Cycle (MDLC), which consists of the stages of concept, design, material collecting, assembly, testing, and distribution. The implementation results indicate that the system is capable of dynamically representing the plant growth cycle through mechanisms involving plant health (HP), water levels, a delay-death phase, and growth acceleration through fertilizer usage. Based on testing results, the application functions according to the design specifications and provides an interactive and engaging learning experience.
Implementation of Multi-Room Computer Laboratory Network Infrastructure Based on Star Topology in an Educational Environment Andi Zulherry; Al-Khowarizmi
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i2.68

Abstract

Reliable network infrastructure is essential to support digital-based learning activities in educational institutions, particularly in computer laboratories that require stable and simultaneous internet access for a large number of devices. This study aims to implement a multi-room computer laboratory network infrastructure consisting of 160 PCs distributed across four laboratory rooms, each containing 40 computers. The network architecture is designed using a star topology, where each PC connects to an access switch within its respective room, and all switches are connected to a central modem acting as the primary gateway and Dynamic Host Configuration Protocol (DHCP) server. The infrastructure follows a peer-to-peer model without centralized server deployment or bandwidth management configuration. The implementation process includes physical network installation, structured cabling, automatic IP configuration through DHCP, and connectivity testing to ensure proper communication and internet accessibility. The results show that all 160 PCs successfully obtained IP addresses without conflicts and were able to access the internet simultaneously under normal operating conditions. The star topology provided ease of installation, simplified troubleshooting, and effective fault isolation. These findings indicate that the implemented infrastructure operates reliably as a foundational network system and provides a baseline for future development, including network segmentation, bandwidth management, and centralized service integration.
Co-Authors Andi Zulherry Dicky Apdilah Fitra Affandi Hasibuan Ibnu Pribudianto Indah Purnama Sari M Rayyan Al Fariz Prasetya M.Rafi Mirza Rian Arief Lubis Moh Hafiz Naufal Oris Krianto Sulaiman Riza Salma Setyo Harry Nugroho