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All Journal Hello World Blend Sains Jurnal Teknik Hanif Journal of Information Systems Tsabit
Andi Zulherry
Universitas Muhammadiyah Sumatera Utara
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Perancangan dan Implementasi Segmentasi LAN pada Infrastruktur Jaringan Skala Menengah Andi Zulherry; Indah Purnama Sari; Mhd. Basri
Hello World Jurnal Ilmu Komputer Vol. 4 No. 4 (2026): Edisi Januari
Publisher : Ilmu Bersama Center

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Abstract

Segmentasi jaringan merupakan salah satu strategi penting dalam perancangan infrastruktur jaringan untuk meningkatkan keamanan, efisiensi, dan pengelolaan lalu lintas data. Penelitian ini membahas perancangan dan implementasi segmentasi LAN pada infrastruktur jaringan skala menengah dengan tujuan memisahkan domain broadcast, mengoptimalkan kinerja jaringan, serta meminimalkan risiko keamanan. Metode yang digunakan meliputi analisis kebutuhan jaringan, perancangan topologi segmentasi, serta konfigurasi perangkat jaringan menggunakan router dan switch. Implementasi dilakukan dengan menerapkan Virtual Local Area Network (VLAN) sebagai teknik segmentasi utama. Pengujian dilakukan untuk mengevaluasi performa jaringan sebelum dan sesudah segmentasi melalui parameter seperti throughput, latency, dan isolasi antar segmen. Hasil pengujian menunjukkan bahwa penerapan segmentasi LAN dapat meningkatkan efisiensi penggunaan bandwidth, menurunkan tingkat broadcast, dan memperkuat keamanan jaringan pada infrastruktur skala menengah. Penelitian ini diharapkan dapat menjadi acuan bagi pengelolaan jaringan yang lebih optimal pada lingkungan organisasi dengan kompleksitas menengah.
Implementasi Keamanan Website Dari Serangan Cross Site Request Forgery Menggunakan Algoritma HMAC-SHA256 Pada Framework Laravel Ismi Qontas Lubis; Andi Zulherry
Hello World Jurnal Ilmu Komputer Vol. 5 No. 1 (2026): Edisi April
Publisher : Ilmu Bersama Center

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Abstract

Ancaman keamanan seperti Cross-Site Request Forgery (CSRF) menjadi tantangan serius bagi aplikasi web, bahkan yang dibangun dengan framework modern seperti Laravel yang memiliki proteksi bawaan. Penelitian ini bertujuan untuk merancang, mengimplementasikan, dan menganalisis efektivitas algoritma HMAC-SHA256 sebagai lapisan keamanan tambahan untuk memperkuat pertahanan terhadap serangan CSRF pada framework Laravel. Metode penelitian yang digunakan adalah penelitian terapan dengan pendekatan kuantitatif. Pengujian dilakukan menggunakan metode Black Box Testing melalui empat skenario berbeda untuk mengevaluasi sistem tanpa proteksi, fungsionalitas normal, serta efektivitas pertahanan berlapis dan lapisan HMAC secara mandiri. Hasil pengujian menunjukkan bahwa sistem tanpa proteksi sepenuhnya rentan terhadap serangan. Sebaliknya, sistem dengan pertahanan berlapis berhasil menolak serangan, di mana lapisan pertama (token CSRF Laravel) memblokir permintaan dengan respons error 419. Puncak pengujian membuktikan bahwa lapisan HMAC-SHA256 mampu berfungsi sebagai benteng pertahanan mandiri yang efektif, dengan berhasil memblokir serangan (respons error 403) bahkan ketika proteksi bawaan dinonaktifkan, tanpa mengganggu fungsionalitas normal aplikasi. Penelitian ini menyimpulkan bahwa implementasi strategi pertahanan berlapis (Defense-in-Depth) menggunakan HMAC-SHA256 secara signifikan meningkatkan ketahanan aplikasi web terhadap serangan CSRF dan terbukti menjadi mekanisme pertahanan independen yang andal.
Implementasi Sistem Pendukung Keputusan Untuk Menganalisis Tingkat Kepuasan Pengguna E-Learning Menggunakan Metode End User Computing Satisfaction (EUCS) Di SMK Multi Karya Maratul Hasanah Vianingrum; Andi Zulherry
Hello World Jurnal Ilmu Komputer Vol. 5 No. 1 (2026): Edisi April
Publisher : Ilmu Bersama Center

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Abstract

Sistem e-learning telah menjadi komponen penting dalam pendidikan modern, namun evaluasi kepuasan pengguna masih sering diabaikan. Penelitian ini bertujuan untuk mengimplementasikan Sistem Pendukung Keputusan (SPK) berbasis web dan menganalisis tingkat kepuasan pengguna terhadap sistem e-learning di SMK Multi Karya menggunakan metode End-User Computing Satisfaction (EUCS). Penelitian kuantitatif ini melibatkan 361 responden (351 siswa dan 10 guru) dengan instrumen kuesioner berbasis 20 butir pertanyaan yang mewakili lima dimensi EUCS: Content, Accuracy, Format, Ease of Use, dan Timeliness. SPK dirancang menggunakan PHP, MySQL, dan Bootstrap dengan pemodelan UML. Hasil uji validitas menunjukkan seluruh item valid (r-hitung > 0,104). Analisis kepuasan menghasilkan skor rata-rata: Content (4,531), Accuracy (4,494), Format (4,492), Ease of Use (4,508), dan Timeliness (4,494), dengan skor total 4,504 yang termasuk kategori "Sangat Puas". Meskipun semua dimensi mendapat penilaian tinggi, dimensi Format memiliki skor terendah dan menjadi prioritas perbaikan. Sistem yang dikembangkan berhasil mengotomatisasi proses evaluasi dan menyajikan rekomendasi berbasis data untuk pengembangan sistem e-learning.
ANALYSIS AND DESIGN OF A MEDICAL RECORD DATA MANAGEMENT INFORMATION SYSTEM WITH A HUMAN CENTERED DESIGN APPROACH Aldi Subari; Andi Zulherry
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/tsabit65

Abstract

Klinik Medika Al-Azhar still uses a manual medical record system that causes various operational constraints such as lost files, delays in patient data retrieval, and the absence of real-time medication stock monitoring. This research aims to analyze and design a web-based Medical Record Data Management Information System using a Human Centered Design (HCD) approach to improve data management efficiency at Klinik Medika Al-Azhar. The research employs a descriptive qualitative method with HCD stages including empathize, define, ideate, prototype, and test. Data collection was conducted through interviews, observations, document analysis, and prototype testing. The system was built using PHP and MySQL with interface design using Figma. Testing was performed using the Blackbox Testing method. The designed system successfully meets user needs in inputting, managing, and searching medical record data quickly and accurately. System implementation is capable of generating automatic medical record reports, reducing the risk of data loss, and improving the quality of clinic services. Blackbox testing results show that all system functions run properly. The HCD approach proved effective in producing an information system that aligns with user needs and preferences, improves data processing efficiency, and supports the enhancement of healthcare services.
Design and Implementation of Multi-Segment LAN Infrastructure for Computer Laboratories Andi Zulherry; Muhammad Gunawan; Mhd. Basri
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/tsabit91

Abstract

Educational computer laboratories require a reliable and well-structured network infrastructure to support learning activities and efficient resource management. However, many laboratory networks are still implemented using a single network segment, which can lead to high broadcast traffic and reduced network performance as the number of connected devices increases. This study proposes the design and implementation of a multi-segment Local Area Network (LAN) infrastructure based on institutional needs in an educational computer laboratory environment. The proposed network architecture consists of four laboratory rooms with a total of 160 computers, where each laboratory operates within a different IP network segment while remaining interconnected through routing mechanisms. Network devices such as the MikroTik RB750Gr3 hEX router are used to manage gateway functions, DHCP services, and network address translation (NAT) for internet connectivity. The implementation is evaluated through connectivity tests between laboratory networks and internet access tests. The results show that all laboratory networks successfully communicate with each other without packet loss and demonstrate low latency values, indicating stable network performance. In addition, internet connectivity tests confirm that all laboratory networks can access external resources reliably. These findings demonstrate that the proposed multi-segment LAN infrastructure improves network organization, scalability, and manageability within educational computer laboratory environments.
Implementation of Multi-Room Computer Laboratory Network Infrastructure Based on Star Topology in an Educational Environment Andi Zulherry
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.
Anomaly Detection in Electrical Energy Consumption Using Long Short-Term Memory (LSTM) Andi Zulherry; Mhd. Basri; muhammad Gunawan
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/tsabit123

Abstract

The increasing deployment of smart meters has generated large volumes of electrical energy consumption data, creating new opportunities for intelligent anomaly detection to reduce non-technical losses, equipment failures, and abnormal consumption patterns. Conventional statistical and rule-based approaches often struggle to capture complex temporal dependencies in sequential electricity usage data. This study proposes a Long Short-Term Memory (LSTM)-based anomaly detection model to identify abnormal electricity consumption patterns with high accuracy. A time-series dataset consisting of historical hourly electricity consumption records was collected from smart metering systems and preprocessed through missing-value imputation, normalization using Min-Max Scaling, and sequence windowing. The proposed LSTM model was trained to learn normal consumption behavior and detect anomalies based on prediction error using an adaptive threshold determined from reconstruction residuals. Model performance was evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC), and compared with conventional Machine Learning methods, including Support Vector Machine (SVM) and Isolation Forest. Experimental results demonstrate that the proposed LSTM model achieved an accuracy of 97.3%, precision of 96.8%, recall of 97.9%, F1-score of 97.3%, and an AUC of 0.985, outperforming the baseline models in detecting anomalous electricity consumption patterns. The superior performance is attributed to the LSTM architecture's ability to model long-term temporal dependencies and nonlinear consumption behaviors. These findings indicate that LSTM provides an effective and reliable approach for real-time anomaly detection in smart energy systems, supporting intelligent energy management, reducing power losses, and improving the operational reliability of modern electrical distribution networks.
Implementasi Algoritma DCT (Discrete Cosine Transform) dan K-Means Clustering untuk Kuantisasi Warna pada Konversi Citra JPG ke Format GIF Sumita Wardani; Andi Zulherry; Karina Andriani; Ichsan Firmansyah
Blend Sains Jurnal Teknik Vol. 5 No. 1 (2026): Edisi Juli
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v5i1.1865

Abstract

Meningkatnya kebutuhan akan penyimpanan dan transmisi citra digital yang efisien mendorong penelitian di bidang kompresi citra dan teknik kuantisasi warna. Penelitian ini bertujuan untuk mengimplementasikan algoritma Discrete Cosine Transform (DCT) yang dikombinasikan dengan K-Means Clustering untuk kuantisasi warna dalam proses konversi citra JPG ke format GIF. Algoritma DCT digunakan untuk mereduksi komponen frekuensi tinggi pada citra guna meningkatkan efisiensi data, sementara algoritma K-Means Clustering diterapkan untuk menghasilkan palet warna yang optimal sesuai dengan batasan format GIF, yaitu maksimal 256 warna. Penelitian ini menggunakan pendekatan eksperimen kuantitatif dan mengimplementasikan sistem menggunakan bahasa pemrograman Python pada platform Google Colab. Parameter evaluasi yang digunakan meliputi Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan ukuran file. Hasil penelitian menunjukkan bahwa proses DCT berhasil mengurangi ukuran file dari 1347,83 KB menjadi 1198,46 KB, dengan nilai MSE sebesar 42,3175 dan nilai PSNR sebesar 31,8692 dB. Proses kuantisasi K-Means dengan 256 klaster menghasilkan nilai MSE sebesar 47,6381 dan nilai PSNR sebesar 31,3504 dB, dengan ukuran file sebesar 5124,70 KB. Eksperimen lanjutan dengan jumlah klaster yang berbeda menunjukkan bahwa pengurangan jumlah klaster menurunkan ukuran file namun juga menurunkan kualitas citra; GIF dengan 8 klaster menghasilkan ukuran file terkecil, yaitu 684,35 KB, dengan nilai PSNR sebesar 25,9143 dB. Temuan ini mengindikasikan bahwa kombinasi algoritma DCT dan K-Means dapat secara efektif mengkonversi citra JPG ke format GIF, meskipun terdapat pertukaran (trade-off) antara efisiensi ukuran file dan kualitas visual citra.
Co-Authors Aldi Subari Ichsan Firmansyah Indah Purnama Sari Ismi Qontas Lubis Karina Andriani Maratul Hasanah Vianingrum Mhd. Basri Muhammad Gunawan Sumita Wardani