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Bahtiar Imran
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Jurnal Computer and Technology
Published by CV. Ninety Media Publisher
ISSN : -     EISSN : 30481880     DOI : https://doi.org/10.69916/comtechno
Core Subject : Science,
Computer Science & IT
Jurnal Computer and Technology or abbreviated Comtechno is a national journal published by the Ninety Media Publisher since 2023 with E-ISSN : 3048-1880. Comtechno focuses on various issues spanning: Internet of Things (IoT), electronics engineering, software engineering, mobile technology and applications, robotics, database system, information engineering, artificial intelligence, interactive multimedia, computer networking, information system audit, accounting information system, information technology investment, information system development methodology, strategic information system (business intelligence, decision support system, executive information system, enterprise system, knowledge management), e-learning, and e-business (e-health, e-commerce, e-supply chain management, e-customer relationship management, e-marketing, and e-government). All submissions are blind and reviewed by peer reviewers. All papers can be submitted in BAHASA INDONESIA or ENGLISH.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 42 Documents
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KLASIFIKASI JENIS DAUN TANAMAN OBAT BERDASARKAN CITRA TEKSTUR MENGGUNAKAN GRAY LEVEL CO OCCURRENCE MATRIX (GLCM) DAN ALGORITMA K-NN Anakanda Bungsu Panogari Lubis; Siti Sundari
Journal Computer and Technology Vol. 3 No. 2 (2025): Desember 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i2.414

Abstract

Penentuan jenis daun tanaman obat yang cepat dan konsisten penting untuk menjamin mutu pemanfaatan fitofarmaka, sementara identifikasi manual rentan bias dan memakan waktu. Penelitian ini merancang sistem klasifikasi berbasis citra tekstur dengan ekstraksi fitur Gray Level Co occurrence Matrix dan pengklasifikasi K-NN. Alur kerja mencakup praproses citra menjadi grayscale, penyetaraan ukuran 128×128, pembentukan GLCM pada empat orientasi, serta perhitungan enam properti tekstur sehingga dihasilkan vektor fitur yang merepresentasikan pola permukaan daun. Vektor ini diklasifikasikan menggunakan K-NN pada skenario multikelas. Dataset berisi seribu citra dari sepuluh kelas yang dibagi menjadi delapan ratus data latih dan dua ratus data uji. Implementasi dilakukan di Google Colab dengan antarmuka Gradio sehingga pengguna dapat mengunggah citra dan memperoleh hasil secara interaktif. Hasil awal menunjukkan akurasi tiga puluh empat persen yang menandakan sistem telah menangkap sebagian sinyal tekstur namun masih memerlukan peningkatan. Perbaikan yang disarankan meliputi kurasi dan augmentasi data, normalisasi pencahayaan, seleksi serta penimbangan fitur, penalaan parameter K-NN, dan eksplorasi pengklasifikasi lanjutan seperti SVM atau CNN agar kinerja meningkat pada variasi citra yang lebih luas. Sistem ini menjadi baseline yang sederhana, transparan, dan mudah direplikasi untuk pengembangan berikutnya.
PENERAPAN METODE LSB (LEAST SIGNIFICANT BIT) DALAM STEGANOGRAFI CITRA DIGITAL UNTUK KEAMANAN INFORMASI Alfin Mardiaman Gea; Nur Wulan
Journal Computer and Technology Vol. 3 No. 2 (2025): Desember 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i2.415

Abstract

Penelitian ini bertujuan untuk merancang dan membangun sebuah sistem steganografi berbasis citra digital menggunakan metode Least Significant Bit (LSB). Metode ini memungkinkan penyisipan pesan rahasia ke dalam gambar dengan cara mengganti bit paling tidak signifikan dari piksel gambar, sehingga perubahan visual pada gambar tetap tidak terlihat oleh pengamat biasa. Sistem dikembangkan menggunakan bahasa Python dan diintegrasikan dengan antarmuka interaktif Gradio untuk memudahkan pengguna dalam melakukan proses penyisipan (encoding) dan ekstraksi (Decoding) pesan. Hasil pengujian menunjukkan bahwa pesan dapat disisipkan dan diekstraksi kembali secara akurat. Selain itu, evaluasi kualitas gambar dilakukan menggunakan parameter Peak Signal-to-Noise Ratio (PSNR), yang menghasilkan nilai sebesar 93.80 dB. Nilai tersebut mengindikasikan bahwa kualitas gambar setelah proses penyisipan tetap sangat baik dan tidak mengalami degradasi signifikan. Dengan demikian, metode LSB terbukti efektif dan efisien untuk aplikasi steganografi ringan yang membutuhkan kerahasiaan pesan tanpa merusak kualitas media digital.
PERAMALAN PENJUALAN SEPATU MENGGUNAKAN METODE SVR (SUPPORT VECTOR REGRESSION) BERDASARKAN DATA HISTORIS PENJUALAN Muhammad Zaul Rabbani W.D; Ahmad Zakir
Journal Computer and Technology Vol. 3 No. 2 (2025): Desember 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i2.416

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sebuah sistem prediksi penjualan pada UD. Rabani dengan memanfaatkan algoritma Support Vector Regression (SVR). Sistem yang dibangun dirancang untuk membantu perusahaan dalam memperkirakan penjualan di masa mendatang berdasarkan data historis, sehingga dapat menunjang proses pengambilan keputusan yang lebih terukur. Manfaat utama dari sistem ini adalah memberikan gambaran prediksi penjualan yang dapat dijadikan acuan dalam perencanaan strategi bisnis, mengoptimalkan ketersediaan stok, serta meminimalisasi risiko kerugian akibat ketidaktepatan perkiraan permintaan pasar. Selain itu, sistem juga dilengkapi dengan fitur manajemen data, hasil prediksi, serta pengaturan aplikasi yang memudahkan pengguna dalam mengelola informasi secara terintegrasi. Hasil penelitian menunjukkan bahwa model SVR mampu menghasilkan prediksi penjualan dengan nilai metrik evaluasi yaitu MAE sekitar 15, MSE sekitar 340, RMSE sekitar 18, dan R² mendekati 0. Visualisasi data memperlihatkan bahwa hasil prediksi cenderung stabil, namun masih belum sepenuhnya mampu mengikuti fluktuasi data aktual. Kata Kunci : SVR, Prediksi, Historis, Sepatu
EFEKTIVITAS SEGMENTASI MENGGUNAKAN VLAN DAN INTER VLAN ROUTING PADA JARINGAN KOMPUTER DI PERUSAHAAN Yazid Zaidan; Haida Dafitri
Journal Computer and Technology Vol. 3 No. 2 (2025): Desember 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i2.417

Abstract

Jaringan komputer tanpa segmentasi berpotensi menimbulkan permasalahan seperti tingginya trafik broadcast, rendahnya keamanan, dan sulitnya pengelolaan jaringan. Untuk mengatasi hal tersebut, penelitian ini menerapkan Virtual Local Area Network (VLAN) dan Inter-VLAN Routing pada topologi jaringan. VLAN digunakan untuk memisahkan jaringan berdasarkan divisi (Administrasi, Keuangan, IT Support, dan HRD) sehingga komunikasi dalam satu VLAN menjadi lebih efisien dan aman. Agar perangkat dari VLAN yang berbeda tetap dapat berkomunikasi, diterapkan konfigurasi Inter-VLAN Routing dengan metode router multi-interface. Implementasi dilakukan melalui perancangan topologi, konfigurasi switch untuk segmentasi VLAN, serta konfigurasi router sebagai penghubung antar VLAN. Hasil pengujian menunjukkan bahwa jaringan memiliki kualitas sangat baik berdasarkan parameter QoS: delay rendah (0–6 ms), packet loss 0%, dan jitter kecil. Dengan demikian, penerapan VLAN dan Inter-VLAN Routing terbukti meningkatkan efisiensi, keamanan, serta fleksibilitas jaringan, sehingga dapat menjadi solusi tepat bagi organisasi skala menengah hingga besar. Kata kunci: VLAN, Inter-VLAN Routing, Router, Segmentasi Jaringan
Majority-Class Collapse in Low-Resource E-Commerce Emotion Mining: Diagnostic Evaluation of LSTM on Imbalanced and Linguistically Noisy Indonesian Reviews Muhammad Raihan; Tommy
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.407

Abstract

Product reviews on e-commerce platforms such as Shopee provide valuable emotional information that can support customer behavior analysis and business decision-making. However, automatically identifying fine-grained emotions remains challenging because user-generated reviews often contain informal language, abbreviations, spelling variations, and severe class imbalance. Although Long Short-Term Memory (LSTM) networks have demonstrated strong capabilities in sequential text modeling, their robustness under highly imbalanced and low-resource real-world conditions remains insufficiently investigated. This study evaluates an LSTM-Word2Vec architecture for multi-class emotion classification (positive, neutral, and negative) using 306 Indomie product reviews collected from Shopee through web crawling. The proposed methodology includes comprehensive text preprocessing, custom Word2Vec semantic embedding generation, LSTM-based classification, and performance evaluation using Accuracy, Precision, Recall, and F1-Score. To further quantify prediction deviations, numerical error metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are also employed. Experimental results reveal a pronounced majority-class collapse, where the model achieved only 31% accuracy by predicting nearly all testing samples as the negative class. Detailed diagnostic analysis indicates that this failure is primarily caused by the combined effects of severe class imbalance, the limited dataset size, and noisy labels generated through heuristic proxy annotation. These findings demonstrate that conventional LSTM architectures are highly vulnerable to noisy, colloquial marketplace language when adequate data quality is unavailable. Consequently, this research establishes a realistic baseline for e-commerce emotion mining and emphasizes that model complexity alone cannot overcome fundamental data limitations.
Beyond Intensity Gradients: A Lightweight Client-Side Morphological Framework for Robust Binary Image Segmentation Gregorius Ananta Sinaga; Haida Dafitri
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.408

Abstract

Edge detection in binary images frequently suffers from contour fragmentation when utilizing conventional gradient operators (e.g., Sobel, Canny), as they inherently rely on continuous intensity variations absent in discrete binary data. To address this, this study proposes a lightweight, client-side morphological gradient framework designed to structurally and topologically extract object boundaries based purely on spatial geometry. A modular web-based computational pipeline was developed using a PHP-JavaScript architecture, integrating adaptive Otsu binarization, dynamic kernel configurations, and core morphological computations. For rigorous algorithmic and engineering validation, the system was benchmarked against the conventional Canny detector using a dataset of 50 botanical images, supplemented by comprehensive white-box and black-box software testing involving 20 participants across 20 distinct test cases. Experimental results demonstrate that the proposed morphological framework achieves superior topological fidelity and boundary continuity. It secured an optimal F1-score of 0.9006 using a 3×3 disk structuring element, significantly outperforming the Canny detector’s F1-score of 0.7761. Furthermore, the method exhibits highly parallelizable efficiency, executing edge extraction at a mean runtime of 18.2 ms, more than twice as fast as the multi-stage Canny operator (42.5 ms). Software evaluations confirmed exceptional system reliability, yielding a 97.4% functional success rate, 95% overall module coverage, and an excellent usability score of 4.58/5.0. Ultimately, this research proves that shifting the paradigm from intensity differentiation to mathematical morphology offers a highly precise, robust, and computationally lightweight solution for real-time, web-native binary image segmentation.
Unlocking Customer Behavior in COD-based E-commerce: An Unsupervised Learning Approach Using Gaussian Mixture Model for Strategic Segmentation Andri Adlian Rangkuti; Haida Dafitri
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.409

Abstract

Customer segmentation in Cash on Delivery (COD) logistics is inherently challenging due to highly heterogeneous, skewed transaction data and elevated rejection rates, which frequently limit the efficacy of traditional deterministic clustering models. To address this, this study proposes a robust probabilistic segmentation framework utilizing the Gaussian Mixture Model (GMM) to extract actionable insights from complex COD operations. We analyzed 5,147 raw transaction records, meticulously aggregating them into 2,225 unique customer profiles engineered around three critical logistics-centric features: delivery frequency, total COD value, and average COD value. The model’s optimal configuration was rigorously determined using the Bayesian Information Criterion (BIC) to prevent overfitting, and the entire analytical pipeline was seamlessly integrated into an interactive, web-based Decision Support System (DSS). Empirical results demonstrate that a two-cluster solution optimally balances statistical fit and operational interpretability, clearly delineating "High-Value Active Customers" (68%) from "Low-Value Occasional Customers" (32%). Comparative analysis validates GMM’s superiority over baseline algorithms like K-Means, achieving a significantly higher Silhouette Score (0.64 vs. 0.51) and effectively capturing the non-spherical, overlapping data distributions characteristic of COD behaviors. Crucially, the probabilistic soft-clustering capability of GMM uniquely identified a transitional segment of 15.7% "boundary customers," offering logistics managers a nuanced, early-warning lens for preemptive risk mitigation and targeted interventions. Ultimately, this research bridges the critical gap between advanced probabilistic analytics and operational logistics, providing express delivery enterprises with a scalable, data-driven tool for dynamic route optimization, failed-delivery mitigation, and differentiated service personalization.
A Hybrid YOLOv5-SqueezeNet Framework with Crop-Union Context Verification for Real-Time Motorcycle Rider Identification Teguh Bagaskara; Rachmat Aulia
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.410

Abstract

Motorcycles are the dominant transportation mode in developing nations, yet their high volume exacerbates traffic violations and accidents. Automated identification of motorcycle operators is crucial for Intelligent Transportation Systems (ITS). However, existing advanced vision models, such as the Segment Anything Model (SAM) or YOLOv9, demand substantial computational resources, rendering them impractical for real-time edge deployment. Furthermore, standard object detectors often fail to contextually distinguish active riders from bystanders. To address these limitations, this study proposes a lightweight, context-aware hybrid framework integrating YOLOv5 for rapid spatial detection and SqueezeNet for semantic contextual verification. The core novelty lies in the Crop-Union Context Verification Mechanism, which extracts the spatial union of paired person-motorcycle bounding boxes, expands it proportionally, and classifies the cropped region using SqueezeNet to confirm the presence of an active operator. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing to enhance image quality under varying illumination. Experimental results demonstrate that the hybrid system successfully identifies motorcycle operators with high spatial confidence (YOLOv5: 0.84 for person, 0.86 for motorcycle) and robust contextual validation (SqueezeNet Top-1 prediction: moped, probability 0.247). By utilizing SqueezeNet’s highly efficient Fire Module architecture (~1.2 million parameters), the proposed framework achieves exceptional computational efficiency without sacrificing discriminative power. Ultimately, the disjunctive fusion of spatial and semantic signals ensures system resilience. This lightweight pipeline proves highly viable for real-time, AI-driven traffic surveillance on resource-constrained edge devices, offering a scalable solution for smart city infrastructure and automated law enforcement.
Synergizing Historical Similarity and Probabilistic Attributes: A Dual-Engine Machine Learning Model for Subsidized Housing Credit Risk Rika Saputri Lubis; Nenna Isra Syahputri
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.411

Abstract

Subsidized mortgage programs require objective and efficient credit approval processes to mitigate subjective biases and manual inefficiencies inherent in traditional evaluations. This study proposes a web-based decision support system leveraging a hybrid ensemble architecture that integrates the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms to assess the creditworthiness of subsidized mortgage applicants at Raja Batu Residence. The proposed framework utilizes KNN for historical data similarity mapping and NBC for probabilistic attribute evaluation, combining their predictions through a Soft Voting Aggregation mechanism to enhance stability. The system's performance was rigorously evaluated using optimized classification metrics and the System Usability Scale (SUS) involving 20 end-users. Empirical results demonstrate outstanding classification performance, with the optimized KNN model achieving an overall accuracy of 93.33% (yielding only 2 false positives and 3 false negatives) and the Naïve Bayes model achieving 92.00% accuracy (yielding 4 false positives and 2 false negatives). This high classification accuracy ensures a well-balanced confusion matrix with a minimized risk profile, aligning effectively with the prudent risk management required for government-subsidized allocations. Furthermore, the deployed web application achieved an "Excellent" usability score of 82.75, confirming its practical viability for non-technical administrative staff. Ultimately, the hybrid integration of KNN and NBC successfully streamlines the credit evaluation workflow, minimizing subjective bias while providing a reliable, highly accurate, and user-friendly tool for housing developers.
Beyond the Black Box in Computer Vision: A Traceable Architecture for Reproducible Canny Contour Identification Randy Hadinata; Khairunnisa
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.412

Abstract

While modern edge detection algorithms are pivotal in advanced computer vision pipelines, conventional implementations typically operate as static, transient black-boxes, severely hindering experimental replication and parameter traceability. This study addresses this methodological bottleneck by developing a structured, web-based contour identification system designed to enforce configuration management within the Canny framework. The decoupled architecture fuses a PHP-driven interface with a high-performance Python backend and a persistent relational database layer, allowing for the deterministic tracking of localized hysteresis thresholds, aperture scales, and spatial gradient vectors. Empirical evaluations were executed on a curated dataset comprising high-contrast object morphologies, characterized by distinct structural boundaries and varied illumination backgrounds to rigorously test edge degradation. The transition from linear Manhattan approximations to an isotropic Euclidean space ( gradient norm) yields single-pixel edge localization sharpness and unbroken contour continuity. Quantitatively, this mathematical refinement achieves a peak F-measure boundary accuracy of 0.91 and a Pratt’s Figure of Merit of 0.895, albeit introducing a 16.8% latency overhead. Furthermore, a multi-factor Analysis of Variance (ANOVA) robustly rejects the null hypothesis, confirming that parameter interactions significantly dictate contour fidelity (). The primary contribution of this research is the transformation of a heuristic vision task into a deterministic, database-backed ecosystem. By embedding an explicit audit trail for every processing trace, this framework provides computer vision practitioners with a rigorous instrument for quasi-quantitative comparative analysis, establishing a transparent benchmark for reproducible boundary extraction in downstream visual recognition tasks.

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