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Kab. indragiri hilir,
Riau
INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,176 Documents
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Comparing the Accuracy and Interpretability of CNN, Transformer, and ConvNeXt for Skin Lesion Classification M. Denny Richardo
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6390

Abstract

Early detection of skin cancer plays an important role in improving treatment outcomes, particularly in cases of melanoma. Numerous studies have employed deep learning models for skin lesion classification; however, most have focused on a single architectural paradigm and placed greater emphasis on improving classification accuracy than on model interpretability. This study presents a comparative analysis of three deep learning architectural paradigms: EfficientNet-B4 as a representative Convolutional Neural Network (CNN), Swin Transformer as a representative Vision Transformer (ViT), and ConvNeXt as a representative modernized CNN for the classification of seven types of skin lesions using the HAM10000 dataset. All models were trained using consistent training configurations, with analytical class weighting applied to address class imbalance. Model performance was evaluated using accuracy, precision, recall, Macro F1-score, and confusion matrices, while Explainable AI (XAI) techniques, including Grad-CAM and attention maps, were employed to assess the transparency of the models' decision-making processes. The experimental results show that ConvNeXt achieved the best overall performance, with an accuracy of 91.62% and a Macro F1-score of 0.8897. It also achieved higher recall for the melanoma class than EfficientNet-B4 and Swin Transformer. The XAI visualizations further demonstrate that ConvNeXt was able to focus its attention on lesion regions that were more clinically relevant compared with the other two models. These findings indicate that ConvNeXt provides a better balance between classification performance and model interpretability, suggesting its potential as a reliable approach for supporting artificial intelligence-based skin lesion diagnosis systems.
Data-Centric AI based Indonesian ALPR System using Yolov11 And TrOCR Zulfikri Rizki Ardi; Andi Sunyoto
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6697

Abstract

The implementation of Automatic License Plate Recognition (ALPR) in Indonesia faces challenges arising from visual degradation and anomalies in real-world datasets, including annotation errors, data leakage, and imbalanced distributions of regional license plate codes. This study aims to develop a precise and adaptive end-to-end ALPR system for Indonesian vehicle license plates by optimizing both license plate detection and character recognition architectures. A Data-Centric AI approach was employed to improve dataset quality through bootstrap relabeling and the targeted synthesis of 2,773 images to address regional distribution imbalances. The proposed system adopts a two-stage processing pipeline, consisting of license plate localization using YOLOv11m and text recognition through a comparative evaluation of PaddleOCR, Tesseract, EasyOCR, and TrOCR-base. Specifically, TrOCR was optimized using a two-stage fine-tuning strategy (K4 Two-Stage) that combines synthetic data during pretraining with real-world data during subsequent fine-tuning. The results demonstrate that the proposed framework effectively mitigates dataset anomalies. The YOLOv11m model achieved a license plate detection mAP@50 of 97.05%. Among the recognition models, TrOCR K4 achieved the best performance, with an Exact Match (EM) accuracy of 86.21% and a Character Error Rate (CER) of 3.03%. End-to-end inference evaluation achieved an overall exact-match accuracy of 87.07%, with a low inference speed of 2.84 FPS (approximately 206 ms per license plate) on an NVIDIA L4 GPU. Overall, the integration of Data-Centric AI, YOLOv11, and TrOCR produced a highly accurate and efficient Indonesian license plate recognition system, demonstrating strong potential for real-time vehicle monitoring applications.
Digital Infrastructure Determinants of SPBE Implementation Effectiveness: Evidence from the North Kalimantan Provincial Government Imelda imelda; Emha Taufiq Luthfi
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6769

Abstract

The implementation of the Electronic-Based Government System (SPBE) has become a key strategy for digital government transformation in Indonesia. However, its effectiveness remains influenced by various aspects of digital infrastructure that are not yet fully optimized, particularly in geographically challenging regions such as North Kalimantan Province. The implementation of SPBE in North Kalimantan Province has demonstrated a substantial improvement in its SPBE index over the past five years. Despite this continuous increase, limited empirical research has examined the contribution of individual digital infrastructure dimensions to the effectiveness of SPBE implementation. This study aims to analyze the effects of network infrastructure, IT resources, information security, and government system integration on the effectiveness of SPBE implementation in North Kalimantan Province. A quantitative approach was employed using 20 research indicators and 20 questionnaire items, with data collected from 75 respondents through a census sampling technique. The data were analyzed using multiple linear regression. The descriptive statistical results indicate that information security had the highest mean score, whereas IT resources had the lowest mean score among the variables examined. The multiple regression analysis shows that network infrastructure, IT resources, information security, and government system integration significantly affect SPBE effectiveness, both individually and simultaneously. Network infrastructure was identified as the most dominant determinant (β = 0.361; p < 0.001), while the regression model explained 97.0% of the variance in SPBE effectiveness (Adjusted R² = 0.970). Based on these findings, the study recommends accelerating the equitable development of digital infrastructure and providing continuous training for IT personnel to improve the effectiveness of SPBE implementation. The findings provide practical guidance for the North Kalimantan Provincial Government to strengthen SPBE implementation by accelerating digital infrastructure development, particularly in remote areas with limited network coverage, while enhancing the capacity and availability of qualified IT resources.
Design of Information Technology Governance for Employee Training Data Management using the COBIT 2019 Design Toolkit Mayer Jeloe Susilo Perdana; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6625

Abstract

The rapid advancement of technology in the digital era requires companies to continuously innovate to remain competitive. The implementation of information technology has become a crucial factor in sustaining business operations, particularly in data management and data quality. PT XYZ faces challenges in managing employee training data due to the absence of an integrated system, resulting in data inaccuracies and inconsistencies. The COBIT 2019 framework enables the company to identify gaps between its current governance practices and the governance processes required to support organizational objectives. The recommendations derived from the audit based on the COBIT 2019 Design Factors indicate that the company needs to develop an integrated system as a fundamental foundation for improving data quality and harmonization in alignment with business objectives and decision-making requirements.
Severity Classification of Diabetes Mellitus Patients at the Puskesmas Pembantu of Binjai City using the K-Nearest Neighbor (KNN) Algorithm Nadya Putri Dwinta; Asrianda Asrianda; Rini Meiyanti
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6835

Abstract

Diabetes mellitus (DM) is a chronic disease with an increasing prevalence, including in Binjai City. Manually determining the severity level of DM patients requires a high degree of accuracy and careful assessment because it involves multiple medical indicators. Therefore, a classification method is needed to assist healthcare professionals in making faster and more accurate assessments. This study aims to develop a classification system for determining the severity of Type 1 and Type 2 diabetes mellitus in patients at Puskesmas Pembantu in Binjai City using the K-Nearest Neighbors (KNN) algorithm and to evaluate its classification performance. The dataset consisted of 594 patient medical records collected from 2024–2025, comprising 13 attributes, including age, height, weight, body mass index, waist circumference, systolic blood pressure, diastolic blood pressure, blood glucose level, and encoded attributes. The data preprocessing stage involved transforming categorical attributes and applying Z-score normalization before calculating similarity using the Manhattan distance metric. The model was evaluated using values of k ranging from 1 to 15 to determine the optimal k. The results show that k = 1 achieved the highest accuracy of 98.86%, with a precision of 97.71%, recall of 100%, and an F1-score of 0.99. The KNN algorithm was subsequently implemented in a web-based system that enables healthcare professionals to enter patient data and obtain classification results directly. These findings demonstrate that the KNN algorithm using the Manhattan distance metric is effective for classifying DM severity levels and can serve as a decision-support tool for healthcare professionals at Puskesmas Pembantu in Binjai City.
Improving Marketing and Operational Efficiency in PT Rumekar Agribusiness through an Integrated E-Commerce based on a Design Thinking Approach Donny Octariyanto; Magdalena A. Ineke Pakereng
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6840

Abstract

The advancement of information technology has driven the need for digital transformation across various sectors, including the agribusiness industry. PT Rumekar currently faces several operational challenges due to the continued use of conventional methods, including manual stock recording, fragmented financial data reporting, slow information flows, and limited marketing reach. This study aims to design an integrated web-based agribusiness e-commerce information system for PT Rumekar to address these challenges. The novelty of this study lies in integrating a sales module (digital storefront) with real-time inventory management and financial reporting within a single platform specifically tailored to agribusiness workflows. The system was designed using the Design Thinking methodology, which adopts a user-centered approach through five iterative stages: Empathize, Define, Ideate, Prototype, and Test. The system was developed using the Laravel framework and provides interfaces and functional features tailored to customers (users) and system administrators (admins). System testing was conducted using two approaches: Black Box Testing to evaluate system functionality and the System Usability Scale (SUS), involving five respondents consisting of three users and two system administrators to assess usability. The Black Box Testing results indicate that all system features functioned as designed and met the specified requirements. Meanwhile, the SUS evaluation yielded scores of 78.0 from users and 78.5 from administrators. Both scores correspond to Grade B and fall within the “Acceptable” acceptability range. These findings indicate that the Rumekar e-commerce website is feasible as a solution for improving sales efficiency, report management, and information dissemination. The system also has potential for adoption by other agribusiness companies facing similar operational challenges.

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