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Alam Rahmatulloh
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Program Studi Informatika Fakultas Teknik Universitas Siliwangi Jl. Siliwangi No. 24 Tasikmalaya, Jawa Barat
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Innovation in Research of Informatics (INNOVATICS)
Published by Universitas Siliwangi
ISSN : -     EISSN : 26568993     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Innovation in Research of Informatics (Innovatics) merupakan Jurnal Informatika yang bertujuan untuk mengembangkan penelitian di bidang: Machine Learning Computer Vision Internet of Things Information System and Technology Natural Language Processing Image Processing Network Security Geographic Information System Knowledge based Computer Graphic Cyber Security IT Governance Data Mining Game Development Digital Forensic Business Intelligence Pattern Recognization Virtual & Augmented Reality Virtualization Enterprise Application Self-Adaptive Systems Human Computer Interaction Cloud Computing Mobile Application Innovatics adalah jurnal peer-review yang ditulis dalam bahasa Indonesia yang diterbitkan dua kali dalam setahun mulai dari Vol. 1 No.1 Maret 2019 (Maret, dan September) dengan proses peninjauan menggunakan double-blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 94 Documents
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Enhancing YOLOv5s with Attention Mechanisms for Object Detection in Complex Backgrounds Environment Impron, Ali; Lestari, Dina; Sutriani, Linda; Anggraini, Syadza; Rizal, Randi
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16833

Abstract

Enhancing performance for object detection in complex environments is essential for real-world applications that represent complexities, such as stacking objects in the same location or environment. Models for detecting objects developed to this day still have difficulties in detecting objects with environments that have complex backgrounds. The reason is that the model often experiences a decrease in accuracy when the object to be detected is occlusion by other objects and is small in size. Therefore, in this study, a model improvement method was carried out in detecting objects in a complex environment. The algorithm used in this study is YOLOv5s. Optimization is carried out by adding a CBAM (Convolutional Block Attention Module) attention mechanism layer which is integrated with the C3 layer (C3CBAM) in the backbone of the YOLOv5s model architecture. In addition, a P2 feature map is also added to the architecture head. The optimization results carried out were quite satisfactory, namely there was an increase in the precision value by 1.6 %, at mAP@0.5 an increase of 1.4 %, and also mAP@50-95 increased by 0.1%. This proves that the enhancement method applied to YOLOv5s in this study can improve the performance of the model. However, with the addition of the attention mechanism layer, it turns out that it can increase the computational load. Therefore, for future research, a method can be applied to reduce computing load, one of the methods is knowledge distillation.
Performance and Effectiveness Evaluation of the National Digital Samsat as a Public E-Government Service Using the PIECES Framework Fitria, Rahma; Syakhila, Amanda; Yulisda, Desvina; Hussain, Azham; Febriandirza, Arafat
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.15672

Abstract

The SIGNAL application is a digital service from the Korlantas Polri that makes it easier for people to take care of STNK validation online. However, some users still experience problems such as the verification process that is not always successful, slow delivery of physical documents, less rapid customer service response, and slow verification or login process. Therefore, this study aims to evaluate the performance of the SIGNAL application with the PIECES framework approach which includes six aspects: Performance, Information, Economic, Control, Efficiency, and Service. The data collection method was carried out through distributing questionnaires to 300 respondents who use the SIGNAL application. In addition, technical performance testing was also carried out using Apptim tools to measure application technical metrics. The results showed that overall, users were satisfied with the system based on the six aspects of PIECES with an average score of 3.9 on a scale of 5.76% of respondents stated that they were satisfied to very satisfied, 15% were undecided/neutral, and 9% were dissatisfied. This finding indicates that the majority of users consider this application to be quite effective and worth using. Performance testing using Apptim resulted in an average response time of 2.4 seconds, CPU usage of 18%, memory usage of 170MB, and no errors (error rate 0%), indicating that the application is quite stable and runs well on user devices. It is hoped that this research can be the basis for further development of the SIGNAL application, especially in improving service aspects and overall system efficiency.
Prediction of Dengue Fever Cases Using the Linear Regression Method Based on Open Data from West Java Province Firdaus, Muhammad Khysam; Yuliansyah, Herman
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16143

Abstract

Dengue Hemorrhagic Fever (DHF) is a widespread disease in tropical regions, including Indonesia. West Java Province reports the highest number of cases, influenced by factors such as rainfall, population density, and total population. Accurate prediction of DHF cases is essential for effective prevention and control strategies. This study aims to propose a predictive model for DHF cases in West Java using the Linear Regression method and to evaluate its performance using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The research utilizes secondary data from 2014 to 2023 on DHF cases, population density, and total population from the Open Data Jabar platform. Rainfall data were collected from Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) and Badan Pusat Statistik Indonesia (BPS). The research process includes data collection, preprocessing, time series splitting, model training and iteration, prediction, and performance evaluation. The results show that among the five focus regions, Bandung City achieved the best prediction performance, with a MAPE of 45.82% and an RMSE of 1216.105. These findings indicate that Multiple Linear Regression is reasonably effective for predicting DHF cases, particularly in Bandung. Despite limitations in data availability especially rainfall data the model provides informative insights. Future work could improve prediction accuracy by incorporating additional independent variables and more advanced modeling techniques, such as machine learning.
Comparison Analysis of Equivalence Class Partitioning and Boundary Value Analysis Techniques in Software Quality Testing of ReservasiPolnep Application Alifiansyah, Zuhrie; Alkadri, Syarifah Putri Agustini; Insani, Rachmat Wahid Saleh
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16789

Abstract

Software testing is a crucial phase before the official launch of an application to ensure its functionality and quality. This study compares two black box testing techniques—Equivalence Class Partitioning (ECP) and Boundary Value Analysis (BVA)—in identifying functional defects in the ReservasiPolnep application. The study involved testing key application features using both techniques, and results were measured using standard software testing metrics: test case coverage, success rates, test time, and cost per defect. The results showed that ECP is more time and cost-efficient, requiring only 26 test cases and 15 minutes 27 seconds per test, with a cost of Rp30 per defect and an 84.6% success rate. In contrast, BVA covers more test scenarios with 36 test cases, taking 27 minutes 5 seconds and costing Rp40 per defect, with a slightly higher success rate of 86.1%. The study concludes that each technique has advantages depending on the context, and highlights the need for input validation improvements in the application.

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