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Jurnal Sistem Informasi dan Informatika (SIMIKA)
Published by Universitas Banten Jaya
ISSN : 26226901     EISSN : 26226375     DOI : 10.47080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Jurnal Sistem Informasi dan Informatika aims to provide scientific literature specifically on studies of applied research in information systems (IS), information technology (IT) and public review of the development of theory, method, and applied sciences related to the subject.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 181 Documents
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SISTEM PENDETEKSI KESEHATAN MENTAL REMAJA MENGGUNAKAN METODE FORWARD CHAINING DAN NAIVE BAYES Ida Ayu Gde Suwiprabayanti Putra; Ni Luh Putu Trisnawati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3901

Abstract

A very important first step in helping people with mental health disorders get medical care is detection. Mental health problems and drug abuse can be detected with a tool called SDQ (for adolescents aged 10 to 17 years). The twenty-five statements in the SDQ fall into five measurable behavioral categories: (1) emotional symptoms (5 statements), (2) behavioral problems (5 statements), (3) hyperactivity (5 statements), (4) friendship problems (5 statements), and (5) prosocial behavior (5 statements). By using SDQ, this research will create an expert system, a computer-based application, to detect adolescent mental health. Expert systems can be used to solve problems in ways thought by experts. This research will build a web-based expert system that uses the PHP programming language. System and accuracy testing will be carried out using black box testing and accuracy value testing to find out whether the symptoms and diagnosis results are appropriate. The research results in the form of a prototype will be available online so that teenagers can check their mental health freely.
AI-BASED APPLICATION FOR INDONESIAN SIGN LANGUAGE DETECTION USING YOLOV8 Devanna Alandra Khansa; Ida Nurhaida
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3847

Abstract

Sign language is used by individuals with disabilities, particularly the deaf and those with speech impairments, as their primary means of communication. However, interaction between people with disabilities and the general public is often hampered by a lack of understanding of sign language. This study aims to develop an artificial intelligence-based application capable of detecting and classifying hand movements in Indonesian Sign Language (BISINDO) using the YOLOv8 algorithm. The YOLOv8 algorithm was chosen for its ability to detect and classify objects in real-time with high accuracy, even under varying lighting and background conditions. This is one of the first studies to implement YOLOv8 for real-time BISINDO detection integrated with a web interface. The dataset used includes 51 classes of hand movements with a total of 10,822 images that have undergone augmentation to increase data diversity. The development process involved data collection, pre-processing, annotation, model training, and integration with an interactive web interface. The resulting model demonstrated high performance, achieving mAP@50 of 96%, mAP@50-95 of 70%, and classification accuracy of 93.8% in the final evaluation. This application is intended to help the deaf community communicate more easily with the wider community. It can improve communication accessibility for individuals with hearing impairments in public and educational settings, as well as provide an innovative solution to support social inclusivity. Further testing and parameter optimization will be conducted to expand the detection coverage and improve the system's performance in the future.
AI-BASED FACIAL DE-IDENTIFICATION FOR CHILDREN'S DIGITAL PRIVACY Komang Putra Satria Negara; Ida Nurhaida
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3848

Abstract

Social media has become an inseparable part of daily life in today's digital era. Many parents frequently share photos of their children online, exposing them to risks related to privacy and security. This research addresses such issues by developing an Android-based facial de-identification application that utilises the YOLOv8 algorithm to protect minors' privacy. The methodology involves several stages: data collection, pre-processing, model training, and application development. The dataset includes over 2,889 images of children, which were augmented to enhance its size and diversity. YOLOv8, a state-of-the-art object detection algorithm, was trained with these images to achieve high precision and recall in identifying children's faces. The developed application integrates the YOLOv8 model within a user-friendly interface built with Flutter. Results indicate that YOLOv8 effectively detects children's faces with a high precision of 95%, accuracy of 88%, recall of 92%, and mAP50 of 0.977. While the model demonstrates strong performance on training data, there is room for improvement on unseen data. By leveraging YOLOv8 and providing an accessible mobile application, the work allows parents to protect their children's identities online. The application mitigates risks of unauthorised use and exploitation of children's images by enabling facial de-identification, thus promoting safer online practices for families.
FACE DETECTION AND ANTI-SPOOFING ON DESKTOP APPLICATIONS USING YOU ONLY LOOK ONCE Fairo Mahaputranda Faisal; Ida Nurhaida
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3849

Abstract

In the digital era, facial recognition systems have become increasingly vulnerable to spoofing attacks, as demonstrated by cases of identity theft using photos or smartphone screens. This study develops a real-time face liveness detection system using YOLOv8 to address these vulnerabilities. Under controlled laboratory conditions, the system achieved exceptional performance metrics: accuracy of 1.0, precision of 1.0, and recall of 1.0, with a mean Average Precision (mAP) of 0.96. However, this study reveals critical insights about the challenges of real-world deployment, including significant performance degradation under poor lighting conditions where genuine faces were misclassified as spoofed images. Compared to existing methods such as Attention-Based Two-Stream CNN (accuracy: 0.91) and Deep Spatial Gradient approaches (accuracy: 0.90-0.92), our system demonstrates superior performance in controlled environments but highlights the persistent challenge of environmental variability in practical applications. These findings emphasize the need for robust preprocessing techniques and diverse training datasets to bridge the gap between laboratory performance and real-world reliability. The study contributes to understanding the limitations of current face anti-spoofing technologies and provides a foundation for developing more robust systems suitable for practical deployment.
PENGGUNAAN COBIT SEBAGAI FRAMEWORK MANAJEMEN RISIKO TI DI SEKTOR ASURANSI : PENDEKATAN SYSTEMATIC LITERATURE REVIEW Annisa Ramadhanti; Nurbojatmiko
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3859

Abstract

This study aims to examine the impact of implementing the COBIT (Control Objectives for Information and Related Technology) framework on IT governance in various organizations, based on a review of relevant literature. COBIT is recognized as a comprehensive framework for effective IT governance and management. Based on the analysis of several recent scholarly publications, it is evident that the implementation of COBIT consistently leads to positive outcomes, including improved operational efficiency, enhanced IT risk control, better regulatory compliance, and higher quality of IT services. Additionally, COBIT supports organizations in identifying performance gaps and aligning IT strategies with business goals. These findings reinforce the core idea of this research—that COBIT serves as a strategic and effective tool for structured and sustainable IT governance. Therefore, the adoption of COBIT not only enhances a company’s IT capabilities but also provides added value in managerial decision-making through risk-based control mechanisms.
SENTIMENT ANALYSIS THE DAMAGE ESAF FRAME WITH SUPPORT VECTOR MACHINE AND IMPACT ON HONDA MOTORCYCLE SALES Kinanthi Putri Ariyani; Aviolla Terza Damaliana; Trimono Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3921

Abstract

Damage to the Enhanced Smart Architecture Frame (eSAF) on Honda motorcycles has triggered consumer concerns and has become a public spotlight. This study analyzes public sentiment towards the problem using the Support Vector Machine (SVM) and its impact on sales at one of the dealerships in Surabaya. The data used was in the form of comments from Twitter social media which were classified into two classes, namely positive and negative. Based on the results of the analysis, the majority of 589 public sentiments (59.7%) tended to be negative towards the problem of damage to the eSAF frame, while 397 public sentiments (40.3%) showed positive sentiment. Sales results showed significant fluctuations after this issue emerged, along with increasing negative sentiment. SVM models with a Linear kernel provide the best results with 85% accuracy, 84% precision, 85% recall, and 85% f1-score. SVM was chosen because it excels in text classification compared to algorithms such as K-Nearest Neighbors (KNN), C4.5, and Naïve Bayes, and has been applied in areas such as face detection, bioinformatics, and text processing. This research provides insights for manufacturers to improve product quality, improve customer service, and restore public trust. In addition, the use of the Support Vector Machine algorithm in sentiment analysis can be a reference for similar research in other fields.
LONGITUDINAL MODELING OF E-COMMERCE CHOICE USING LATENT GROWTH CURVE TO ASSESS INFLUENCING FACTORS AMONG LATE ADOLESCENTS Fadlila Agustina; Dwi Arman Prasetya; Aviolla Terza Damaliana
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3923

Abstract

The rapid growth of e-commerce in Indonesia has significantly influenced consumer behavior, particularly among late adolescents aged 18–21 years. This study examines the dynamic factors affecting e-commerce preferences, including price, service quality, and customer loyalty, using Latent Growth Curve Modeling (LGCM). This method was chosen for its ability to analyze variable changes longitudinally, allowing the identification of growth patterns and factors influencing shifts in consumer behavior over time. Data were collected through an online survey involving 400 respondents over three time periods. The study’s findings reveal that price is the most stable variable (intercept 0.5302, slope 0.0811), whereas service quality (intercept 0.8127, slope -0.0285) and loyalty (intercept 0.8508, slope -0.0188) show slight declines. Innovation, functioning as a covariate, significantly affects the intercept of all variables, particularly loyalty, although its impact on growth rates varies. The model demonstrates a good fit, with RMSEA (0.0730), CFI (0.9844), and TLI (0.9402), confirming its validity. Visualizations indicate that loyalty evolves more dynamically than service quality, highlighting the crucial role of innovation in customer engagement. This study emphasizes the need for e-commerce platforms to prioritize innovation and service quality improvements to foster long-term loyalty. These findings provide valuable insights into consumer behavior dynamics and offer strategic recommendations for achieving competitive advantage in the digital marketplace.
ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION Aulia Nur Fitriani; Kartika Maulida Hindrayani; Trimono Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3930

Abstract

The distribution of social assistance in Indonesia often experiences problems due to inaccuracies in recipient data between those recorded in government systems and field conditions. In Kalipuro Village, Mojokerto District, data mismatches caused difficulties in screening assistance, requiring village officials to manually re-filter the data. This triggered protests from citizens who should have received assistance but did not get their rights. To overcome this problem, this research proposes the use of the K-Medoids algorithm which is able to overcome sensitivity to outliers. This algorithm is used to cluster data based on criteria such as occupation, number of assets, number of dependents, and income. In addition, this research incorporates the Particle Swarm Optimization (PSO) technique to optimise the clustering process, which is expected to improve accuracy and efficiency in social assistance distribution. The results of clustering analysis using the K-Medoids algorithm show that the best cluster is obtained at the number of clusters K=5, with the distribution of cluster 0 (179 households), cluster 1(89 households), cluster 2 (296 households), cluster 3 (354 households), and cluster 4 (94 households). The Silhouette Score value of 0.6531 indicates good cohesion and separation between clusters. Based on the analysis, cluster 1 is the top priority group of aid recipients, followed by clusters 4, 2, 3, and 0. The K-Medoids algorithm effectively identifies the most needy community groups, supporting targeted and efficient decisions in aid distribution.
IDENTIFIKASI KELAINAN JANTUNG DARI DATA EKG MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK Sumiati Sumiati; Haris Triono Sigit; Wahyudin Nor Achmad; Idris Kusuma
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3937

Abstract

This study is one of the initial approaches in implementing Backpropagation Neural Network for ECG signal classification. The condition of the human heart can be known based on the results of electrocardiogram medical records, so that with the results of electrocardiogram medical records it can be known whether the heart is normal or abnormal. Symptoms of abnormal heart disease in the heart often come suddenly. Early recognition of heart disease with further procedures and treatment can prevent an increase in the risk of fatal heart attacks. This study has a very important goal in an effort to detect and classify heart abnormalities more efficiently. By utilizing artificial neural networks (ANN) and backpropagation methods, it can utilize computing capabilities to analyze patterns in electrocardiogram (ECG) data. The results show that the classification of heart abnormalities with an epoch value of 2000, a learning rate of 0.01 with normal and abnormal targets, obtained the number of Hidden Neurons as many as 25, the number of weight patterns 44 and a mean squared error (MSE) value of with an accuracy of 0.61364 from 25 inputs.
IMPLEMENTATION OF KERNEL COMBINATION GAUSSIAN PROCESS REGRESSOR IN LOYALTY PREDICTION (CASE STUDY: ONLINE MOTORCYCLE TAXI) Luqna Aziziyah; Dwi Arman Prasetya; Trimono Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3945

Abstract

In the application-based transportation industry, customer loyalty is a crucial factor affecting service sustainability. This study aims to analyze and predict customer loyalty in online motorcycle taxi services in Surabaya using the Gaussian Process Regressor (GPR) with a kernel combination approach. Data were collected through a survey of 467 students from public universities in Surabaya, considering service quality, price, and innovation factors. The analysis process includes data processing, validation, cleaning, and modeling using Gaussian Process Regression techniques. The results indicate that the kernel combination in GPR effectively captures complex non-linear patterns in survey data, with low Root Mean Squared Error (RMSE) and R² values close to 1. These findings suggest that the proposed approach can provide accurate customer loyalty predictions. This study contributes to developing strategies for online motorcycle taxi service providers to enhance user experience and maintain market share. The findings highlight the importance of applying machine learning models to understand customer behavior and support data-driven business decision-making.

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