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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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Stabilization of Image Classification Accuracy in Hybrid Quantum-Classical Convolutional Neural Network with Ensemble Learning Oumarou, Hayatou; Siradj, Yahdi; Rizal, Randi; Candra, Fikri
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10437

Abstract

Stabilization of Image Classification Accuracy in Hybrid Quantum-Classical Convolutional Neural Network Model with Ensemble Learning. Image classification plays a significant role in various technological applications, such as object recognition, autonomous vehicles, and medical image processing. Higher accuracy in image classification implies better capabilities in recognizing and understanding visual information. To enhance image classification accuracy, a Hybrid Quantum-Classical Convolutional Neural Network (HQ-CNN) model is developed by integrating quantum and classical computing elements with ensemble learning techniques. Compared to conventional neural networks, HQ-CNN enriches feature mapping in image classification predictions. The research results with HQ-CNN using ensemble learning demonstrate impressive and stable accuracy, with the lowest deviation being 1.1037.
Natural Language Processing for Unstructured Data: Earthquakes Spatial Analysis in Indonesia Using Platform Social Media Twitter Nursiyono, Joko Ade; Gibran, Rasya Khalil
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 1 (2023): Maret 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i1.6678

Abstract

As a country who had a high risk affected by the earthquake, social media have an important role. Besides to serving earthquake information, the spread of information on social media is so wide and fast. However, information on social media has a gap to reach validity and doesn't contain detailed information about spatial information. By leveraging crawling result data on Twitter, then data will be processed with Natural Language Processing (NLP), this research aims to proves about transformation of unstructured data into structured data with NLP for use on spatial analysis in Indonesia using data text on platform social media, Twitter. In addition, this research is also aims to reveal correlation between earthquake magnitude and earthquake frequency. The results proves that NLP can be used for spatial analysis with data text on Twitter related to earthquake. Besides that, the value of maximum magnitude are great significance to the earthquake frequency.
Software Testing in the Indonesian Industry: Survey of Methods, Tools, and Documentation Maspupah, Asri; Rahmani, Ani; Min, Joe Lian; Roshinta, Trisna Ari
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12636

Abstract

Software testing plays a crucial role in the software development by ensuring that software is accurate and of high quality. Many software companies neglect software testing, which can lead to unprofitable business outcomes. For example, ineffective software testing may fail to identify all defects, resulting in increased development costs. A key factor determining the success of software testing is the strategy for implementing the testing process, the selection of testing tools, and the documentation of testing activities. This article examines the current state of software testing processes in the Indonesian software industry. The research objective is to analyze the software testing implementation strategy within the software development context, focusing on three main areas: software testing methodology, software testing tools, and software testing documentation. The research employs a survey method, collecting data from several respondents, Indonesian software companies, via an online questionnaire. The research findings indicate that testing is still predominantly manual. However, some software companies have begun to adopt a combination of manual and automated testing. Most companies utilize software testing documentation for reporting purposes during the execution of tests. Nevertheless, documenting test cases as a guide for testing execution is not prioritized as highly as bug reporting. Conversely, many Indonesian software companies have adopted testing tools and conducted performance testing to ensure software quality. Consequently, the software testing process in the Indonesian software industry tends to adhere to formal methods in accordance with the ISO/IEC/IEEE 29119 software testing standards
Analysis of Image Improvement and Edge Identification Methods in Watermelon Image Sudiarjo, Aso; Praseptiawan, Mugi; Setyoningrum, Nuk Ghurroh; Drajat, Hilmi Maulana; Natsir, Fauzan
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10699

Abstract

The initial stage in digital image processing, known as pre-processing, plays a vital role in enhancing image quality. This essential step involves employing various techniques to prepare the image for subsequent analysis and feature extraction. Among the array of pre-processing methodologies utilized, thresholding, median averaging, median filtering, rapid Fourier transform, point operations, intensity modification, and histogram equalization stand out as prominent tools. These techniques are employed to mitigate noise, enhance contrast, and optimize the overall visual quality of the image. Once the pre-processing phase is complete, the focus often shifts to specific tasks, such as identifying objects or features within the image. In the context of analyzing watermelon images, one such task is the detection of watermelon seeds. To accomplish this, the pre-processed image undergoes further refinement through the application of edge detection techniques. Gradient edge detection, isotropic, Canny, and Sobel edge detection are among the methods commonly employed for this purpose. These techniques aim to highlight the edges and contours of objects within the image, facilitating the identification of distinct features such as watermelon seeds. However, our investigation reveals that not all edge detection methods are equally effective in this context. By employing a combination of pre-processing techniques and judiciously selecting edge detection methods, researchers can enhance the accuracy and reliability of their image processing workflows, ultimately advancing our understanding of complex biological structures such as watermelon seeds.
Classification Of The Maturity Level Of Glutinous Rice Tape Fermentation Using Convolutional Neural Network Yunianti, Rizqi; Murinto, Murinto
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.11442

Abstract

Stiky tape is a popular snack in Indonesia made from fermented ketan rice. One of the main benefits of eating white cheddar rice is to trigger the digestive system. Excessive consumption can result in a decrease in sweetness and inappropriate texture. Therefore, it is necessary to classify the maturity level of the tape, so that there is no excessive maturity that results in adverse effects on the body and the quality of the tapes.The study aims to test the accuracy of the white tape maturity classification program as well as design and implement a classification system using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The white tape image data set was obtained with the iPhone X camera in jpg format, covering three maturity classes: raw, ripe, and rotten, each consisting of 400 images. The data set is divided into 768 training data, 192 validation data, and 240 test data, then processed through preprocessing stages including resize, augmentation, and rescale. The CNN model was implemented with the VGG16 architecture and tested on various Epochs, producing an accuracy of 0.98 on Epoches 20 and 30, and reaching 0.99 on the 40th. The results of the research showed that the CNN method with VGG-16 architecture was effective in classifying the maturity level of the tape, achieving high accuration and significant consistency as the number of Epochs increased. This implementation is expected to preserve the quality of the tapes and extend the application of modern technology in traditional industries.
Sentiment Analysis of Societal Attitudes Toward the Childfree Lifestyle Using Latent Dirichlet Allocation and Support Vector Machines Husen, Ratna Andini; Agustin, Agustin; Erlinda, Susi; Junadhi, Junadhi; Perumal, Thinagaran
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12005

Abstract

This research investigates societal perspectives on the childfree lifestyle through Intent Sentiment Analysis, combining Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) techniques. The childfree lifestyle, a deliberate decision by individuals or couples to remain childless, has spurred extensive public discourse, particularly on platforms like Twitter. This research aims to analyze sentiments and intentions within these discussions to uncover their implications for social dynamics and familial relationships. Using LDA, dominant topics were identified from a dataset of Twitter comments on the childfree topic. LDA uncovered hidden themes by modeling topics as mixtures of words, which were subsequently classified into positive, negative, and neutral sentiments using SVM. Data preprocessing included cleaning, tokenization, and stop word removal, while oversampling with SMOTE addressed class imbalances. The optimal number of topics was determined using coherence scores, with the highest coherence value of 0.400 achieved at one topic. The findings revealed that positive sentiments were classified more effectively than negative and neutral sentiments when using LDA and SVM with SMOTE. The top 10 topics primarily reflected societal commentary on the childfree lifestyle. Challenges included incomplete preprocessing, suboptimal clustering of similar themes, and imbalanced data, which limited the effectiveness of topic modeling and classification. Addressing these issues through improved feature selection, parameter optimization, and data augmentation could enhance performance for underrepresented categories. This research provides valuable insights into public attitudes toward the childfree lifestyle, offering implications for social research and policy development in the context of evolving societal norms.  
When Culture Meets Code: Enhancing E-Payment Technology Adoption Through QRIS in The Digital Transformation Sari, Danar Retno; Sorongan, Erick; Kusno, Hendra Sanjaya
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13153

Abstract

Bank Indonesia has implemented the National Non-Cash Movement (GNNT) to advance the payment system in Indonesia, focusing on safety and efficiency. The use of QRIS technology has accelerated transaction processes, reducing queues and increasing efficiency in various sectors. The electronic money component has seen significant growth, especially in non-bank institutions, with a rise in shopping transactions and the number of merchants. Trust in QRIS technology is high due to ease of use and secure transactions, supported by government regulations. This research investigates the factors influencing the adoption of QRIS (Quick Response Code Indonesian Standard) technology as an electronic payment system in Indonesia after the COVID-19 pandemic. The study focuses on the cultural dimensions of Power Distance (PD) and Uncertainty Avoidance (UA), and their influence on Trust and the Attitude to Use QRIS. A quantitative methodology using a questionnaire was employed, involving 103 active QRIS users, with analysis conducted using Structural Equation Modeling (SEM) via SmartPLS. Results show that Uncertainty Avoidance has a significant positive effect on Trust, which in turn significantly influences the Attitude to Use QRIS. However, Power Distance does not significantly impact Trust, suggesting that while government regulations are important, they do not directly enhance trust in the system. The findings highlight the importance of improving user confidence by reducing perceived risks and enhancing the security of the QRIS platform to foster wider adoption of cashless payments. Future research should explore the role of government policies in further detail to enhance user trust in digital payment systems.
A Clustering-Based Artificial Intelligence Approach for Minimizing of Ionizing Radiation Exposure in Uyo Metropolis Nigeria Umoren, Imeh; Inyang, Saviour Joshua; Etuk, Ubong E.; Essien, Daniel
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13024

Abstract

Electromagnetic Field (EMF) radio frequency exposure is a growing concern due to its impacts on public health and the environment. This study aims to develop a data-driven framework for clustering and analyzing long-term far-field EMF exposure in Uyo Metropolis, Nigeria, with a focus on identifying exposure patterns and assessing their implications. Data were measured at multiple locations using smart meter strategically deployed across three major roads in uyo metropolis to capture variations in exposure levels. The preprocessing steps involved data cleaning and normalization to enhance data quality and reliability for meaningful analysis.  Four clustering algorithms, namely, K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Model (GMM), were employed to analyze the distribution of radiation levels. The Silhouette score was used to evaluate the different clustering methods with respect to cohesion within clusters and separation from other clusters. The best results were obtained by Hierarchical Clustering and GMM, each achieving a mean Silhouette score of 0.81, indicating well-defined and highly contrasting clusters. K-Means performed moderately well, with an average Silhouette score of 0.73, while DBSCAN, due to its sensitivity to noise and parameter settings, achieved a lower score of 0.62. These findings highlight significant spatial variability in EMF exposure across different urban zones, emphasizing the need for targeted regulatory measures. The study underscores effectiveness of machine learning and offers a scalable approach for characterizing EMF exposure. Results reported offer scalable and data-driven framework for characterizing exposure patterns, with important implications for public health policies, urban planning strategies, and regulatory interventions.
Approximate Bayesian Inference for Bayesian Confidence Quantification in DNA Sequence Classification Using Monte Carlo Dropout Approach Alamsyah, Nur; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Danestiara, Venia Restreva
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.14349

Abstract

Splice junction classification in DNA sequences is critical for understanding genetic structures and processes, particularly the differentiation between exon-intron (EI), intron-exon (IE), and neither boundaries. Traditional neural network models achieve high accuracy but often lack the ability to quantify uncertainty, which is essential for reliability in sensitive applications such as bioinformatics. This study addresses this limitation by incorporating Bayesian confidence quantification into DNA sequence classification using the Monte Carlo Dropout (MCD) approach. A baseline neural network was first implemented as a reference, achieving a test accuracy of 95.61%. Subsequently, MCD was applied, which not only improved the test accuracy to 96.03% but also provided uncertainty estimation for each prediction by sampling multiple inferences. The uncertainty values enabled the identification of low-confidence predictions, enhancing the interpretability and reliability of the model. Experiments were conducted on a binary-encoded DNA sequence dataset, representing nucleotides (A, C, G, T) and their splice junctions. The results demonstrated that MCD is a robust approach for DNA sequence classification, offering both high predictive performance and actionable insights through uncertainty quantification. This research highlights the potential of Bayesian confidence quantification in genomic studies, particularly for tasks requiring high reliability and interpretability. The proposed approach bridges the gap between accurate predictions and the need for robust uncertainty estimation, contributing to advancements in bioinformatics and machine learning applications in genetic research.
The Impact of Linguistic Features on Emotion Detection in Social Media Texts Setyoningrum, Nuk Ghurroh; Febriani SM, Neng Nelis; Alam, Alam; Nurdin, Arif Muhamad; Nursamsi, Dede Rizal; Lodana, Mae B
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13221

Abstract

Emotions are an important aspect of human life, and scientific theories on emotions have been widely developed in various research fields such as philosophy, psychology, and neuroscience. In human-computer interaction, understanding emotions is also very important. Detecting emotions not only enables better decision-making, but is also useful in various contexts such as business, politics, and mental health. The focus on identifying emotions in text arises because emotions are often implied without explicit words. Through the analysis of grammar and sentence structure, text mining techniques enable the extraction of sentiments and emotions. Detecting and identifying emotions in text is important because it can be applied in a variety of fields, including decision-making, prediction of human emotions, product assessment, analysis of political support, and identification of depression. Text as textual data is an important source of information due to its ability to convey human emotions. In this research, emotion detection uses the Naïve Bayes method, with attribute weighting to improve accuracy using count vector. This classification approach allows grouping text into six emotion categories: happy, sad, fear, love, shock, and anger. The Naïve Bayes method was chosen for its reliability in classifying data based on conditional probabilities. Thus, this research provides a deeper understanding of understanding and managing emotions in the context of social media. The data classification results yield precision, recall, F1-Measure, and accuracy values.

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