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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Detection of Misoriented Polarized Electronic Components on PCBs Using HOG Features and Neural Networks Jamzuri, Eko Rudiawan; Ikhsan, Habyb Nur
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11330

Abstract

Mounting misorientation on polar electronic components in printed circuit boards (PCBs) can cause malfunctions in electronic devices. This study proposes an automatic detection system that utilizes the Histogram of Oriented Gradients (HOG) feature and employs classification using an artificial neural network. The research was conducted by collecting data from PCB images featuring polar components, such as diodes, electrolytic capacitors, and transistors. Once the components are identified, the HOG features are extracted to generate feature vectors used in artificial neural network training. The experiment results show that this system can detect component orientation errors with a high degree of accuracy, achieving accuracy values of 99.5% for transistor components, 97% for electrolyte capacitors, and 93.6% for diodes. Additionally, F1 values and high precision are achieved for all three types of components. The ReLU activation function has been shown to perform best among other activation functions. While the results are promising, further research is necessary to automate the identification of component locations without relying on manual cropping processes.
Boosting CNN Accuracy for Sundanese Script Recognition through Feature Extraction Techniques Pradana, Musthofa Galih; Khoirunnisa, Hilda
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11332

Abstract

Sundanese script is included in the cultural heritage in Indonesia, especially the culture in West Java. As a society that appreciates and preserves Indonesian culture and art, active participation can be realized through efforts to strengthen and preserve this script, one of which is by utilizing digital media. One of the technology-based digital media that can be used to preserve culture is image detection to make it easier to recognize Sundanese script. One of the models that can be used is the Convolutional Neural Network (CNN) with the MobileNetV2 architecture, with limited resources this architecture is able to produce good detection. This study applies the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture which will be tested with two main test scenarios, namely by applying feature extraction and without using feature extraction. The focus of this study will explore the influence and significance of the influence of feature extraction on the final results of image detection using the Convolutional Neural Network (CNN). The two feature extraction models used are Local Binary Pattern and Gray-Level Co-occurrence Matrix. These two feature extraction models will be tested with Sundanese script image data with data of 2,300 Sundanese script images. The results of this study show that the best results were obtained in the Convolutional Neural Network (CNN) with Gray-Level Co-occurrence Matrix (GLCM) with the best accuracy results at 93.8%. This is because the addition of the Gray-Level Co-occurrence Matrix (GLCM) is able to capture spatial texture statistics such as contrast, homogeneity, entropy, and correlation between pixel pairs. With these results, it can be concluded that in this study feature extraction has an effect and is able to increase the detection accuracy of the Convolutional Neural Network (CNN) model with the MobileNetV2 architecture in Sundanese script image data.
Frontend Implementation on EngVenture Application at IntSys Research Lab Nurhana Rifki, Slamet Ikhvan; Gamayanto, Indra; Wibowo, Sasono; Sirwenda, Alfian Bisma Daniswara; Ismanto, Ivan; Kurniawan, Michael Christ
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11337

Abstract

In today's digital era, the use of mobile applications for English learning is increasingly popular as an alternative to self-study. However, many available applications still lack the ability to provide an interactive, adaptive, and enjoyable learning experience, and do not provide integrated proficiency measurement features such as the TOEFL test. This research focuses on the frontend implementation of the EngVenture application, an English learning platform developed at IntSys Research Lab using the Rapid Application Development (RAD) method. This application is designed to address these issues by integrating gamification elements and a TOEFL-like practice test system to increase engagement and measure user progress. Data were collected through literature studies and questionnaires distributed to 100 respondents from various educational levels. The results showed that 82% of respondents needed a fun learning medium, 92% wanted a TOEFL test feature, and 88% were interested in the gamification feature. The application was developed using Flutter and Dart, with a responsive UI/UX design and real-time feedback features. System testing was conducted using two methods: black-box User Acceptance Testing (UAT) to assess functionality, and a System Usability Scale (SUS) to measure the application's usability. Test results showed that all features functioned well, with an average SUS score of 84.25, which falls into the Acceptable (Grade B+, Excellent) category. These results demonstrate that EngVenture meets user needs in terms of functionality and usability, and has the potential to become an interactive and effective English language learning tool.
Design and Implementation of a Backend System and DevOps Workflow for Interactive Learning Applications Daniswara Sirwenda, Alfian Bisma; Wibowo, Sasono; Gamayanto, Indra; Rifki, Slamet Ikhvan Nurhana; Ismanto, Ivan; Kurniawan, Michael Christ
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11338

Abstract

English language learning in Indonesia faces significant challenges, including limited vocabulary retention, poor pronunciation, and passive learning methods. The EngVenture application was developed to address these issues by integrating gamification principles with interactive English learning environments. This study aims to design and implement a backend system and DevOps workflow that ensure optimal performance, security, and stability for gamification-based learning applications. The Rapid Application Development (RAD) method was employed, comprising requirements planning, user design, construction, and cutover phases. System requirements were identified through a validated questionnaire (Cronbach's α = 0.89) distributed to 101 respondents from diverse backgrounds. Results indicated that users prioritized data security (90.1%), system speed (91.1%), and secure authentication (69.3%) as critical factors. Based on these findings, a RESTful API-based backend was designed and integrated with Docker, Jenkins, and Nginx, incorporating security features such as JWT authentication, API key validation, and SSL/TLS encryption. Quantitative evaluation over a 20-day period demonstrated significant improvements: 85% faster deployment time (6.23→1.48 minutes), 43.4% reduction in error rate (211→138 errors), 95.7% build success rate, stable API response time (~160ms) under load testing with 1,000 concurrent requests, and near-zero downtime (<5 minutes). This research demonstrates that the integration of structured backend architecture and automated DevOps practices significantly enhances system reliability, deployment efficiency, and user satisfaction in educational technology applications such as EngVenture.
Efficient Feature Extraction Using MobileNetV2 and EfficientNetB0 for Multi-Class Brain Tumor Classification Amelia, Hemas Anggita; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11354

Abstract

Brain tumor classification in MRI is complicated by the similarity of imaging features across multiple tumor classes.  This study evaluates the use of lightweight convolutional neural network (CNN) architectures as feature extractors combined with machine learning classifiers for multi-class classification. MobileNetV2 and EfficientNetB0 were used to extract fixed-length feature representations, which were then classified using Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbors. The evaluation used stratified five-fold cross-validation, and performance was measured with accuracy, F1-score, and Matthews Correlation Coefficient (MCC). Results show that EfficientNetB0 features paired with SVM achieved the highest test accuracy (98.5%), while Logistic Regression also yielded competitive performance (97.1%). Class-wise analysis indicated strong results for pituitary and non-tumor cases. This work shows that lightweight CNN-based feature extraction may serve as a practical direction for improving multi-class brain tumor MRI classification, with potential benefits for applications in resource-limited environments.
Benchmarking Deepseek-LLM-7B-Chat and Qwen1.5-7B-Chat for Indonesian Product Review Emotion Classification Nurohim, Galih Setiawan; Setyadi, Heribertus Ary; Fauzi, Ahmad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11369

Abstract

Upon completing their shopping experience on an e-commerce platform, users have the opportunity to leave a review. By analyzing reviews, businesses can gain insight into customer emotions, while researchers and policymakers can monitor social dynamics. Large Language Models (LLMs) utilization is identified as a promising methodology for emotion analysis. LLMs have revolutionized natural language processing capabilities, yet their performance in non-English languages, such as Indonesian, necessitates a comprehensive evaluation. This research objective is to perform a comprehensive analysis and comparison of Deepseek-LLM-7B-Chat and Qwen1.5-7B-Chat, two prominent open-source Large Language Models, for the emotion classification of Indonesian product reviews. By leveraging the PRDECT-ID dataset, this study evaluates the performance of both models in a few-shot learning scenario through prompt engineering. The methodology outlines the data preprocessing pipeline, a detailed few-shot prompt engineering strategy tailored to each model's characteristics, model inference execution, and performance assessment using the accuracy, precision, recall, and F1-score metrics. Analytical results reveal DeepSeek achieved an accuracy of 43.41%, exhibiting a considerably superior ability to comprehend instructions compared to Qwen, which attained a maximum accuracy of only 20.35% and often yielded near-random predictions. An in-depth error analysis indicates that this performance gap is likely attributable to factors such as pre-training data bias and tokenization mismatches with the Indonesian language. This research offers empirical evidence regarding the comparative strengths and weaknesses of DeepSeek and Qwen, providing a diagnostic benchmark that underscores the significance of instruction tuning and robust multilingual representation for Indonesian NLP tasks.
User Requirement Recommendation Model for Waste Reporting Platforms Based on UX Topics and Sentiment Analysis Dwijayanti, Irmma; Lahitani, Alfirna Rizqi; Habibi, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11371

Abstract

Waste management remains a critical issue in Indonesia, as emphasized in the RPJMN 2025–2029. Ineffective collection and processing services, coupled with limited public participation, continue to hinder progress. Meanwhile, social media has emerged as a primary channel for citizens to express complaints and reports on waste, yet the unstructured nature of comments poses challenges for integration into official reporting systems. This study proposes a user requirements recommendation model based on social media data by integrating sentiment analysis, topic modeling, and rule-based recommendation. Data were collected from YouTube and TikTok comments. Sentiment classification was performed using Support Vector Machine (SVM), while Latent Dirichlet Allocation (LDA) was employed for topic modeling, with results mapped onto the UX Honeycomb dimensions. Recommendation rules were then formulated by combining sentiment polarity with UX dimensions. The SVM model achieved an average accuracy of 87.5% with balanced precision, recall, and F1-score. LDA produced 15 coherent topics, which were distributed across seven UX dimensions. The integration revealed that the main user requirements include transparency in report follow-up through real-time notifications and clear status updates. Additional recommendations involve simplifying the reporting process, providing auto-fill features, improving visual design, and establishing a user appreciation system. The findings demonstrate the potential of leveraging social media comments to systematically capture user requirements, offering practical insights for developers to design waste reporting platforms that are effective, user-friendly, and responsive to community expectations.
Comparative Performance of SVM and BERT-Base Using Hybrid Preprocessing for Fast Fashion Sentiment Analysis Mulianingrum, Restu Lestari; Hidayat , Erwin Yudi
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11385

Abstract

Fast fashion poses major environmental and social challenges, yet public awareness in Indonesia remains insufficiently understood. This study compares Support Vector Machine and BERT-Base for sentiment analysis of 3,513 TikTok comments on fast fashion sustainability using a hybrid preprocessing pipeline that incorporates a 404-entry slang dictionary and IndoNLP utilities to address informal language, code-mixing, and character elongation. Sentiment labels generated using VADER were validated against 1,747 manually annotated samples, achieving Cohen's Kappa of 0.7155, indicating substantial agreement. BERT-Base achieves 92.7% accuracy with F1-scores of 0.86, 0.94, and 0.93 for negative, neutral, and positive classes, while SVM attains competitive 90.4% accuracy with F1-scores of 0.84, 0.93, and 0.91. BERT demonstrates superior negative sentiment detection with recall of 0.87 compared to SVM at 0.82, critical for identifying sustainability concerns. Computational analysis reveals significant trade-offs as BERT requires 230.2 seconds of GPU training and 3.449 seconds of inference, whereas SVM operates efficiently on CPU with 25.9 seconds of training and 0.051 seconds of inference, representing 8.9× and 67.6× efficiency advantages. The sentiment distribution comprising 46.9% neutral, 34.5% positive, and 18.6% negative comments indicates limited critical awareness among Indonesian users. These findings demonstrate that systematic preprocessing bridges the performance gap between classical and transformer models while enabling deployment decisions based on resource constraints, providing methodological insights for low-resource informal text analysis and practical guidance for scalable social listening, greenwashing detection, and evidence-based sustainability communication strategies.
Comparison of Sarima and Exponential Smoothing Methods in Forecasting Exchange Rates for Farmers in Central Java Province Sulistyono, MY Teguh; Annabil, Muhammad Naufal
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11396

Abstract

This study compares the performance of the SARIMA and Exponential Smoothing (Holt-Winters) models in forecasting the Farmer Exchange Rate (NTP) for Central Java Province from 2016 to 2025. The monthly statistical data used was obtained from the Central Java Provincial Statistics Agency. The models were evaluated using MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) on test data for the period January 2016 to September 2025, while forecasting was carried out from October 2025 to December 2027. The results show that the SARIMA (1,1,1) (1,1,1,12) model has an MAE of 6.94 and an RMSE of 7.88, indicating that the model can make accurate predictions with few errors. However, the Exponential Smoothing model has a lower MAE and RMSE, implying that this model is more accurate in forecasting long-term NTP. Both models show comparable seasonal trends, with Exponential Smoothing being more stable and sensitive to seasonal changes.  This study also proposes the use of alternative forecasting approaches, such as ARIMAX, VAR, or machine learning to improve the accuracy of future forecasts.  The results of this study can be used to develop agricultural policies that maintain food price stability, improve farmer welfare, and predict future inflation fluctuations.
Comparison of Multiple Linear Regression and Random Forest Methods for Predicting National Rice Production in Indonesia Nur Cahyo, Sefrico Aji; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11398

Abstract

Rice is a strategic commodity that plays an important role in maintaining national food security. However, rice production in Indonesia still fluctuates due to variations in harvest area, productivity, climate conditions, and differences in regional characteristics. This condition demands a predictive model capable of providing more accurate production estimates to support food policy planning. This research aims to predict national rice production by comparing two methods: Multiple Linear Regression and Random Forest Regression, using data from the Central Bureau of Statistics (BPS) and Nasa Power for the period 2018–2024. The analysis stages include data preprocessing, data exploration, categorical variable transformation, splitting data into training and testing sets, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The research results show that harvested area is the most dominant factor influencing rice production, followed by productivity, year, and province. Based on the evaluation results, Random Forest provided the best performance with an MAE value of 40,599.94, an RMSE of 77,153.07, and an R² of 0.9991. The low error value and the proximity of the prediction to the actual data indicate that this model is better at capturing non-linear patterns and inter-regional variations compared to Multiple Linear Regression. Overall, Random Forest can be an effective method for predicting national rice production and can be further developed in subsequent research by incorporating climate variables or other external factors.

Page 64 of 70 | Total Record : 695

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