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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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Expert System for Early Detection of Postpartum Complications Using Certainty Factor Method Nurhayati, Nurhayati; Pertiwi, Mumpuni Intan; Kholilurrohman, Maulana Rifky; Tamarussal, Naraya Kyesa
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.v9i6.11009

Abstract

Postpartum complications are one of the main causes of maternal mortality. The objective of this study was to design and build an expert system capable of early detection and providing consultation regarding health complications that occur during the postpartum period using the certainty factor method. The certainty factor approach is utilized to overcome the uncertainty that arises during the diagnosis process by combining the confidence values ​​of each symptom entered by the user. The research methods included needs identification, data collection, knowledge acquisition, knowledge base development, software design, software development, and testing. Needs identification generated 10 data sets, data collection was conducted through literature studies, and knowledge acquisition was obtained through interviews. The knowledge base was compiled based on information from experienced medical personnel. The software design included data flow, interface, and database. The software development resulted in a good early detection expert system. The expert system trial indicated superior performance in identifying health complications during the postpartum period with an accuracy rate of 91%. The ability to recognize positive cases reached 90.9%, and the error rate was 10%, indicating this system is reliable and accurate in decision-making.
Sentiment Analysis of Coretax on Social Media X Using Naive Bayes, SVM, and LSTM for Service Improvement Dermawan, Steven; Ayunda, Afifah Trista
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.v9i6.11063

Abstract

In January 2025, Indonesia’s Ministry of Finance launched Coretax to replace DJP Online. However, the launch triggered widespread dissatisfaction among users, reflecting negative public sentiment. This study aims to analyze public perception of Coretax and evaluate the performance of machine learning models in sentiment classification. A total of 6.036 Indonesian language tweets related to Coretax, posted between January and April 2025, were collected using Tweet Harvest. The dataset consists of 0,83% positive, 51,05% negative, and 48,11% neutral sentiments. The research methodology involved several stages: data crawling, manual labeling, preprocessing (cleaning, case folding, stopword removal, tokenization, normalization, stemming, and specifically for LSTM: conversion of tokens into numerical indices, padding, and embedding), feature representation using TF-IDF for classical models and word embedding for deep learning, data balancing with SMOTE, model implementation (Naive Bayes, Support Vector Machine with various kernels, and LSTM), model evaluation and comparison, and visualization through word clouds. The application of SMOTE succeeded in improving the performance of all algorithms. After applying SMOTE, the SVM with the RBF kernel achieved the best performance with 90,70% accuracy, 91% precision, 90,66% recall, and 90,66% F1-score. Keyword analysis revealed that terms such as “data” and “mudah” dominated positive sentiment, “silakan” and “kakak” were prevalent in neutral sentiment, while “sistem” and “error” frequently appeared in negative sentiment. The findings highlight the urgent need for system infrastructure improvements, user-centered features, responsive technical support, taxpayer training, and continuous updates to enhance Coretax and restore public trust.
Implementation of Braille-Mobile Device to Help Visually and Speech-Impaired Persons Communicate Based on the Blynk IoT Kamaruddin, Kamaruddin; Suparno, I Wayan; Jalil, Abdul; Fauzy, Ahmad; Serlina, Serlina
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.v9i6.11072

Abstract

Visual and speech impairment is a condition in which an individual is unable to communicate with family or society due to the inability to see and speak. The objective of this study is to develop a Braille-Mobile device that assists individuals with visual and speech disabilities in communicating remotely with family members or society using Internet of Things (IoT) technology. In this study, the method used to generate messages from the Braille-Mobile device is based on the combination of six buttons pressed on the device, which are translated into letters using the Braille code concept. The messages are then transmitted via the Blynk IoT platform from the Braille-Mobile device to the mobile devices of family members or society through the Internet network. The results of this study show that the developed Braille-Mobile device can be used to send messages in the form of the words HELP, EAT, DRINK, and DRUG to family smartphones using IoT technology with a success rate of up to 76.25% and a message transmission time ranging from 4 to 8 seconds. Furthermore, the Braille-Mobile device is also capable of receiving confirmation from family smartphones in the form of voice responses.
Sentiment Analysis on Google Play Store Reviews to Measure User Perception of the Gojek Application Using CNN Anissa, Cahya Rahmi; Tania, Ken Ditha; Sari, Winda Kurnia
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.v9i6.11084

Abstract

This study was conducted to analyze sentiment towards user reviews from the Google Play Store regarding the Gojek application. The analysis aims to measure user perceptions using a Convolutional Neural Network (CNN). This study aims to understand user views on the Gojek application. By understanding user perceptions, the information obtained can be utilized by the company's service team to improve the quality of the application for users. User perceptions are grouped into three labels: positive, neutral, and negative. To produce an effective model, this study uses three data sharing ratios simultaneously with the same parameters: 90:10, 80:20, and 70:30. Due to the large amount of data, random sampling is needed to balance the data and thus increase accuracy in the data processing process. Model evaluation was carried out using a confusion matrix, precision, recall, and F1-Score. The results obtained with the highest accuracy of 84.29%. This study successfully demonstrates that CNN is able to process user review data well.
Real-Time Arrow Detection and Scoring on Archery Targets Using YOLOv8 with Euclidean Distance-Based Zone Estimation Adam, Safri; Fitri, Novi Aryani; Bibi, Sarah; Sufandi, Muhammad Ridhwan
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.v9i6.11086

Abstract

The current study aims to create an automated scoring system for archery target board using computer vision technologies. As archery has develop from a traditional practice to a competitive activity, the scoring procedures have become a crucial element. While the current manual scoring procedures are fallible and can be challenging for organizers. This study offers a solution to this issue by using YOLO v8 (You Only Look Once) architecture for real- time arrow recognition and scoring. The development process consists of dataset collecting, picture pre-processing, model training and implementation using 2 photos of the target boards with arrows. The computer processes the scores by calculating the distance from the center of the arrow to the selected scoring zones using Euclidean distance. System testing established a baseline accuracy of 67%. While users noted the system's processing efficiency (speed), this accuracy level highlights significant room for improvement. The results demonstrate the potential for applying computer vision to automate the archery scoring system, while simultaneously emphasizing the critical need for advanced model performance enhancements. This study serves as a preliminary step in exploring automated sport technology, expected to contribute to future refinements of the archery scoring system.
Abstract Syntax Tree Model for Minimizing False Negative in Semantic Evaluation of Python Fill-in-the-Blank Nurhasan, Usman; Prasetya, Didik Dwi; Patmanthara, Syaad
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.v9i6.11090

Abstract

This study develops and evaluates an automated assessment model using Abstract Syntax Trees (AST) with a view to overcoming the limitations of string-matching techniques in the assessment of Fill-in-the-Blank (FIB) programming answers. Traditional string-matching techniques have a relatively high False Negative Rate (FNR) of 21.5% within the context of detecting semantic equivalence. The current model uses semantic structural triangulation to ascertain the semantic similarity of student answers. Technical assessment shows that the AST approach markedly reduces the FNR to 4.5%. The model demonstrates high reliability (ϰ = 0.83) with high classification accuracy (F1 Score = 0.966) which attests to its inferential validity. From a pedagogical perspective, system implementation leads to substantial learning gains, evidenced by a large effect size (Cohen’s d = 1.82) and a high normalized gain (Normalized Gain = 0.90). Multiple regression analysis confirms that semantic accuracy is the primary causal factor driving improved student comprehension. Ontologically, while AST is valid as a partial representation, its limitations—particularly tree isomorphism in recursive structures—highlight the need for further exploration of graph isomorphism approaches. Control Flow Graphs (CFG) and Data Flow Graphs (DFG) offer more expressive relational models for capturing control and data dependencies. The model demonstrates functional feasibility with a System Usability Scale (SUS) score of 76.47. Overall, the AST Triangulation Model is validated as pedagogically effective, inferentially robust, and supportive of evaluative transparency. Future research recommends validating the model on more complex tasks and releasing it as open-source to support reproducibility.
Hybrid PSO-XGBoost Model for Accurate Flood Risk Assessment Nabilah, Lailatun; Hakim, Lukman
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.v9i6.11094

Abstract

Flood risk prediction is a crucial step in disaster mitigation. This study optimizes the Extreme Gradient Boosting (XGBoost) algorithm using the Particle Swarm Optimization (PSO) method to improve prediction accuracy. The process includes data cleaning, normalization, and classification of risk levels into low, medium, and high. The XGBoost model is trained both before and after parameter optimization of n_estimators, max_depth, and learning_rate. Before optimization, the model achieved 93% accuracy but struggled to identify minority classes. After optimization with PSO, accuracy increased to 97%, with the recall for the low-risk class improving from 21% to 57%. The optimized model also demonstrated more stable performance compared to Support Vector Machine (SVM) and Random Forest. These findings indicate that the combination of XGBoost and PSO can provide more accurate and efficient flood risk predictions.
Proboboost: A Hybrid Model for Sentiment Analysis of Kitabisa Reviews Prasetya, Rakan Shafy; Fahmi, Amiq; 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.v9i6.11138

Abstract

The rapid advancement of digital technology has significantly transformed public behavior in social activities, particularly in online donations and zakat payments. The Kitabisa application was selected in this study not only for its popularity but also due to its high user engagement and large volume of reviews on the Google Play Store, making it an ideal representation of public trust in Indonesia’s digital philanthropy ecosystem. This research aims to analyze user sentiment toward the Kitabisa application using a hybrid Proboboost model, which combines Multinomial Naive Bayes (MNB) and Gradient Boosting Classifier through a soft voting mechanism. The model is designed to address class imbalance and improve accuracy in short-text sentiment analysis for the Indonesian language. The study employed preprocessing techniques including case folding, text cleaning, stopword removal, and stemming using the Sastrawi algorithm. Feature extraction was performed using TF-IDF, with an 80:20 train-test split and 5-fold cross-validation to ensure model reliability. Experimental results indicate that the Proboboost model achieved an accuracy of 89.51% and an F1-score of 87.4%, outperforming the Naive Bayes baseline with 87.98% accuracy. The sentiment distribution demonstrates a dominance of positive sentiment (87.24%), followed by negative (8.53%) and neutral (4.23%) reviews. These findings suggest that users generally express satisfaction and trust toward the Kitabisa platform. The results also confirm that the hybrid Proboboost model effectively balances classification performance between majority and minority sentiment classes, offering deeper insights into user perceptions of digital philanthropic services.
Improving Efficient Ship Detection Performance Using Contextual Transformers for Maritime Surveillance Wungow, Marsel Marhaen; Manoppo, Dayen; Mustikayani , Ni Made Shavitri; Putro, Muhammad Dwisnanto
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.v9i6.11189

Abstract

Ship surveillance plays a crucial role in enhancing defense systems in coastal areas. An automatic vessel detection system is necessary to accurately identify vessels and their categories, typically utilizing a reliable computer vision system. The nano version of YOLO11 has emerged as one of the object detection methods that officially provides lightweight computing, but still has limitations in extracting complex features. Contextual Transformer (CoT) efficiently utilizes long-range relationships, thereby enhancing feature discrimination performance. This study proposes a vessel detection system by modifying the YOLO11 architecture using the Contextual Transformer block. This work introduces YOLO11-Pico, a lighter version of nano, with channel size adjustments at certain stages for further efficiency. The proposed CoT block applies fewer multiplication mapping operations, which are capable of representing global features to obtain richer contextual information. The SeaShips dataset is used as the source of data for model training and evaluation. Experimental results demonstrate that the proposed model YOLO11-pico-CoT achieves superior performance compared to prominent lightweight YOLO architectures, including the YOLO11n baseline, YOLOv5n, YOLOv10n, and the latest YOLOv12n. The integration of CoT contributes positively to improving the accuracy of ship category and location predictions, achieving 0.964 mAP50 and 0.714 mAP50:95. Additionally, efficiency evaluations show that the proposed module is computationally lighter and has fewer parameters, specifically 1,711,250 parameters while operating at 3.97 FPS, giving it an advantage in terms of capabilities over the comparison methods.
Identification of Source Code Plagiarism Using a Natural Language Processing (NLP) Approach Based on Code Writing Style Analysis Akbar, Muhammad Ilham; Ningrum, Novita Kurnia
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.v9i6.11206

Abstract

Source code plagiarism identificatio requires a system capable of identifying semantic similarity rather than mere textual resemblance. This study utilized a dataset of 1,000 source code files, which after cleaning resulted in 996 individual code samples collected from GitHub repositories. The dataset included various programming languages (Python, Java, JavaScript, TypeScript, C++), divided into 697 training data, 149 validation data, and 149 testing data. The model employed was CodeBERT, configured with a hidden size of 768, 12 layers, and 12 attention heads. CodeBERT generated vector embeddings for each code sample, which were then projected by a Siamese Network to calculate cosine similarity between code pairs. Testing used a threshold of 0.80 to classify plagiarism. The identification results achieved an accuracy of 96.4%, precision of 95.2%, recall of 97.8%, F1-score of 96.4%, and an error rate of 4.6%. The system produced similarity scores and status labels of “plagiarism detected” or “not detected,” demonstrating the effectiveness of the CodeBERT-based approach for adaptive and intelligent code similarity identificatio.

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