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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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Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
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.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Design and Implementation of an IoT-Based Smart Drip Irrigation System Using Takagi-Sugeno Fuzzy Logic for Melon Cultivation Nur Alif, Muhammad Sufi; Dian Pertiwi, Kharisma Monika
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.v9i6.11424

Abstract

The melon plant (Cucumis melo L.), a species of the Cucurbitaceae family, requires precise water management to support optimal growth. This study developed an Internet of Things (IoT)-based innovative irrigation system employing Takagi-Sugeno fuzzy logic to regulate water supply for melon cultivation in a greenhouse. The system integrates a capacitive soil moisture sensor and a DS18B20 temperature sensor, both connected to an ESP8266 microcontroller, which controls a solenoid valve used in the drip irrigation method. Sensor data are transmitted in real-time to Firebase Realtime Database (cloud platform) for monitoring through a web-based interface. The solenoid valve opening duration ranges from 0 to 720 seconds per irrigation session, dynamically adjusted according to soil moisture and temperature inputs. Experimental results demonstrate that the proposed system effectively maintains soil moisture within the optimal range of 60%–80%. However, plant growth evaluation indicates that the system has not fully promoted healthy development, particularly in plant height and leaf width, likely due to additional factors such as soil conditions, humidity, and nutrient availability. Despite these limitations, the proposed smart irrigation system shows strong potential for further refinement to enhance water efficiency and support sustainable melon cultivation.
Comparative No-Reference Evaluation of Classical Image Sharpening Techniques under Varying Degradation Conditions Santoso, Siane; Setiadi, De Rosal Ignatius Moses; Pramunendar, Ricardus Anggi
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.v9i6.11430

Abstract

This research conducts a comparative evaluation of four image sharpening methods: Unsharp Masking, Laplacian of Gaussian, High-Boost Filtering, and Adaptive High-Boost Filtering. These methods are tested on low-contrast, blurred, normal, and high-contrast images. The assessment uses No Reference Image Quality Assessment metrics, specifically BRISQUE and NIQE, along with intensity histogram analysis and visual inspection. Results show that High-Boost Filtering improves global contrast, reducing BRISQUE scores to 26.28 for low-contrast images and 27.56 for high-contrast images, although it can cause halo artifacts. Unsharp Masking performs best on blurred images, lowering BRISQUE to 26.65, but it is more sensitive to noise. The Laplacian of Gaussian yields relatively low NIQE scores, such as 3.04 in low-contrast and 3.10 in high-contrast images; however, its output often appears coarse in texture. Adaptive High-Boost Filtering performs best on normal images, achieving a BRISQUE score of 11.89, but shows limited improvement in other cases. Notably, alignment between NIQE scores and perceptual evaluation is only observed in high-contrast images. These results confirm that no single technique is universally optimal, emphasizing the importance of selecting sharpening methods based on specific image degradation characteristics. Additionally, this observation highlights that BRISQUE more reliably reflects perceived image quality, whereas NIQE occasionally diverges from subjective judgments.
Capability Level Assessment of IT Governance in the SIAP KOJA Application Using the COBIT 2019 Framework Widasari, Yesya Najwa; Oktadini, Nabila Rizky
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.v9i6.11433

Abstract

This study assesses the IT governance capability of the SIAP KOJA application at the Jambi City Department of Communication and Informatics (Diskominfo). SIAP KOJA was introduced to strengthen attendance discipline, transparency, and accountability through geofencing and biometric features. Using the COBIT 2019 framework, the assessment aligns IT processes with institutional objectives and focuses on two key processes: APO11 (Manage Quality) and BAI05 (Manage Organizational Change Enablement). Data were collected through a literature review, interviews, observations, and a structured questionnaire based on the COBIT 2019 Process Assessment Model. The sample comprised five personnel from Diskominfo’s Informatics Applications Division, purposively selected for their direct involvement in planning, development, and operations. Results indicate Level 3 (Defined) capability for both APO11 and BAI05 with standards documented. At Level 4, APO11 reached 75.56% (Largely Achieved) and BAI05 reached 76.00% (Largely Achieved). Because these fall below the ≥85% “Fully Achieved” threshold, progression was halted, and the capability level remains Level 3. Limitations in structured measurement and continuous monitoring contribute to a two-level gap from the Level 5 (Optimizing) target. The study recommends formalizing a quality management system with service-level agreements and performance indicators; strengthening outcome-based change management through compliance audits and systematic user feedback; and institutionalizing lessons learned. These improvements are essential for enhancing governance capability, ensuring system reliability, and supporting successful digital transformation in local government.
Analysis of Deep Learning Algorithms Using ConvNeXt and Vision Transformer for Brain Tumor Disease Ekayanda, Gilang; 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.v9i6.11438

Abstract

This study aims to conduct a comparative analysis and identify the most effective deep learning architecture between ConvNeXt and Vision Transformer (ViT) for the automated classification of brain tumors from MRI imagery. Rapid and accurate brain tumor diagnosis is crucial; however, the manual interpretation of MRI scans is time-consuming and reliant on specialist expertise, creating an urgent need for reliable automation in brain tumor diagnosis. This research utilizes a dataset of 4,600 images, balanced between 2,513 'Brain Tumor' and 2,087 'Healthy' instances. A robust 5-Fold Cross-Validation methodology was employed to evaluate model performance, wherein the data was divided into five folds, each consisting of 920 images, ensuring every image served as both training and testing data. The quantitative results demonstrated high efficacy from both models, although ConvNeXt achieved a slight, consistent advantage. ConvNeXt obtained an accuracy of 99.13%, precision of 99.13%, recall of 99.13%, and an F1-Score of 99.13%. In comparison, the ViT model scored an accuracy of 98.13%, precision of 98.14%, recall of 98.13%, and an F1-Score of 98.13%. This quantitative superiority was validated through qualitative analysis using saliency maps, which confirmed that the models' computational attention was accurately focused on the anatomical locations of the actual tumor lesions.
An Ensemble Learning Approach for Sentiment Analysis of Maxim Application Reviews Using SVM, KNN, and Random Forest Sasmita, Ruth Mei; Meiriza, Allsela; Novianti, Hardini
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.v9i6.11447

Abstract

The development of online transportation applications such as Maxim has increased the need for sentiment analysis to understand user opinions from reviews on the Google Play Store. The main challenges in this analysis are language diversity, variations in writing style, and data imbalance, which affect model accuracy. This study aims to evaluate the performance of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) algorithms, as well as ensemble approaches through the Voting Classifier and Combined Classifier, in sentiment analysis of Maxim app reviews. The dataset consists of 2,851 Indonesian-language reviews collected through web scraping from the Google Play Store in 2025. Sentiment labels were automatically determined based on user ratings, where ratings of 4–5 were categorized as positive and ratings below 4 as negative, with an initial distribution of 2,295 positive and 556 negative reviews before balancing using SMOTE–Tomek Links. Preprocessing steps included case folding, tokenization, stopword removal, and stemming using Sastrawi, while feature weighting was performed with unigram TF-IDF. The Combined Classifier merged the probability scores from the SVM, KNN, and RF models to produce the final prediction. Evaluation was conducted using 5-Fold Cross Validation with accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The results show that RF and the Combined Classifier achieved the best performance with 85% accuracy, 87% precision, 85% recall, 86% F1-score, and 0.91 ROC-AUC, while SVM and the Voting Classifier ranked in the middle and KNN ranked the lowest. These findings confirm that ensemble learning, particularly the Combined Classifier, effectively improves the accuracy and stability of review classification compared to individual methods.
Comparative Analysis of Random Forest, SVM, and Naive Bayes for Cardiovascular Disease Prediction Rayadhani, Windy Aldora; 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.v9i6.11451

Abstract

Cardiovascular disease is one of the leading causes of death worldwide; therefore, accurate early detection is essential to reduce fatal risks. This study aims to compare the performance of three machine learning algorithms — Random Forest, Support Vector Machine (SVM), and Naïve Bayes — in predicting cardiovascular disease risk using the Mendeley Cardiovascular Disease Dataset, which contains 1,000 patient records and 14 clinical attributes. The models were evaluated using accuracy, precision, recall, and F1-score metrics, and their performance differences were statistically tested using the paired t-test. The experimental results indicate that the Random Forest algorithm achieved the best performance with 99% accuracy, 100% recall, 98% precision, and an F1-score of 99%. The SVM model followed with 98% accuracy and 100% recall, while the Naïve Bayes algorithm obtained 94.5% accuracy and an F1-score of 95%. The p-value < 0.05 confirmed that the performance differences among the three models were statistically significant. From a clinical perspective, a model with high recall, such as Random Forest, is more desirable because it reduces the likelihood of false negatives, which are critical in heart disease diagnosis. The feature importance analysis also revealed that age, resting blood pressure, and cholesterol level were the most influential factors in predicting cardiovascular risk. These findings suggest that machine learning algorithms, particularly Random Forest, have strong potential to be implemented in Clinical Decision Support Systems (CDSS) for accurate and efficient early detection of cardiovascular disease.
Optimizing F1 Tyre Performance Prediction with SVC, XGBoost, and Optuna For Dutch GP 2022 Anandatama, Dimas Haydar; Gamayanto, Indra
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.v9i6.11452

Abstract

Formula 1 has evolved into a data-centric sport where strategic decisions, particularly tire compound selection (Soft, Medium, Hard), are critical for success. The ability to accurately identify a competitor's compound from observable telemetry data offers a significant strategic advantage, yet the predictive signals are subtle and difficult to distinguish. This study implements and compares two distinct machine learning methodologies to classify F1 tyre compounds using telemetry data from the 2022 Dutch Grand Prix. First, a baseline model was established using standard dynamic features (e.g., avg_speed, avg_rpm). While this approach confirmed the superiority of XGBoost over SVC, it yielded a modest accuracy of 67.99% and revealed a critical deficiency: a failure to reliably identify the HARD compound, registering a poor F1-score of 0.57. To address these limitations, an advanced methodology was developed, integrating hybrid feature engineering (e.g., LapTime, SectorTime, TyreLife) with deep hyperparameter optimization via Optuna. This enhanced approach resulted in a significantly more robust XGBoost model, achieving a final, stable accuracy of 77.34%. More importantly, it solved the baseline's primary flaw, increasing the F1-score for the critical HARD compound by 36.8% to 0.78. A feature importance analysis confirmed this methodological shift, as the most dominant predictors changed from the baseline's generalized avg_speed to the advanced model's outcome-based features (LapTime, Sector3Time). The findings definitively conclude that while algorithm selection is important, the most critical factor for this task is the quality of feature engineering. Integrating outcome-based and strategic-context features is essential to successfully extracting the subtle performance signatures that differentiate F1 tyre compounds.
Analysis of Stacking Ensemble Method in Machine Learning Algorithms to Predict Student Depression Levina, Naouthla Asia; 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.v9i6.11453

Abstract

Mental health issues, particularly depression among university students, require early detection and intervention due to their profound impact on academic performance and overall well-being. Although machine learning has been utilized in previous studies to predict depression, most research still relies on single-model approaches and rarely employs publicly available datasets that have undergone comprehensive preprocessing. This study aims to develop a depression prediction model for university students using a two-level stacking ensemble technique with cross-validation stacking, integrating Random Forest, Gradient Boosting, and XGBoost as base learners, and Logistic Regression as the meta-learner. A public dataset from Kaggle was utilized, consisting of 502 student records and 10 multidimensional predictor variables. Data preprocessing included cleaning, feature encoding, and standardization. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The proposed stacking ensemble model achieved excellent performance, with an accuracy of 98.02%, ROC-AUC of 99.8%, precision of 96%, recall of 100%, and an F1-score of 98% for the depression class. These results demonstrate that the stacking ensemble method is highly effective for early depression detection among university students and has strong potential for implementation as a decision-support tool in academic environments.
Orchid Species Classification Using the DenseNet121 Deep Learning Model with a Data Imbalance Handling Approach Akbar, Fadhilah Aditya; Sari, Christy Atika
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.v9i6.11458

Abstract

For conservation, commercial cultivation, and scientific research, accurate identification of orchid species often requires specialized expertise. In this study, the DenseNet121 deep learning architecture was employed to develop an automated classification system for four popular orchid species. DenseNet121 was selected for its ability to extract complex hierarchical features and its strong performance on limited-scale datasets. The initial dataset comprised 1,935 images of Phalaenopsis, Cattleya, Dendrobium, and Vanda orchids. However, after manual removal of duplicate images, only 1,658 images remained, revealing significant class imbalance. The undersampling method was applied to balance each class to 248 samples. The dataset was then split into 75% training, 15% validation, and 10% testing, and enhanced through data augmentation techniques such as rotation, flipping, brightness variation, width shift, height shift, and zoom. The final model achieved 97.00% accuracy with class-specific performance ranging from 92.59% to 100% accuracy across different orchid species. This research can serve as a foundation for developing mobile or web applications to assist researchers, farmers, and orchid enthusiasts in accurately identifying orchid species, while supporting conservation efforts for orchid biodiversity in Indonesia.

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