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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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Prediction of Tuberculosis Treatment Outcomes in Indonesia Using Support Vector Machine and Random Forest Triloka, Joko; Sugianto, Dian
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10018

Abstract

Tuberculosis (TB) remains a global health challenge, particularly in developing countries such as Indonesia, which ranks third worldwide in the number of TB cases. This study aims to evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in predicting TB patient recovery rates based on clinical data obtained from healthcare facilities in Indonesia. Evaluation results indicate that the model achieved very high precision scores (100%) for the "Deceased," "Transferred," and "Default" categories; however, these findings require critical interpretation due to the likely class imbalance in those categories. In contrast, for the "Recovered" and "Completed" categories—where data instances were more numerous—the model exhibited lower precision and recall values (below 90%), reflecting challenges in accurately predicting majority classes. These results suggest that despite seemingly high numerical performance, model predictions can be biased if class distribution is not appropriately considered. The main contribution of this research lies in providing a comparative analysis of two widely used machine learning algorithms in predicting TB recovery outcomes, while emphasizing the importance of addressing data imbalance issues in clinical predictive modeling. The findings provide a practical basis for integrating predictive algorithms into clinical workflows, enabling more accurate monitoring of patient recovery and timely adjustments of TB treatment plans in Indonesia.
Implementation of Clustering Method Using K-Means Algorithm for Grouping BPJS Health Patient Medical Record Data Sapitri, Anggri; Nurdin, Nurdin; Afrilia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10046

Abstract

Clustering medical record data of BPJS Health patients is essential in supporting data-driven decision-making in hospitals. This study aims to implement the K-Means algorithm to cluster patient medical records at RSUD Simeulue based on BPJS class and patient address variables. The data were first normalized using the Z-Score method to standardize variable scales, followed by the iterative application of the K-Means algorithm until convergence was reached at the sixth iteration. The study employed three Cluster, namely Cluster 1 (Very Many), Cluster 2 (Many), and Cluster 3 (Not Many). The final results show that Cluster 1 contains 258 patients from Class 1 and 292 from Class 2; Cluster 2 consists of 296 patients from Class 2; and Cluster 3 includes 101 patients from Class 1, 115 from Class 2, and 148 from Class 3. In addition to classification by BPJS class, clustering based on patient address revealed a dominant distribution from Simeulue Timur, Teluk Dalam, and Teupah Selatan sub-districts. The clustering results were implemented into a web-based information system using the Laravel framework and MySQL database, enabling hospital administrators to visualize and analyze patient data effectively. This study demonstrates that the K-Means algorithm can be effectively applied in classifying medical record data to support healthcare management decision-making.
Clustering Coastal Areas Based on Aquaculture Productivity in North Aceh Regency Using K-Means Algorithm Ulfa, Septia Mulya; Dinata, Rozzi Kesuma; Risawandi, Risawandi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10094

Abstract

This study aims to cluster coastal subdistricts in North Aceh Regency based on the productivity of seven key aquaculture commodities milkfish, vannamei shrimp, tiger shrimp, tilapia, mojarra, grouper, and crab using the K-Means algorithm. The dataset, sourced from 15 coastal subdistricts, was normalized using the Z-Score method. The optimal number of clusters was determined using the Elbow Method, and clustering performance was evaluated with the Silhouette Score, yielding a value of 0.5293, indicating a moderately well-defined structure. The resulting clusters reflect distinct productivity levels: Cluster 0 (low), Cluster 1 (moderate), and Cluster 2 (high). A two-dimensional PCA plot was used to visualize the clusters, showing clear separations among them. These findings offer valuable insights for regional planners and policymakers in developing targeted aquaculture strategies and optimizing resource allocation, particularly for underperforming areas.
Clustering of Aquaculture Productivity Villages in East Aceh Using the K-Means Algorithm Arif, M. Arif Saputra; Dinata, Rozzi Kesuma; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10102

Abstract

This study aims to classify villages based on the level of pond utilization and to develop a web-based application for categorizing aquaculture areas in East Aceh Regency. In contrast to traditional definitions based on harvest volume, this research defines productivity functionally—whether the pond area is actively managed or abandoned. The dataset consists of 146 villages and includes five primary variables: number of fish farmers, total pond area, number of pond plots, productive pond area, and abandoned pond area. Clustering was conducted using the K-Means algorithm, resulting in two main groups: productive and non-productive villages. Validation through the Silhouette Score revealed that using k = 2 yielded the highest score of 0.7576, indicating the most optimal clustering structure. The analysis showed that 92% of villages were categorized as productive, while 8% fell into the non-productive cluster. These two clusters differ significantly in terms of land utilization ratios and the number of active aquaculture workers. The findings not only offer a more refined spatial insight but also serve as a basis for the Department of Marine Affairs and Fisheries in formulating aquaculture zoning, revitalization programs, and more targeted resource allocation.
Regional Clustering in Sumatera Based on Walfare Indicators Using Fuzzy C-Means Putri, Rahma Dana; Sari, Devni Prima
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10103

Abstract

Welfare refers to a condition in which individuals have sufficient means to meet both physical and spiritual needs. In Indonesia, welfare is a national goal, yet Sumatra experiences the highest development disparity, contributing to unequal welfare distribution across regions. This study aims to cluster regions in Sumatra based on welfare indicators using the Fuzzy C-Means (FCM) method, analyze cluster characteristics, and provide policy recommendations for decision-makers. FCM is used because it accommodates uncertainty and allows each data point to belong to more than one cluster, making it suitable for welfare analysis. Cluster validity was tested using Partition Coefficient Index (PCI) and Silhouette Coefficient, both indicating that the optimal number of clusters is two. The results show that Cluster 1 consists of 62 regions with relatively higher welfare conditions, while Cluster 2 includes 92 regions with lower welfare characteristics. One notable member of Cluster 2 is Ogan Komering Ulu, with a high membership degree of 0.869. Recommended policies include improving access to clean water and healthcare, enhancing education, strengthening local economies, and delivering targeted social assistance to underdeveloped areas. For Cluster 1, sustainable development efforts should be maintained.
Image-Based Classification of Healthy and Unhealthy Goats Using ResNet-18 Deep Learning Model Amin, Nurrochim Amin Putra; Wardhana, Ajie Kusuma
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10267

Abstract

Early detection of livestock health conditions is a critical factor in maintaining animal productivity and welfare. This study aims to develop an image-based classification system for identifying healthy and unhealthy goats using deep learning techniques. The dataset of goat images was obtained from Roboflow and processed through a series of augmentation, normalization, and feature extraction stages using the ResNet-18 convolutional neural network architecture pretrained on ImageNet. The dataset was divided into training and testing sets with a 70:30 stratified split to ensure balanced class distribution. To address class imbalance, a random undersampling technique was applied. The model was trained using optimally tuned hyperparameters, including the Adam optimizer, cross-entropy loss function, a batch size of 32, and 20 epochs. Evaluation results showed that the model achieved an accuracy of 95.97%, with a precision of 96.22%, recall of 95.97%, and F1-score of 95.92%. The confusion matrix and evaluation curves demonstrated that the model is both stable and reliable. These findings indicate that the proposed system has strong potential to be implemented in automated and real-time livestock health monitoring applications, providing a fast, accurate, and non-invasive solution for precision livestock farming.
Comparison of FEM-LSDV Panel Regression with Classical Panel Regression Models in Analyzing Economic Growth in Indonesia Andi, Harismahyanti A; Alimatun, Najiha; Yunita, Andi Isna; Ratmila, Ratmila; Nur'eni, Nur'eni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10318

Abstract

This study evaluates the performance of multiple panel regression approaches in modeling the determinants of regional economic growth in Indonesia. It specifically compares three classical panel models: the Common Effect Model (CEM), the Random Effect Model (REM), and the Fixed Effect Model (FEM), alongside the Fixed Effect Model with the Least Squares Dummy Variable (FEM LSDV) approach. The analysis is based on panel data covering 34 provinces from 2019 to 2023, using key macroeconomic indicators such as inflation, investment, exports, money supply, open unemployment rate, and participation in the national health insurance program (JKN). The models are assessed using formal statistical tests, including the Chow and Hausman tests, and evaluated through performance metrics such as RMSE, AIC, and R-squared. The results show that the FEM LSDV model offers the best performance, with an R-squared value of 0.7039, RMSE of 0.5442, and an AIC of 365.55. Notably, the model identifies North Maluku Province as contributing positively and significantly to economic growth, while the year 2020 shows a significant negative impact, likely due to the economic disruptions caused by the COVID-19 pandemic. These findings demonstrate the effectiveness of the FEM LSDV approach in capturing both spatial and temporal heterogeneity in regional economic analysis and support its application in policy-oriented research.
Classification of Rice Leaf Diseases Using Support Vector Machine with HSV and GLCM-Based Feature Extraction Ramli, Rizal; Evanita, Evanita; Akbar Riadi, Aditya
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10403

Abstract

This study aims to classify rice leaf diseases using the Support Vector Machine (SVM) algorithm based on image processing and feature extraction. A total of 600 rice leaf images were collected, each representing one of five disease types: bacterial blight, leaf smut, leaf blast, brown spot, and hispa. The images underwent preprocessing, including resizing, background removal, and feature extraction using HSV and GLCM methods. Extracted features were then used to train and test an SVM classification model. The evaluation using confusion matrix showed an overall accuracy of 83%, with class-specific F1-scores ranging from 0.72 to 0.90. These results indicate that SVM is effective in classifying rice leaf diseases and can potentially assist farmers in early disease detection to reduce crop loss.
Z-Score Based Initialization for K-Medoids Clustering: Application on QSAR Toxicity Data Nurdin, Nurdin; Amalia, Nova; Fajriana, Fajriana
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10448

Abstract

The efficiency of clustering algorithms significantly depends on the initialization quality, especially in unsupervised learning applied to complex datasets. This study introduces an enhanced K-Medoids clustering approach using Z-Score-based medoid initialization to improve convergence speed and cluster validity. The method was evaluated using the QSAR Fish Toxicity dataset, consisting of 908 instances and seven numerical features. Initial medoids were selected based on standardized Z-Score values, resulting in a substantial reduction in convergence time from an average of 6 iterations to just 2. Clustering performance was assessed using three internal validation metrics: Davies-Bouldin Index (DBI), Silhouette Coefficient (SC), and Calinski-Harabasz Index (CHI). The DBI score decreased from 1.7328 to 0.8768, indicating improved cluster compactness and separation. In parallel, the SC increased from 0.327 to 0.619, and the CHI rose from 214.75 to 562.43, confirming more coherent and well-separated clusters. These results demonstrate that Z-Score-based initialization significantly boosts the robustness of K-Medoids, offering a simple yet effective strategy for unsupervised partitioning, particularly in toxicological and biochemical data analysis.
Enhancing Clustering Accuracy Using K-Means with Seeds Optimization Mahiruna, Adiyah; Ngatimin, Ngatimin; Destriana, Rachmat; Rachmawanto, Eko Hari; Yuliansyah, Herman; Hidayat, Muhammad Taufiq
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10458

Abstract

In this study, the development of the Mean-based method proposed by Goyal and Kumar will be carried out by changing the initial cluster center determination step, which was originally based on the origin point O (0,0), to be replaced with the arithmetic mean. To assess the performance of the proposed method, it will be compared with the Global K-means method and the Mean-based K-means method. In this study, the performance of these methods will be measured using the Davies-Bouldin Index, and the significance of the proposed method will be measured using the Friedman Test. This study proposes a method of Improving K-Means Performance through Initial Center Optimization based on Second Global Average for Clustering Osteoporosis Diagnosis of lifestyle factors. Evaluation of K-Means performance through Initial Center Optimization based on Second Global Average with DBI measurements. The targeted experimental results of this study include improving the performance of K-means optimized through the initial center based on Second Global Average. From the results of nine experiments with the number of clusters [2,3,4,5,6], it can be seen that the method proposed in this study has the same superior performance compared to the Mean Based method and compared to the Global K-means method.

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