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The Journal of Algorithmic Digital Engineering and Networks (JADEN)
Published by CV. Data Sinergi Digital
ISSN : -     EISSN : 31106757     DOI : https://doi.org/10.65853/jaden.v1i2.120
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
The Journal of Algorithmic Digital Engineering and Networks (JADEN) is a scientific journal committed to publishing high-quality research articles in the fields of algorithmic studies, digital engineering, and network systems. Manuscripts submitted through the Online Journal System (OJS) must align with the journal’s focus and scope, as well as adhere to the author guidelines established by JADEN. The peer review process employs a Double Blind Review mechanism to ensure objectivity and academic integrity. All accepted articles in JADEN are made available through open access, enabling unrestricted readership and facilitating the dissemination of knowledge to the global scientific community. JADEN accepts and publishes high-quality scientific articles covering, but not limited to, the following research areas: Algorithm Design and Analysis Computational Intelligence Software Engineering and Software Architecture Data Mining and Knowledge Discovery Cybersecurity and Cryptography Image and Video Processing Information Systems and Enterprise Applications Computer Networks and Network Protocols Artificial Intelligence and Expert Systems Information Retrieval and Database Systems Big Data Analytics and Data Science Modeling, Simulation, and Optimization Robotics and Autonomous Systems Internet of Things (IoT) and Edge Computing Machine Learning and Deep Learning Soft Computing and Evolutionary Computation Multimedia Systems and Applications Digital Signal Processing Cloud Computing and Distributed Systems Blockchain Technology and Applications In addition to the above, JADEN also welcomes manuscripts that address emerging topics and interdisciplinary research in algorithmic, digital engineering, and network-related domains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
 1 
Feature-Based Pistachio Classification: A 28-Dimensional Dataset with KNN Steven Alexander Pasaribu Pasaribu; Reza permana Olivera Simbolon; Bonfilio Delatersia Nainggolan
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.97

Abstract

Pistachio (Pistacia vera L.) is a high-value horticultural crop in which accurate variety classification is essential for quality assurance and trade. Conventional classification methods relying on visual inspection are subjective, labor-intensive, and inadequate for large-scale industrial deployment. This study evaluates the effectiveness of the k-Nearest Neighbors (KNN) algorithm for pistachio variety classification using a feature-based dataset comprising 2,148 samples and 28 morphological and color descriptors. Experimental results indicate that the optimized KNN configuration (k = 5, Euclidean distance metric, distance-based weighting) attained a test accuracy of 97% and a macro F1-score of 0.97, while both ROC-AUC and average precision values approached 0.99. The confusion matrix demonstrated only marginal misclassifications, confirming the high discriminative capability of the handcrafted features. These findings underscore that feature-based approaches remain competitive with, and in some contexts superior to, deep learning methods by providing interpretable, computationally efficient, and robust solutions. The proposed model offers practical potential for integration into industrial pistachio sorting systems and broader agricultural informatics applications, supporting scalable and cost-effective automation in crop quality assessment
Deep Learning for Prediction Contents High Calories on Fast Food Menus Romayani Lbn.Gaol Lbn.Gaol; Muhammad Harry Riyendra
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.100

Abstract

The rising consumption of fast food, often high in calories, has contributed to increasing rates of obesity and non-communicable diseases. This study applies a Deep Learning approach to predict high-calorie content in fast food menus using numerical nutritional data. The dataset comprises 515 menu items with 17 attributes, including calories, fat, cholesterol, protein, sugar, sodium, vitamins, and food categories. Data preprocessing involved handling missing values, normalization, categorical encoding, and splitting the dataset into training and testing sets with an 80:20 ratio. A Multi-Layer Perceptron (MLP) model with three hidden layers was implemented, employing ReLU activation and a sigmoid output for binary classification. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, and compared with Logistic Regression, SVM, and Random Forest. Results show that the MLP achieved an accuracy of 0.89, precision of 0.87, recall of 0.91, F1-score of 0.89, and ROC-AUC of 0.94, outperforming classical methods. The high recall underscores the model’s capability to reliably detect high-calorie items. These findings indicate that deep learning effectively captures complex relationships among nutritional attributes compared to traditional algorithms. This research contributes to the advancement of intelligent nutrition-based systems that can be integrated into mobile or web applications to support healthier lifestyle decisions. Future work may expand to larger and more diverse datasets, explore multimodal data such as images, and assess the broader health impacts of high-calorie fast food consumption.
Machine Learning-Based EDA and Classification for Banana Quality Evaluation Anggi Putri Dewi Harefa Harefa; Heny Prisla Panjaitan; Romaster Sijabat
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.101

Abstract

This study explores the application of Machine Learning (ML) for the evaluation of banana quality through Exploratory Data Analysis (EDA) and classification techniques. The research emphasizes the importance of objective, accurate, and consistent assessment methods to replace traditional manual evaluation, which is often subjective and inefficient. A dataset comprising multiple banana attributes, including sweetness, firmness, acidity, size, and ripeness, was analyzed to identify key patterns and relationships. Several ML algorithms, such as Random Forest (RF), were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. Results demonstrated that the Random Forest algorithm achieved the highest performance with an overall accuracy of 90%, effectively distinguishing bananas in the “Good” and “Poor” categories, though limitations remained for the “Average” class due to class imbalance. These findings highlight the potential of ML-based approaches to enhance smart agriculture practices, providing a reliable and scalable solution for automated banana quality evaluation. Future research should focus on addressing class imbalance and integrating additional features, such as image-based or biochemical data, to further improve classification performance.
Oversampling SMOTE to Handle Imbalance in Multiclass Diabetes Dataset Dika Dika; Syahrul Ramadhan
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.102

Abstract

Diabetes mellitus is a chronic disease with an increasing global prevalence that requires early detection and accurate classification to prevent severe complications. Machine learning has been widely applied in diabetes prediction; however, one of the major challenges lies in the class imbalance problem commonly found in medical datasets. This study focuses on the Multiclass Diabetes Dataset, which consists of 264 samples and exhibits imbalanced distribution among classes (Class 2: 128 samples, Class 0: 96 samples, Class 1: 40 samples). Such imbalance may bias the classifier toward majority classes, reducing its ability to recognize minority classes. The results indicate that SMOTE effectively improved the model’s ability to classify minority classes, with significant increases in recall and F1-score. Among the tested algorithms, Random Forest achieved the best performance, with an overall accuracy of 98% and F1-score above 0.98. Although KNN experienced a slight performance drop after SMOTE, other algorithms, particularly SVM and Logistic Regression, demonstrated notable improvements.
Hybrid Ensemble Learning to Improve Prediction Disease Kidney Chronic Fahmi Izhari Izhari; Rini Meiyanti
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.103

Abstract

Chronic Kidney Disease (CKD) is a major global health issue with a steadily increasing prevalence and high mortality rates. Early detection remains challenging due to non-specific clinical symptoms, often leading to late diagnosis and severe complications such as kidney failure. Machine learning (ML) offers significant opportunities to support early detection and prediction through clinical and laboratory data analysis. However, single models such as Random Forest (RF), Gradient Boosting (GBM), and Support Vector Machine (SVM) still face limitations in generalization and stability when applied to complex and imbalanced datasets. This study proposes a Hybrid Ensemble Learning approach that combines bagging, boosting, and stacking strategies to improve predictive accuracy and robustness. Experimental results using the CKD dataset demonstrate that the Hybrid Stacking model achieves the best performance, with 99% accuracy, 1.0 precision, 0.983 recall, and an AUC-ROC of 0.992. These findings highlight that Hybrid Ensemble Learning, particularly stacking, significantly enhances model sensitivity and reliability, making it a promising tool for supporting clinical decision-making in CKD prediction.
Fungal Disease Detection Using CNN Deep Learning Method Dika Dika; Muhammad Iqbal
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.120

Abstract

This study aims to detect mushroom diseases based on digital images using the Deep Learning Convolutional Neural Network (CNN) method. Fungal diseases are often the main cause of decreased quality and yield, so a fast and accurate detection method is needed. The dataset used consists of images of healthy mushrooms and diseased mushrooms obtained through direct image capture at the cultivation location. The research stages include image preprocessing, CNN model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the CNN model is able to detect mushroom diseases with a high level of accuracy, so this method has the potential to be used as a decision support system in mushroom cultivation.
Modeling Sentiment Patterns in Indonesian Gojek Reviews with TF-IDF and Support Vector Machine Angel Gloria Simanullang; Yashelter Aenjealik Nazara; Seikel Krina Michi Berutu; Marisa Aurelia Damanik; Aryapto Pardosi Pardosi
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.121

Abstract

The rapid growth of mobile-based ride-hailing services in Indonesia has significantly increased the use of digital platforms such as Gojek. This growth has generated a large volume of user reviews on the Google Play Store, reflecting user experiences, perceptions, and satisfaction levels. However, manual analysis of these reviews is inefficient due to their volume and subjective nature. Therefore, this study aims to model sentiment patterns in Indonesian Gojek user reviews using a machine learning approach. This research applies the Support Vector Machine (SVM) algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction. The dataset consists of 5,000 Indonesian-language user reviews collected from the Google Play Store. Text preprocessing includes case folding, cleaning, tokenizing, stopword removal, and stemming. Sentiment labeling is performed automatically using a lexicon-based approach, classifying the data into positive, negative, and neutral categories. The results indicate that neutral sentiment dominates the dataset (60.26%), followed by positive (31.34%) and negative (8.40%) sentiments. The SVM model achieves an accuracy of 72.58%. The neutral class shows the highest recall (99.17%), while the positive class achieves the highest precision (99.65%). These findings indicate that TF-IDF and SVM are effective for modeling sentiment patterns, although challenges remain in handling ambiguous expressions, such as sarcasm or mixed sentiments, which can lead to misclassification.
Hierarchical Clustering Sensitivity to Distance Metric Selection Based on Z-Score Normalization Christine Casin Rumondang Simanjuntak; Sindi Rosdiana Dakhi; Yohanes Newman Afipratama Sarumaha
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.122

Abstract

This study examines the sensitivity of hierarchical clustering results to the choice of distance metrics using Z-score standardized data from the World Happiness Report. Socio-economic datasets often contain variables with different scales, which can bias distance-based clustering methods if not properly addressed. Therefore, Z-score normalization was applied to ensure each variable contributes equally to the distance computation. The analysis focuses on six socio-economic indicators, namely gross domestic product (GDP), social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Hierarchical clustering was performed using a fixed average linkage method to control the cluster-merging strategy, while three distance metrics Euclidean, Manhattan, and Cosine were compared to evaluate their influence on clustering outcomes. The results demonstrate that the selection of distance metrics affects dendrogram structure, cluster membership, and the relative proximity among countries. Differences across distance metrics were further reflected in internal cluster evaluation using the silhouette coefficient, indicating varying levels of cluster compactness and separation. These findings highlight that, even after proper normalization, distance metric selection plays a critical role in shaping hierarchical clustering results and their interpretation in socio-economic analyses. Overall, the study underscores the importance of methodological consistency and careful metric selection when grouping countries based on multidimensional happiness-related indicators.
K-Medoids Robustness to Outliers in Management Data Bunga Apriana Gultom; Fanni Maria Dortua Tampubolon Tampubolon; Theresia SU Tanjung Tanjung
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.123

Abstract

This study examines the robustness of the K-Medoids algorithm to the presence of outliers in management data derived from Airbnb property listings, focusing on price and review rating attributes as key indicators in market segmentation and managerial decision-making. The increasing use of data from digital platforms is often accompanied by the emergence of extreme values that have the potential to disrupt the cluster structure and reduce the reliability of the analysis results. Therefore, a clustering method is needed that is able to maintain the stability and consistency of results even though the data contains outliers. This study uses a quantitative and experimental approach based on secondary data, where the implementation of the K-Medoids algorithm and visualization of clustering results are carried out using Python programming, while manual calculations based on Excel spreadsheets are used as a means of conceptual validation of the medoid selection process and distance measurement. The analysis is carried out by comparing the cluster results before and after the presence of outliers using the Manhattan distance metric and evaluating the total cost function at each iteration. The results show that although extreme values in the price attribute cause a wider spread in the data, the cluster structure and medoid positions remain relatively stable. The resulting clusters also retain clear managerial significance, grouping properties into economy, regular, and premium segments, thus remaining relevant to support pricing strategies, service differentiation, and reputation management.
Structural Robustness of Decision Trees under Educational Data Sampling Variations Tiorina Tiorina; Sartika Jane Caroline; Aischa Najwa Salsabilla
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.124

Abstract

Decision tree models are widely applied in educational data analysis due to their simplicity and interpretability. However, these models exhibit high sensitivity to variations in training data, where minor sampling changes can result in substantially different tree structures, potentially reducing model reliability and consistency. This study aims to empirically investigate the structural robustness of decision trees under variations in educational data sampling and to evaluate strategies for improving model stability. An experimental framework is implemented using several educational datasets processed through random subsampling, bootstrap resampling, and k-fold cross-validation. Structural robustness is quantitatively assessed using tree edit distance, node similarity ratio, and tree depth variability. The results indicate that small sampling perturbations can cause significant structural divergence, particularly in datasets characterized by high noise levels and feature correlations. Nevertheless, pruning techniques and ensemble-based stabilization methods effectively enhance structural consistency and reduce model variance. These findings highlight the importance of robustness evaluation in educational data mining and provide empirical insights for developing reliable, stable, and interpretable decision-support systems in educational environments.

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