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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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Extreme Learning Machine Method Application to Forecasting Coffee Beverage Sales Sutanto, Yusuf; Setyadi, Heribertus Ary; Nugroho, Wawan; Al Amin, Budi
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.v9i5.10465

Abstract

Sales estimates can be used to set product prices and increase expected profits. Flyover coffee shop Karanganyar does not have a methodical forecasting method to estimate and predict their need/demand for coffee beverage products. Two previous research that used Extreme Learning Machine (ELM) method in other predictions stated that ELM method has high accuracy and fast compilation time. Another research predicted jeans sales using the ARIMA model and produced an accuracy of 17.05% based on the MAPE (Mean Absolute Percentage Error) method. Menstrual cycle prediction using the Long Short-Term Memory (LSTM) method produces a MAPE value of 7.5%. Two advantages of ELM method from two previous research were used as the basis for selecting ELM method used in our study. To help predict sales of coffee beverage menus, this research utilized an artificial neural network method using ELM algorithm. ELM method consists of an input layer and an output layer connected through a hidden layer. Data used for the test was daily sales data for a month. Data used for this study consisted of 215 data samples. Daily sales data at the Flyover coffee shop were collected from June to December 2024. Based on the results and analysis of error values using MAPE method, an average error value was 8.274%. From comparison of original data results and prediction data, an average MAPE error value the best number of features and hidden neurons is 5.65%.
Stock Price Prediction Using Deep Learning (LSTM) with a Recursive Approach Zakka, Muhamad Syukron; Emigawaty, Emigawaty
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.v9i5.10514

Abstract

Stock price prediction is a critical topic in financial technology research, as accurate forecasts support better decision-making in volatile markets. Numerous studies have applied statistical and machine learning models; however, most focus on one-step-ahead predictions and lack evaluation of recursive strategies in multi-day horizons. This study investigates the application of Long Short-Term Memory (LSTM) with a recursive forecasting approach to enhance stock price prediction accuracy. The dataset was enriched with multiple technical indicators and processed through a systematic Knowledge Discovery in Databases (KDD) pipeline, including preprocessing, transformation, modelling, and evaluation. Experimental results show that the recursive LSTM model achieves superior performance compared to baseline machine learning methods, with high accuracy in short-term horizons and stable performance up to nine days ahead, although accuracy gradually declines due to error accumulation. This work highlights the importance of integrating recursive forecasting with technical indicators to improve predictive capability in emerging markets and provides a foundation for developing adaptive financial forecasting frameworks.
Aircraft Image Classification on a Small-Scale Dataset using MobileNetV2 with Grad-CAM as Explainable AI Lestari, Susi; Dzulfiqar, Mohamad Alif; Lubis, Ahmadi Irmansyah; Nova, Muhammad Andi; Zaimah, Zaimah; Mulyadi, Mulyadi
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.v9i5.10546

Abstract

This study explores aircraft image classification using MobileNetV2 combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. A dataset of 1,500 balanced images—helicopters, propeller aircraft, and jets—was split into training, validation, and testing sets with data augmentation to reduce overfitting. Transfer learning with pre-trained MobileNetV2 achieved an accuracy of 87.56%, with macro-average precision and recall of 85.76% and 87.69%. Grad-CAM visualizations confirmed that correct predictions relied on distinctive features such as rotor blades, propellers, and engines, while misclassifications often stemmed from background distractions or less discriminative areas. These findings demonstrate the potential of lightweight architectures for small-scale datasets and highlight the value of Explainable AI in validating deep learning models. The study provides a practical reference for educational contexts and offers directions for future work with larger datasets.
Automated Generation of Folklore Short Stories Using T5 Transformer Model Pirade, Evangelika; Darma Putra, I Ketut Gede; Singgih Putri, Desy Purnami
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.v9i5.10582

Abstract

High reading interest plays an important role in increasing knowledge and fostering a stronger literacy culture. With the growing access to information and technology, reading interest is also expected to improve through innovative and interactive platforms. However, traditional reading materials often fail to attract younger generations who are more engaged with digital content. To address this challenge, one of the efforts undertaken is the development of a modern platform that provides a collection of short stories enriched with cultural and educational values, tailored to appeal to contemporary readers. This study aims to design and implement a short story generation system using a Transformer-based language model, specifically T5 (Text-to-Text Transfer Transformer). The model is fine-tuned using a curated dataset of folktales from various regions, with the goal of producing relevant, engaging, and coherent narrative texts. The generation process is supported by pre-processing techniques to structure the data into narrative components such as introduction, conflict, climax, and resolution. The generated stories are then evaluated through human evaluation methods, including questionnaires and User Acceptance Testing (UAT), to assess their quality, coherence, engagement, and cultural relevance. This ensures that the system not only produces technically valid texts but also delivers narratives that are meaningful and enjoyable for readers. Ultimately, this study contributes to the promotion of literacy by presenting local wisdom and traditional values from diverse cultures through stories in a more modern, engaging, and accessible format for the younger generation.
Detection of Sugarcane Leaf Disease Using Pre-Trained Feature Extraction and SVM Method Izza, Mufidatul; Lutfi, Moch.
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.v9i5.10626

Abstract

Sugarcane (Saccharum officinarum) is an important commodity in the sugar industry, but it is vulnerable to leaf diseases such as Red Rot, Rust, Yellow Leaf, and Mosaic, which can significantly reduce the quality and quantity of yields. Manual identification is time-consuming and prone to subjective errors, therefore an automatic detection method based on digital images is required. This study proposes a combination of VGG16 pre-trained as a feature extractor with Support Vector Machine (SVM) as a classifier. The dataset used is the Sugarcane Leaf Disease Dataset from Kaggle, consisting of 2,521 images of five classes, which were then balanced through augmentation in the form of rotation, zoom, and flipping to a total of 3,000 images (600 per class). The preprocessing stage includes resizing the images to 224×224 pixels and normalization using the preprocess_input function. Three model scenarios were tested, namely SVM, VGG16, and VGG16+SVM. Evaluation was carried out using two methods, namely an 80:20 train–test split and 10-fold cross-validation, with metrics of accuracy, precision, recall, F1-score, G-Mean, and AUC. The experimental results show that VGG16+SVM provides the best performance with an accuracy of 99.60% on the 80:20 scheme, while on 10-fold cross-validation the average accuracy is 80.76%. This value surpasses the baseline SVM and VGG16 + Softmax, proving that the integration of VGG16 feature extraction with SVM classification can produce stable and accurate performance. This research contributes to the development of image-based plant disease detection systems to support precision agriculture and fast decision-making.
Enhancing E-Commerce Customer Segmentation with Fuzzy C-Means Soft Clustering Probabilities Putra, Muhamad Iqbal Januadi; Alexander, Vincent; Chusyairi, Ahmad; Abdurrahman, Raka Admiral; Pratama, Alexander Daniel
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.v9i5.10652

Abstract

Customer segmentation is of paramount importance in the e-commerce industry, enabling businesses to improve marketing strategies and customer engagement. This study compares the performance of two clustering algorithms, K-Means and Fuzzy C-Means (FCM), using Walmart’s public e-commerce dataset of 550,068 transactions. After preprocessing and normalization, the elbow method was applied to determine the optimal number of clusters, yielding seven clusters for K-Means and eight for FCM. Experimental evaluation based on the silhouette score shows that FCM achieved 0.48, outperforming K-Means which scored 0.36, indicating that FCM generated clusters with stronger cohesion and separation. However, this improvement comes at a computational cost. K-Means consistently required less than 0.02 seconds per run, while FCM averaged 0.3 seconds and peaked at 1.38 seconds when the number of clusters increased, making it approximately 20–30 times slower. Cluster distribution analysis further revealed that K-Means produced an uneven segmentation dominated by a single large cluster, whereas FCM generated a more balanced distribution across its clusters. This demonstrates the advantage of FCM in capturing overlapping and multidimensional customer behaviors through partial memberships, in contrast to the rigid and oversimplify assignments of K-Means. These findings highlight the benefit of adopting FCM for e-commerce segmentation, as it provides more interpretable and actionable insights for personalized marketing. At the same time, the trade-off between clustering quality and computation time suggests that future research should explore optimization techniques such as parallelization, approximate fuzzy clustering, or hybrid models that combine the efficiency of hard clustering with the interpretability of soft clustering.
Forecasting the Number of Passengers for the Jakarta-Bandung High-Speed Rail using SARIMA and SSA Models Mualifah, Laily Nissa Atul; Riyanto, Indra Mahib Zuhair; Rahmawati, Elke Frida; Maulana, Muhammad Fahrezi; Ahdiat, Keyzha Mutiara; Nurdin, Achmad Raihan; Pangestika, Adelia Putri
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.v9i5.10720

Abstract

Time series forecasting is essential for analyzing past data to predict future trends, supporting planning, and decision-making. The SARIMA model is widely used for seasonal data but may be less effective for highly fluctuating or non-stationary data, which can impact forecast accuracy. As an alternative, Singular Spectrum Analysis (SSA) offers a flexible approach, decomposing time series into trend, seasonal, and noise components without strict parametric assumptions, making it effective for complex data patterns. This study compares SARIMA and SSA models in forecasting daily passenger counts on the Jakarta-Bandung high-speed rail, using data from November 1, 2023, to September 30, 2024. The results show that the performance of SSA is more stable compared to SARIMA in the term of MAPE, where SSA provides lower MAPE then SARIMA in all three scenarios of data splits. These results are expected due to the non-linear pattern that appears in the data. Moreover, the predictions on both methods show that slight increment of passengers in the end of 2024 to the beginning of 2025. This finding suggests that the government needs to consider implementing interventions if they wish to change the current trend, such as offering discounts or year-end holiday promotions.
Mobile-Based Multi-Output Animal Taxonomy Classification Using CNN with Edge and Cloud Deployment Patrick, Jason; Ramadhani, Shumaya Resty
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.v9i5.10780

Abstract

Distinguishing animals that appear visually similar but belong to different species or taxonomic groups, such as Eurasian and house sparrows, koi and common carp, or leopard cat and domestic cat, remains challenging and hinders biodiversity education. This study develops a Convolutional Neural Network (CNN)-based multi-output, multi-class taxonomy classification system capable of identifying seven animal species across five taxonomic levels (class, order, family, genus, species), producing 35 possible outputs. The dataset comprised 6,998 images from public sources. Among various configurations, the best-performing model (D3-M2), trained using the High Dataset with 256×256 input size, 0.2 dropout, and four hidden layers, achieved 90.15% average accuracy, the highest F1-score at the family level (98.11%), and 95.99% at the species level. Slightly lower species-level performance was due to high visual similarity among particular species. Edge AI deployment offered faster inference (0.17s) and offline capability, making it ideal for field use. Real-world testing under bright and low light at 30, 60, and 100 cm showed higher accuracy (64.8%) than low light (57.1%), with the most stable performance at 60 cm. However, limitations include an imbalanced dataset and limited environmental variation affecting species-level accuracy. Future work will focus on expanding dataset diversity and employing advanced architectures to improve fine-grained classification. This system offers a practical tool for biodiversity education and species identification, particularly in field environments where rapid, offline, and accurate classification is essential.
Implementation of the Support Vector Machine (SVM) Method for Classifying the Maturity Level of Oil Palm Fruit Oktaviana, Boni; Suriati, Suriati
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.v9i5.10783

Abstract

This study discusses the classification of palm fruit ripeness levels using the Support Vector Machine (SVM) method. Palm fruit ripeness significantly affects the yield and quality of the oil produced. By utilizing image processing techniques, colour and texture features are extracted from the fruit images to support the classification process. The SVM model was trained with a dataset covering various ripeness levels, including unripe, ripe, overripe, and rotten. The evaluation results show the high accuracy of the SVM model in identifying ripeness levels. This study highlights the potential of machine learning technology in improving the productivity and quality of agricultural products. Support Vector Machine (SVM) is a machine learning method used to classify data into categories by finding the optimal dividing line between two classes, thereby maximizing the distance between the data from the two classes. SVM itself has proven to be very effective in detecting images, as evidenced by several studies such as detecting the ripeness level of melon fruit, each producing a model with an accuracy level above 86%. Thus, this study uses SVM suitable for use in detecting the ripeness level of oil palm fruit. This study produced an SVM model with an accuracy level of 93%.
Image-Based Classification of Indonesian Traditional Houses Using a Hybrid CNN-SVM Algorithm Ikhsan, M.; Rahardi, Majid
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.v9i5.10864

Abstract

The diversity of Indonesian traditional houses represents a cultural heritage that must be preserved. However, the lack of interest among younger generations and the difficulty in recognizing the distinctive architectural characteristics of traditional houses present challenges to preservation efforts. This study aims to develop an image classification model for Indonesian traditional houses using a hybrid CNN-SVM approach to improve recognition accuracy. The dataset consists of 3,919 images from five classes of traditional houses, namely gadang, joglo, panjang, tongkonan, and honai, with an 80% training split, 10% validation, and 10% testing. The data were processed through resizing, augmentation, and normalization before being trained using a CNN architecture with five convolutional layers as a feature extractor and an SVM serving as a multi-class classifier. The experimental results show that the hybrid CNN-SVM model achieved an accuracy of 96.68%, with consistently high precision, recall, and F1-score across all classes. These findings demonstrate that integrating CNN as a feature extractor and SVM as the final classifier can enhance the model’s generalization capability in distinguishing images of Indonesian traditional houses.

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