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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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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction Wijaya, Ekaprana; Soeleman, Moch. Arief; Andono, Pulung Nurtantio
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8820

Abstract

This study aims to enhance price prediction accuracy using Artificial Neural Networks (ANN) by comparing three optimization methods: Stochastic Gradient Descent (SGD), Adam, and RMSprop. The research employs a systematic approach involving the design, training, and validation of ANN models optimized by these techniques. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R Square are utilized to evaluate the effectiveness of each method. The results indicate that the Adam optimization method outperforms the others, achieving the lowest MSE of 0.0000503 and the lowest MAE of 0.0046, resulting in an impressive R Square value of 0.9989. Adam's superior performance can be attributed to its adaptive learning rate mechanism, which effectively adjusts to the high volatility and noise characteristic of stock price data, enabling the model to converge faster and more accurately. In comparison, SGD produced a higher MSE of 0.0001208 and MAE of 0.0075, while RMSprop yielded an MSE of 0.0000726 and MAE of 0.0059. These findings highlight Adam's ability to significantly enhance the predictive capabilities of ANN, particularly in dynamic and complex datasets, making it a preferred choice for this application. The novelty of this research lies not only in its comparative analysis of various optimization methods within the ANN framework but also in the exploration of unique ANN features and their application to a specific stock price prediction case study, providing deeper insights into the practical implications of optimization strategies. This study lays the groundwork for future research by suggesting the exploration of additional optimization algorithms and more complex neural network architectures to further improve prediction accuracy.
Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification Giri, Gst. Ayu Vida Mastrika; Sanjaya ER, Ngurah Agus; Suhartana, I Ketut Gede
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8825

Abstract

The classification of mental states based on electroencephalogram (EEG) recordings has recently gained significant interest in cognitive monitoring and human-computer interaction fields. Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. Among deep learning models, Gated Recurrent Unit (GRU) models have established great potential for sequential EEG data analysis. The applications of the GRUs are less reviewed in tasks concerning classification cases of mental states compared to hybrid and convolutional models. Based on this paper, we will propose a method for developing a model based on the GRU network trained with raw EEG data in the classification tasks of mental states of concentration and relaxed conditions. We analyzed 400 EEG recordings taken from 10 subjects within a controlled environment and collected using the Muse EEG Headband. The mean, standard deviation, skewness, kurtosis, power spectral density, zero-crossing rate, and root mean square were extracted as statistical features from the raw EEG data. After parameter tuning, the GRU-based model achieved an excellent average accuracy value of 95.94% and also yielded precision, recall, and F1-scores within the range of 0.95 to 0.97 over 5-fold cross-validation. This shows that GRU works well in classifying mental states based on the EEG data.
Optimization of Urban Waste Collection Routes Using the Held-Karp Algorithm in a Web and Mobile-Based System Arsita, Tiara Juli; Lapatta, Nouval Trezandy; Joefri, Yuri Yudhaswana; Angreni, Dwi Shinta; Pratama, Septiano Anggun
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8832

Abstract

In 2023, the Environmental Agency of Palu City recorded a total waste production of 97,492 tons, of which 10.4% was plastic waste. The Palu City Government operates a fleet of garbage trucks on a predetermined collection schedule. However, garbage bins frequently overflow before their scheduled pickup, resulting in extended waste accumulation and inefficiency. This study proposes a web and mobile-based system to enhance waste management by integrating bin condition reporting and shortest route calculation for collecting full bins. The Held-Karp algorithm is utilized to address the Travelling Salesman Problem (TSP) for determining optimal collection routes. The system was developed using Golang, Flutter, ReactJS, and a MySQL database. API functionality was validated using Postman, and overall system functionality was tested using the black-box method. A case study involving 8 test points (1 starting point, 10 waste collection points, and 1 endpoint) demonstrated that the proposed system reduces travel time by up to 21.74%, costs by 22.29%, fuel consumption by 21.16%, and distance traveled by 21.16% compared to conventional methods. These results highlight the potential of the system to significantly optimize waste collection operations and support sustainable urban waste management practices.
Clustering Time Series Forecasting Model for Grouping Provinces in Indonesia Based on Granulated Sugar Prices Amatullah, Fida Fariha; Ilmani, Erdanisa Aghnia; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L. M. Risman Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8840

Abstract

Clustering time series is the process of organizing data into groups based on similarities in specific patterns. This research uses the prices of granulated sugar in each province of Indonesia. According to USDA reports, sugar consumption in Indonesia in 2023 reached 7.9 million tons. On April 26, 2024, the price of granulated sugar peaked in the Papua Mountains at Rp29,320 per kg, while the lowest price was recorded in the Riau Islands at Rp16,460 per kg. The research aims to cluster provinces based on the characteristics of granulated sugar prices and to use forecasting models for each group. Two groups were formed based on the price patterns of granulated sugar over time. The provinces of Papua and West Papua are in group 2, while the other 30 provinces are in group 1. The best model developed using the auto ARIMA method is ARIMA (2, 1, 0), with a MAPE value of 2.36% for cluster 1, and ARIMA (1, 1, 1), with a MAPE value of 2.59% for cluster 2. These values are less than 10%, indicating that the models built using the auto ARIMA method for clusters 1 and 2 are suitable for forecasting.
Automatic Fish Feeding and Temperature Control System for Aquariums Based on Internet of Things (IoT) Widyati, Made Ayu Sri; Anshori, Yusuf; Lamasitudju, Chairunnisa Ar.; Laila, Rahmah; Joefrie, Yuri Yudhaswana
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8847

Abstract

Keeping fish in aquariums has become one of the people's hobbies. An important factor in fish maintenance is the process of feeding and controlling the temperature of the aquarium. However, with various activities, fish care is often not carried out properly. This study develops an automatic system for feeding and controlling the temperature of the aquarium with goldfish as the test object. This study designs an automatic system to control the temperature and feeding in the aquarium using hardware such as a DS18B20 temperature sensor, load cell, and ultrasonic sensor. This system is controlled by ESP32 for reading sensor data and Arduino Uno for controlling the relay, cooling system, heater, and servo motor. ESP32 reads sensor data and sends it via MQTT to Node-red. Based on this data, the system regulates the temperature by activating the cooler (peltier and water pump) if the temperature is >28℃ and turning off the cooler when the temperature is <26℃. The heater is active if the temperature is <24℃ and stops when the temperature reaches 26℃. Feeding is carried out according to schedule, with servo 1 dropping feed into the load cell until the weight reaches the target weight. After that, servo 2 moves the feed into the aquarium. If the weight has not reached the target, servo 1 continues to be active. Based on the test, the average percentage of error in the temperature sensor is 0,08%, the weight sensor is 1.10%, and the ultrasonic sensor is 1.61%. This system successfully performs four times a day feeding and controls the temperature within the optimal range for goldfish, which is 24-28℃. The test results show that this system functions well and is in accordance with the research objectives.
Sentiment Analysis of Telegram App Reviews on Google Play Store Using the Support Vector Machine (SVM) Algorithm Nevrada, Nofsa Atia; Syaputra, Muhammad Adie
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8851

Abstract

This study aims to analyze the sentiment of Telegram application reviews on the Google Play Store using the Support Vector Machine (SVM) algorithm. From a total of 14,700,000 initial reviews, a sampling technique was carried out to obtain 400 review data, which then went through the pre-processing stage to produce 345 review data to be classified. The SVM model used showed good performance with an accuracy of 81.16%, precision in the positive class reached 93%, recall in the negative class of 94%, and an average f1-score of around 81%. However, there was a discrepancy between the high rating and the content of the review, which highlighted the existence of high-rated reviews that contained criticism or vice versa. The confusion matrix analysis also showed some misclassification, where reviews should be categorized as positive sentiment but detected as negative, and vice versa. This research is expected to provide valuable feedback for Telegram application developers to improve the quality of service, although the results of this analysis have not been fully discussed in practice. The limitation of this study is that it only tested reviews that used Indonesian, which limited the scope of the findings to the context of local users.
Implementation of the K-Nearest Neighbors (KNN) Regressor Method to Predict Toyota Used Car Prices Ghaisani, Mauhiba Salmaa; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8860

Abstract

The development of the automotive industry in Indonesia has experienced significant growth in recent decades, especially in the used car market segment. One of the used car brands that has high demand is Toyota, because it has a reliable reputation and quality. However, there are challenges that are often faced by sellers and buyers of used cars, namely in determining prices correctly and accurately. Incorrect pricing can be detrimental to one party, either the price is too high or too low. Prices that are too high can slow down the turnover of goods in the market. While low prices can cause sellers to experience losses. The purpose of this study is to help find good performance in determining the price of used Toyota cars. This study will use one of the Machine Learning methods, namely K-Nearest Neighbors Regressor. The KNN method is one method that can be used for classification and regression. In addition, this algorithm is a simple algorithm and can provide accurate prediction results based on its proximity to existing data. This study uses selected relevant features, namely model, year, kilometer, tax, mpg, and cc. The results of this study obtained MAE = 3.31686, MSE = 26.43640, RMSE = 5.14163, and R2-Score = 0.99501 using 90:10 data division and k = 1. This proves that KNN Regressor is an effective method in predicting the price of used Toyota cars. Therefore, the K-Nearest Neighbors (KNN) Regressor method is able to provide a fairly accurate price estimate with a minimal error rate.
Optimization of Distribution Routes Using the Genetic Algorithm in the Traveling Salesman Problem Naufal, Rahmad; Hasibuan, Muhammad Siddik
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8864

Abstract

Transportation plays a vital role in business operations, as it is essential for product distribution to maintain profitability. Optimizing distribution routes is crucial to reducing transportation costs, travel time, energy usage, and resource allocation while maximizing efficiency. Micro-entrepreneurs, particularly settled retailers, often face challenges in determining optimal travel routes, resulting in inefficiencies in product distribution. This issue is classified as a Traveling Salesman Problem (TSP), which involves finding the shortest possible route connecting several locations before returning to the starting point. To address this problem, this study applies a two-step approach: the greedy algorithm to provide an initial solution and the genetic algorithm for further optimization. The research employs both manual calculations and MATLAB 2018A software to solve the TSP. Results demonstrate that the optimized route reduces the travel distance by 1,260 meters compared to the initial solution, highlighting significant improvements in operational efficiency.
Evaluasi Usability E-Commerce yang Terintegrasi dengan Fan Community Platform Menggunakan Metode Cognitive Walkthrough Khalisatifa, Aida; Ibrahim, Ali
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8865

Abstract

This study aims to evaluate the usability and user experience of Weverse Shop e-commerce app after integration into Weverse app using the Cognitive Walkthrough and Post Study System Usability Questionnaire (PSSUQ) methods. Cognitive Walkthrough is used to identify usability issues from an expert perspective, while PSSUQ is used to quantitatively measure user experience through three subscales: System Usefulness, Information Quality, and Interface Quality. Participants in this study ran 7 task scenarios relevant to the application features. Based on the analysis results, the average scores for the PSSUQ subscales were 2.99 for System Usefulness, 2.98 for Information Quality, and 2.87 for Interface Quality, with an overall score of 2.95. These results indicate that the application interface still needs improvement, especially in the aspects of navigation and information delivery. This research provides recommendations for improvements to usability elements to increase user satisfaction.
Comparison of EfficientNet-B0 and ResNet-50 for Detecting Diseases in Cocoa Fruit Maylianti, Ni Putu; Wijayakusuma, I Gusti Ngurah Lanang; Arta Wiguna, I Putu Chandra
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8868

Abstract

Cocoa is a plant that is very susceptible to disease. One of the diseases that often attacks cocoa is black spots on the fruit. Detecting diseases in cocoa fruit is usually done manually by experts, which has limitations in providing information and is very expensive. this study proposes a model for detecting cocoa fruit diseases based on deep learning, namely convolution neural networks (CNN). This study compares CNN architectures, namely EfficientNetB0 and ResNet50 because these two architectures are very popular. EfficientNetB0 is known to be efficient in utilizing model parameters and the ability to achieve high accuracy, while ResNet50 uses Residual block recognition which allows deeper and more accurate model training. The dataset used is 3344 healthy cocoa fruit images, 943 black pod rot images and 103 pod borer images. From this study, the results for the accuracy of both methods are equally superior with an accuracy of 96% while for the precision of the EfficientNetB0 architecture is superior to ResNet50 with a value of 95.7% while for recall and f1-score ResNet50 is superior with a recall value of 94.7% and f1-score 93.3%. Based on the Confusion Matrix, it can be seen that ResNet50 is able to predict pod borer accurately so it can be concluded that in this study ResNet 50 is superior to EfficientNetB0. However, ResNet50 requires more parameters than EfficientNetB0 so ResNet50 requires a very large amount of data and when using a small amount of data EfficientNetB0 is more suitable for use.

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