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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 1,006 Documents
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Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data Latuconsina, Muhammad Sidik; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12179

Abstract

Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively detecting positive patients. This study aims to compare the performance of two modern Gradient Boosting algorithms, LightGBM and CatBoost, in predicting diabetes risk. Random Forest and Logistic Regression algorithms were included as baseline models to benchmark effectiveness. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during the training data preprocessing stage. The dataset was sourced from the Kaggle public repository (Diabetes Prediction Dataset), comprising 100,000 patient medical records with clinical attributes such as age, body mass index (BMI), and HbA1c levels. Performance evaluation utilized Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) metrics. Experimental results demonstrated a tight competition, where LightGBM achieved the highest Accuracy of 97.16%. However, CatBoost demonstrated superior sensitivity (Recall) of 69.71% and the highest F1-Score of 80.48%. This makes CatBoost the most reliable model in minimizing False Negatives compared to LightGBM and Random Forest, whereas Logistic Regression showed the lowest performance. Furthermore, interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that HbA1c and blood glucose levels were the most dominant features in detection, validating the model's alignment with clinical diagnosis. This study concludes that the CatBoost algorithm combined with SMOTE offers a more sensitive, transparent, and efficient diabetes prediction for medical screening.
Opinion Mining of Pedometer Application Reviews on Google Play Store Using Fine-Tuned IndoBERT-Base Primono, Anggi; Sanjaya, Ucta Pradema
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12184

Abstract

User reviews on the Google Play Store provide valuable insights into user satisfaction and application performance. However, manual analysis of these reviews is inefficient due to large data volume and the informal characteristics of the Indonesian language. This study proposes an opinion mining approach using a fine-tuned IndoBERT-Base model to classify user sentiments into three classes: positive, neutral, and negative. A total of 1,665 reviews of a Pedometer application were collected, with 1,636 reviews retained after preprocessing. The dataset was divided into training, validation, and test sets using stratified sampling to preserve class distribution. Experimental results show that the proposed model achieves an accuracy of 94.51% and a weighted F1-score of 0.93 on the test set. Despite strong overall performance, the results indicate that class imbalance significantly affects the classification of neutral and negative sentiments. Error analysis reveals that ambiguous expressions and limited samples in minority classes remain challenging for the model. This study demonstrates that fine-tuned IndoBERT-Base is effective for sentiment analysis of Indonesian mobile application reviews while highlighting the importance of addressing imbalanced data in opinion mining tasks.
Forecasting Export Values in West Sumatra Using Backpropagation Neural Network Rahmawati, Desi; Martha, Zamahsary
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12199

Abstract

Export value is an important indicator in supporting regional economic growth. However, its movement tends to be volatile and non-linear, making it difficult to forecast using conventional statistical methods such as ARIMA. This study aims to forecast the export value of West Sumatra Province using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The data used consist of monthly export values from January 2006 to October 2025 obtained from Badan Pusat Statistik (BPS) of West Sumatra Province. The data were normalized and modified using the rolling window method, then divided into training and testing datasets. Several network architectures were evaluated through a trial-and-error process with variations in the number of neurons in the hidden layer. The best model was achieved with the BPNN(12,12,1) architecture, yielding a Mean Square Error (MSE) of 0.0236 and a Mean Absolute Percentage Error (MAPE) of 25.31%. The results indicate that the model is capable of capturing non-linear patterns and reasonably following the trend of the actual data. The selected model was then used to perform short-term forecasting of export values for the period from November 2025 to March 2026. The findings demonstrate that the Backpropagation Neural Network algorithm is effective for forecasting export values in West Sumatra Province. This study contributes theoretically by enriching the application of artificial intelligence in regional economic forecasting and practically by supporting data-driven policy formulation for export strategies in West Sumatra.
Classification Of Student Depression Using Support Vector Machine Modelling and Backward Elimination Sabar, Rohmat Abidin; Pajri, Afril Efan; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12203

Abstract

Depression among university students has become a serious mental health concern that can negatively affect academic performance and overall well-being. Early detection of Depression is essential to provide timely support and preventive interventions. This study proposes a machine learning approach to classify student Depression using a Support Vector Machine (SVM) combined with Backward Elimination (BE) for feature selection. The dataset used in this research was obtained from a public repository and consists of 502 student records with multiple psychological and demographic attributes. Data preprocessing included categorical encoding and Min–Max normalization, followed by an 80:20 split for training and testing. Experimental results show that the baseline SVM model achieved an accuracy of 0.9208, while the application of Backward Elimination improved the performance to 0.9604. In addition, precision, recall, and F1-score also showed notable improvements, indicating a reduction in misclassification, particularly for non-depressed students. These findings demonstrate that integrating feature selection with SVM can enhance classification performance and provide a more efficient model for supporting early Depression detection among university students.
Sentiment Analysis of the Free Nutritious Meal Program (MBG) on Social Media X (Twitter) Using K-Nearest Neighbor and Artificial Neural Network Hakim, Fernanda Amri; Prastya, Ifnu Wisma Dwi; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12205

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) is a national policy initiated by the Indonesian government to improve public nutritional status, particularly among children and vulnerable groups. Since its implementation, the program has generated extensive public discussion on social media, reflecting diverse opinions, support, and criticism. This study aims to analyze public sentiment toward the MBG program on social media X (Twitter) using machine learning-based text classification methods. A total of 9,038 Indonesian-language tweets were collected and processed through text preprocessing, semi-automatic sentiment labeling with manual validation, and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. Sentiments were classified into three categories: positive, neutral, and negative. The performance of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and ANN with class balancing using Synthetic Minority Over-Sampling Technique (ANN + SMOTE) was evaluated using accuracy, precision, recall, and F1-score metrics supported by confusion matrix analysis. The results indicate that the ANN + SMOTE model achieved the highest performance with an accuracy of 93.58%, outperforming ANN (92.59%) and KNN (86.28%). The sentiment distribution indicates that public opinion toward the MBG program is predominantly neutral (52.1%), followed by positive (40.0%) and negative (7.9%) sentiments. These findings suggest that while the MBG program is generally well received, negative sentiments provide important feedback related to program implementation and governance.
Optimization of Vehicle Routing Problem with Time Windows (VRPTW) with Hybrid Dragonfly Algorithm Approach on Delivery Routes Ramadhian Putra, Muhammad Rizky; Yunita, Yunita
Journal of Applied Informatics and Computing Vol. 10 No. 2 (2026): April 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i2.11937

Abstract

Efficient product distribution is a critical component of supply chain management, especially for small-scale business that operate under limited vehicle capacity and strict delivery time constraints. This research focused on solving the Vehicle Routing Problem with Time Windows (VRPTW) by applying a hybrid optimization strategy that integrates the Nearest Neighbor (NN) method and Dragonfly Algorithm (DA) to reduce total travel distance while ensuring compliance with capacity and time windows requirements. In that proposed approach, the Nearest Neighbor method is utilized to construct an initial feasible route based on proximity considerations, whereas the Dragonfly Algorithm is employed to enhance the route configuration through balanced exploration and exploitation processes. The effectiveness of the hybrid method is evaluated using real contribution data obtained from HoneyBee Bakery & Cake, a small-cake bakery enterprise located in Palembang, Indonesia. The experimental results indicate that the Nearest Neighbor method generates an initial route with a total distance of 72.54 km. After applying the hybrid NN–DA optimization, the total travel distance is reduced to 62.65 km, achieving a reduction of 9.89 km or an efficiency improvement of 13.64%, without increasing the number of vehicles used. Furthermore, the parameter sensitivity analysis reveals that variations in the number of dragonflies and iterations have a considerable impact on solution quality and convergence behavior. Overall, the findings confirm that the proposed hybrid method offers an effective and practical solution for VRPTW in real-world distribution contexts. Additionally, a web-based application is developed to support route optimization and data processing, enabling easier adoption by non-technical users in small-scale distribution environments.
Integration of Multi-Modal Sensors and Images for Monitoring Book Stock Inventory in an Internet of Things- Based Warehouse Fandi Ishadinata; Supriadi Sahibu; Zahir Zainuddin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11750

Abstract

Study This designing system supervision inventory stock books in a warehouse based on the Internet of Things (IoT), with combining multi-modal sensors and digital images . The system This developed For increase accuracy recording stock , reduce errors caused humans , as well as monitor condition goods in a way directly Components device hard used includes Raspberry Pi 5 as controller , loadcell sensor for measure weight , ultrasonic sensor For evaluate capacity , and Raspberry Pi camera for needs visual verification . The information generated will sent to the IoT platform via MQTT protocol and visualized with using Node-RED. Approach study following the Research and Development (R&D) model based on ADDIE, including stages analysis needs , design , development , implementation , and assessment system . The results of implementation show that system This capable monitor stock with precise and provide announcement automatic moment capacity storage reaching the minimum limit. The combination of multi-modal sensors and imagery allows manager warehouse For get information about weight , capacity , and appearance condition goods in a way simultaneously , so that decision For filling repeat can done more fast and accurate . Trial show that this IoT technology capable increase efficiency operational , pressing cost power work , and minimize risk lost goods , making them the right modern solution For management inventory in the warehouse.
Integration of YOLOv8 and IoT-Based LiDAR Sensors on Drones for Deforestation Area Detection and Reforestation Mapping Muh. Agus; Putri Ayu Maharani; Ilham Ali Marka M; Muhammad Aslam Al-Fadillah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11778

Abstract

Deforestation has caused significant ecosystem degradation and increased the need for more accurate and efficient monitoring systems and reforestation planning. This study develops an integrated system combining YOLOv8 and Internet of Things (IoT)-based LiDAR sensors on a drone platform to detect deforested areas and generate 3D mapping to support reforestation efforts. The dataset consists of 1,822 aerial images collected from public datasets and drone-mounted cameras under various lighting conditions and flight altitudes. The YOLOv8 model was trained using transfer learning with an input size of 640 × 640, a batch size of 16, a learning rate of 0.001, and 100 training epochs. The results demonstrate that the model achieved a precision of 93%, a recall of 90%, and an mAP@0.5 of 94%, while successfully performing real-time deforestation area detection on the drone platform. Integration with the LiDAR sensor produced 3D point cloud visualizations with mapping deviations of less than 2 meters. The developed system effectively supports the identification of priority areas for reforestation more rapidly, accurately, and efficiently than conventional manual survey methods.
Fuzzy Mamdani-Based Vegetable Crop Recommendation System with Historical Climate Pattern Analysis in Deli Serdang Regency Meryatul Husna; Mhd Ikhsan P Siregar; Fachry Ferdiansyah Sembiring; Arif Ridho Lubis
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11923

Abstract

Climate variability poses significant challenges to short-cycle vegetable farming, leading to crop failure and economic losses. This study develops a Decision Support System (DSS) to recommend suitable vegetable crops based on historical climate pattern analysis in Deli Serdang Regency. The system utilizes meteorological data from BMKG spanning January 2022 to December 2024, including average temperature, rainfall, and humidity. Historical pattern analysis employs a three-month rolling mean to predict climate conditions for the upcoming planting period. The Fuzzy Mamdani method is implemented as the inference engine to determine crop suitability scores by processing uncertainty in growing requirements. The system was tested across four planting periods (January, April, July, and October) and successfully generated differentiated recommendations with fuzzy scores ranging from 50% to 88%. Results demonstrate that the system effectively adapts recommendations to seasonal climate variations, providing farmers with data-driven decision support to reduce planting risks and improve crop success rates. Future enhancements include real-time climate data integration and expansion of input variables such as soil type and solar radiation intensity.
Layered Authentication Optimization in IoT-Based Package Receiving System using Voice-Trigger and PIN Verification Mulia Sulistiyono; Muhtar Efendi; Uyock Anggoro Saputro; Bernadhed Bernadhed
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12228

Abstract

The rapid growth of courier and e-commerce services has increased the risk of package delivery problems, including unattended deliveries, package theft, and fraudulent claims by unauthorized recipients. To address these issues, this paper proposes the design and implementation of an Internet of Things (IoT)–based package receiving system that integrates voice recognition, PIN-based authentication, and remote monitoring capabilities. The proposed system employs a voice recognition module as an initial access trigger, followed by a keypad-based PIN verification derived from the package tracking number to enhance access security. A servo-controlled locking mechanism is used to physically open and close the package container, while real-time notifications and remote control are provided through a Telegram bot. The system supports two access modes: local access using voice commands and PIN input, and remote access via Telegram-based commands. System development follows a prototyping approach, and performance evaluation is conducted through functional testing, connectivity testing, and voice recognition accuracy measurements. Experimental results show that the voice recognition module achieves an average recognition accuracy of 74%, with recognition performance decreasing as the distance between the sound source and the microphone increases. Connectivity testing indicates that network latency remains within an acceptable range for distances up to 20 meters. Functional testing confirms that the locking mechanism, authentication process, and notification system operate reliably under defined test scenarios. The results demonstrate that the proposed system can serve as a practical IoT-based solution for improving package reception security and monitoring. However, the system is intended as a supportive security mechanism and does not replace advanced authentication or surveillance systems.

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