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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 805 Documents
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Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms for Classifying the Maturity Level of Melon Salma, Leza Maulidina; Handayani, Irma
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.v10i1.11737

Abstract

This determination of melon fruit ripeness is an important factor in ensuring fruit quality in terms of taste, texture, and market value. However, ripeness assessment is still predominantly performed manually and relies on subjective judgement, which may lead to decreased product quality, inefficient distribution processes, and potential economic losses. Therefore, an automated approach for classifying melon ripeness levels is required. This study aims to analyze and compare the performance Support Vector Machine (SVM) and Naïve Bayes algorithms for melon ripeness classification based on digital images using Histogram of Oriented Gradients (HOG) feature extraction method. The dataset used in this study consists of 630 melon images divided into three ripeness classes, 209 unripe, 220 semi ripe, and 201 ripe images. The research process includes image preprocessing, data augmentation, feature extraction, model training, and performance evaluation. Experimental results show that the SVM with a Radial Basis Function (RBF) kernel, using parameter C=10 and the default value, achieves the highest classification accuracy of 94%, while the Naïve Bayes algorithm attains an accuracy of 65%. These results indicate that the SVM algorithm demonstrates superior classification performance compared to Naïve Bayes in determining melon ripeness levels.
Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning Prasetio, Erlanda; Handoko, L. Budi Handoko; Hastuti, Khafiiz
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.v10i1.11738

Abstract

Retrieval-Augmented Generation (RAG) AI chatbots have gained popularity for their effectiveness in producing accurate, fast, and reliable responses; however, they have faced critical challenges stemming from limited datasets, outdated documents, and noisy, unfiltered data. This study proposes a Multi-Agent Fallback in Retrieval Augmented Generation (MAF-RAG). This robust RAG system testing pipeline integrates three-phase retrieval, filtering, and re-ranking data, along with a multi-agent debating process to address these challenges. This study demonstrates MAF-RAG's ability to perform under a constrained dataset, using a near-deployment dataset of 1,100 real-world documents. The pipeline utilizes 150 testing queries, carefully selected to reflect real-world RAG-based chatbot scenarios. A sentence-transformers/all-MiniLM-L6-v encoder encodes various chunks of documents into a 384-dimensional query vector embedding, ensuring an accurate relationship between testing queries and vectorized documents. The results show that the proposed MAF-RAG significantly outperforms the baseline system, achieving a mean F1-score of 0.556, an improvement of 18.8% over the Enhanced Baseline (mean F1-score = 0.469) and a 70.0% improvement over the Legacy Baseline (mean F1-score = 0.327). MAF-RAG also achieves the highest success rate, with 78% of the queries, while other baseline systems manage only 34% and 62%, respectively. MAF-RAG also reduces the failure rate by 42.1%, significantly increasing system reliability. Although MAF-RAG exhibits an increase in latency of 4.9%, these trade-offs are outweighed by the significant improvements in system reliability and performance. These findings highlight the contribution of this study: by implementing a robust retrieval testing pipeline, system accuracy can be improved, reducing the presence of noisy and unfiltered documents, and increasing system performance even when faced with challenging and varied datasets, making it a suitable solution for a RAG-based chatbot system that faces dataset challenges.
Optimizing Sentiment Classification Models for TikTok Comments using Emotion-Based Preprocessing and Grid Search Ermawan, Bagas Restya; Prayoga, Mahendra Bayu; Fadhillah, Akmal Rafi; Utami, Ema
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.v10i1.11742

Abstract

TikTok has become one of the social media platforms with a significant influence on public opinion formation in Indonesia. However, the linguistic characteristics of user comments which are expressive, concise, and feature emotional forms like emojis, emoticons, and excessive capitalization pose challenges for sentiment analysis. This research aims to optimize a sentiment classification model for TikTok comments using emotion-based preprocessing and hyperparameter optimization via Grid Search. The dataset comprises 4,500 comments from three different time periods discussing the Minister of Finance, Purbaya Yudhi Sadewa. Three testing scenarios were conducted: common preprocessing, emotion-based preprocessing, and a combination of emotion-based preprocessing with Grid Search. The results indicate that emotion-based preprocessing improved model accuracy by 4–5%, while Grid Search optimization provided an additional increase of up to 3%, achieving a peak F1-score of 0.92 with the LightGBM model. Analysis based on sentiment time-periods reveals that across the three different periods, sentiments remained predominantly positive. The integration of emotion-based processing and parameter tuning proved effective in enhancing the model's ability to understand emotional variations in text and to map periodic changes in public sentiment on Indonesian-language social media.
Classification of Melinjo Fruit Ripeness Using a Convolutional Neural Network (CNN) Based on Digital Images Kurniawan, Anggi Ade; Wibowo, Setyoningsih; Mutiara Sari, Nur Latifah Dwi
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.v10i1.11744

Abstract

The subjective and ineffective manual sorting of melinjo fruit, a key ingredient in Indonesian cuisine, results in inconsistent quality. This study aims to create and evaluate an automated classification system for judging the ripeness of Gnetum gnemon fruit in order to solve these issues and offer a reliable and objective quality control method. The approach was to create a customized Deep Convolutional Neural Network (Deep-CNN). The model was trained and evaluated using a simple dataset of 5,718 images that were separated into three maturity levels: raw, semi-ripe, and fully ripe. Twenty percent of the dataset was used for testing, and the remaining 80 percent was used for training. Image preparation techniques like contrast enhancement and scaling to 250x250 pixels were applied in order to optimize the model's input data. The evaluation was conducted using a test dataset consisting of 1,144 photos. After eight epochs of training with the Adam optimizer, the generated Deep-CNN model demonstrated remarkable efficacy with a final classification accuracy of 99.91%. The high level of performance that remained throughout the testing phase confirmed the model's strong ability to accurately identify the ripeness levels of melinjo fruit. The previously unresolved issue of automated melinjo classification is addressed in this work with a tailored and remarkably accurate (99.91%) solution. Its primary advantage is that it provides a trustworthy and unbiased technical alternative to subjective hand sorting. This directly meets industry needs by offering a scalable method to improve operational effectiveness, standardize product quality, and increase the commercial value of melinjo fruit of agricultural products.
Recommendation System Yogyakarta Tourism Using TF-IDF and Cosine Similarity Methods with Word Normalizer Ulul Albab, Jauhar Fauzi; Rohman, Arif Nur
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.v10i1.11751

Abstract

The abundance of tourism information in Yogyakarta often overwhelms tourists due to non-standard text data. This research develops a tourism recommendation system using Content-Based Filtering by integrating TF-IDF and Cosine Similarity algorithms, enhanced with a Word Normalizer stage. The research method involves data preprocessing including case folding, filtering, stopword removal, and stemming combined with word normalization to standardize irregular spellings. Text feature representation is calculated using TF-IDF weighting, followed by measuring similarity between destinations through vector-based Cosine Similarity. The query testing of Pantai Parangtritis against Pantai Ngandong yielded the highest similarity score of 0.9397. System performance evaluation showed a Precision@5 of 0.84, Recall@5 of 0.10, and Mean Average Precision (MAP) of 0.81. In conclusion, strengthening the method with a Word Normalizer significantly improves the validity of top-ranked recommendations, enabling tourists to accurately find relevant attractions according to their preferences.
Knowledge Discovery in Sharia Mobile Banking Reviews Using Aspect-Based Sentiment Analysis and Machine Learning Nashiroh Ramadhani, Muthia; Ditha Tania, Ken; Afrina, Mira
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.v10i1.11753

Abstract

User reviews provide important insights into the quality of digital banking applications; however, their large volume makes manual analysis inefficient. This study applies Aspect-Based Sentiment Analysis (ABSA) to examine user perceptions of the BYOND by BSI application based on three aspects: interface, features and performance, and services. Three classification algorithms were compared: Naïve Bayes, Support Vector Machine (SVM), and Random Forest, evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. The results indicate that SVM and Naïve Bayes achieved the best performance, with an accuracy of 0.95 and an F1-score of 0.92, whereas Random Forest exhibited slightly lower performance with an F1-score of 0.89. Furthermore, sentiment analysis reveals the features and performance aspect exhibits the highest proportion of negative sentiment (39.6%), primarily associated with system reliability issues, login problems, transaction failures, and application instability. These findings demonstrate that ABSA can serve as an effective knowledge discovery approach for identifying critical functional issues and supporting data-driven prioritization in improving digital banking services, particularly within the context of sharia banking applications.
Exploring Public Opinion on the 'Makan Bergizi Gratis' Program on X: A Comparative Analysis of IndoBERT-Large and NusaBERT-Large Models Arunia, Aurelya Prameswari; Sani, Ramadhan Rakhmat; Dewi, Ika Novita; Sulistyono, MY Teguh
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.v10i1.11757

Abstract

Program Makan Bergizi Gratis (MBG) has triggered extensive discourse on social media platform X, which serves as a primary space for public expression of opinions toward government policies. This study aims to analyze public sentiment toward the MBG program while simultaneously comparing the performance of two prominent Transformer-based models, namely IndoBERT-Large and NusaBERT-Large. This research adopts a quantitative approach employing supervised learning on 10,201 Indonesian-language posts (tweets) collected through web scraping from February 2024 to September 2025. A total of 2,000 samples were manually annotated as ground truth, achieving a high level of inter-annotator reliability (Cohen’s Kappa, κ = 0.81). The experimental results indicate that IndoBERT-Large outperforms NusaBERT-Large, achieving an accuracy of 83.00%, while NusaBERT-Large demonstrates competitive performance with an accuracy of 80.50%. Substantively, public discourse is dominated by negative sentiment, accounting for nearly 50% of the total data, reflecting public concerns regarding budgetary constraints and technical implementation issues. Positive sentiment ranges between 33% and 36%, indicating sustained and substantial public support for the program. These findings confirm the effectiveness of Transformer-based models in accurately capturing the dynamics of public opinion toward government policies using social media data.
Implementation of Real-Time Swarm Drone Formation Using Firebase and MIT App Inventor with Interpolation-Based Control in Gazebo Wijaya, Ryan Satria; Soebhakti, Hendawan; Fatekha, Rifqi Amalya; Anggraini, Sarah
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.v10i1.11767

Abstract

This paper presents the implementation of a real-time swarm drone formation control system that leverages Firebase as the communication bridge and MIT App Inventor as the user interface. The simulation is conducted in the Gazebo environment with five quadcopter drones. Formation commands are sent from an Android application to Firebase, then processed by a Python-based ROS node to adjust drone positions. Four primary formations - line, triangle, circle, and star - are implemented, along with a dynamic mode enabling sequential transitions among multiple patterns. The integration of linear interpolation ensures smooth transitions, consistent timing, and stable hovering. Experimental results show an average response delay of 0.4–0.6 seconds and stable altitude at 3.5 meters. This approach demonstrates an intuitive and scalable swarm control method. Future enhancements may include telemetry feedback, Firebase authentication, and PID tuning to optimize control accuracy.
Performance Comparison of Naive Bayes and Support Vector Machine Methods in Music Genre Classification Based on Audio Signal Feature Extraction Using Mel-Frequency Cepstral Coefficients (MFCC) Naserwan, Kevin Putrayudha; Miraswan, Kanda Januar; Utari, Meylani
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.v10i1.11770

Abstract

Music genre classification has gained increasing attention with the emergence of digital music platforms. One of the relevant features extracted from audio signals is Mel-Frequency Cepstral Coefficients (MFCC), which is widely recognized as an effective technique. MFCC features are extracted at the frame level and aggregated at the clip level to represent each music track, making them suitable for audio-based classification tasks. This study applies Naïve Bayes and Support Vector Machine (SVM) algorithms for classification using the GTZAN dataset consisting of 1,000 audio files from 10 music genres, each with a duration of 30 seconds. The performance of these methods is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM demonstrates superior performance, achieving an accuracy of 95.25% compared to 50.37% for Naïve Bayes. This performance gap can be attributed to SVM’s ability to model non-linear decision boundaries and effectively handle high-dimensional MFCC feature spaces. The main contribution of this study lies in the systematic evaluation of multiple SVM kernel configurations and parameter settings, providing empirical insights into the robustness of classical machine learning methods for MFCC-based music genre classification. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction.
Performance Evaluation of Multi-Cloud Failover Using Domain Name System Zaelani, Cahya; Muhammad Suranegara, Galura
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.v10i1.11788

Abstract

This research implements and analyzes a multi-cloud failover system using DNS failover via AWS Route53 and Nginx reverse proxy load balancers on Google Cloud (primary) and Herza Cloud (backup), with AWS EC2 as shared backend web servers. An Ubuntu control node orchestrates deployments across these providers, enabling automatic traffic rerouting from the primary to secondary load balancer upon failure detection via health checks. Performance testing employed wrk benchmarking (4 threads, 250 connections, 300s) and Python monitoring scripts under baseline and failover scenarios with DNS TTLs of 30s, 60s, and 120s. Baseline yielded 2,291.81 req/s throughput, 108.42ms average latency, and 231.15ms p99 latency. Failover results showed TTL 30s optimal for reliability (152.65s downtime, 48.62% failed requests, 30.53s average recovery), outperforming TTL 60s (243.92s downtime, 83.48% failures due to health check mismatch) and TTL 120s (186.88s downtime) and TTL 30s is recommended for high availability in low-budget SMEs, balancing reduced downtime against DNS overhead. However, this approach is limited to small-scale infrastructure.

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