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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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Super Encryption with Hill Cipher and Myszkowski Transposition for Android Multimedia Files Security Aldi Febriayanto; Eko Hari Rachmawanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13293

Abstract

This research aims to design, implement, and evaluate an Android-based data security application called Secure Vault using a super encryption method that combines Hill Cipher (substitution) and Myszkowski Transposition (transposition). The background of this research is motivated by the increasing number of cyberattacks on Android devices, particularly those targeting multimedia data. The method used is Research and Development with the Waterfall model. A total of 300 synthetic multimedia files (images, videos, documents, texts) were encrypted and decrypted using three methods: Hill Cipher alone, Myszkowski Transposition alone, and super encryption. Testing included computational time, Shannon entropy values, black box testing (15 scenarios), User Acceptance Test (UAT) with 30 respondents, and statistical analysis (ANOVA, Pearson correlation, paired t-test). The results showed that super encryption produced an average encryption time of 7.56 seconds and the highest entropy increase (0.92 bits/byte, approaching 92.8% of the maximum 8 bits/byte). The encryption/decryption success rate for valid files reached 100%, with a black box pass rate of 90.3%. The UAT obtained an average score of 3.90/5 (Good category). The Pearson correlation between file size and encryption time was very strong (r = 0.939, p < 0.001). The conclusion of this research is that super encryption effectively enhances multimedia data security on Android with high entropy and perfect functional success, although there is a trade-off in processing time that remains acceptable. The Secure Vault application is feasible for protecting personal data.
Performance Evaluation of Word2Vec and FastText Embeddings in a CNN-BiLSTM Model for Sentiment Classification of the LPDP Alumni Controversy Dwi Erzalianti; Joice Junansi Tandirerung; Cici Suhaeni; Bagus Sartono
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13298

Abstract

This study aims to analyze public sentiment toward the LPDP alumni controversy on social media using a deep learning approach. The research data consist of YouTube user comments related to the LPDP issue, which were processed through text preprocessing and automatically labeled using IndoBERT into three sentiment classes: negative, neutral, and positive. This study compares two text representation methods, namely Word2Vec and FastText, implemented within a hybrid CNN–BiLSTM architecture. In addition, data imbalance was addressed using class weighting and undersampling scenarios, while TF-IDF-based Logistic Regression was used as the baseline model. The results show that the baseline achieved an accuracy of 0.83 but was strongly biased toward the negative class as the majority class. The CNN–BiLSTM model improved the ability to detect minority classes. Under the class weighting scenario, FastText demonstrated more stable performance with an accuracy of 0.77 and a macro F1-score of 0.62. Under the undersampling scenario, Word2Vec was more stable, achieving an accuracy of 0.68 and a macro F1-score of 0.67. These findings indicate that both text representation and imbalance-handling strategies substantially affect sentiment classification performance.
The Application of Naïve Bayes Algorithm in Detecting Hoaxes on National News Portals in Indonesia Cut Rifa Salsabil; Nurdin Nurdin; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13306

Abstract

The rapid advancement of information technology in Indonesia has led to a massive spread of digital disinformation, commonly known as an infodemic. The inability to filter inaccurate information manually necessitates a reliable, automated hoax detection system. This study aims to implement and evaluate the Multinomial Naïve Bayes algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to classify news articles as either factual or hoax. The research utilizes a dataset of 2,910 Indonesian news articles published in 2025, collected from verified national news portals and fact-checking websites. The text data underwent comprehensive preprocessing—including case folding, cleansing, stopword removal, and stemming—before being evaluated using 5-Fold Cross-Validation and an 80:20 data split. Experimental results demonstrate that the Naïve Bayes model achieves highly stable and competitive performance, recording an accuracy of 93.81%, a precision of 93.84%, a recall of 93.81%, an F1-Score of 93.82%, and a 5-Fold Cross-Validation F1-Score of 93.39%. Notably, the algorithm exhibited a significantly low False Negative rate, missing only 15 hoax documents out of 582 test samples. Furthermore, the trained model was successfully integrated into a real-time, web-based user interface using Streamlit. This practical implementation provides an accessible and efficient initial screening tool for the general public and journalists to assist in verifying news authenticity, thereby supporting efforts to mitigate the impact of digital hoaxes.
Headline-Based Indonesian Political Misinformation Classification: A Comparative Study of Naive Bayes, SVM, CNN, and IndoBERT Models Ade Ariyo Yudanto; Naila Nabiha Qonita; Nur Aulia Maknunah; Inria Purwaningsih; Septian Rahardiantoro; Agus Mohamad Soleh
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13325

Abstract

Political misinformation spreads rapidly through digital media, making early detection increasingly important. This study evaluates headline-based Indonesian political misinformation classification using Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and IndoBERT. Headlines were selected because they represent the first information encountered by readers and enable rapid screening, although they may not fully represent the article content. The dataset consists of 5,132 Indonesian political news headlines collected from TurnBackHoax and DetikNews Politics. Leakage-aware preprocessing was applied to reduce explicit source-related cues before model training. Experimental results show that IndoBERT achieved the best performance with an accuracy of 90.25% and a Macro F1-score of 90.24%, outperforming the other evaluated models. Additional analyses, including McNemar statistical significance testing, computational cost comparison, and error analysis, showed that although IndoBERT achieved the highest predictive performance, its improvement over the other evaluated models was not statistically significant. Error analysis further revealed that several misclassification cases involved stylistic overlaps between misinformation and factual headlines, suggesting the possibility of residual source-style dependency and shortcut learning. Therefore, the proposed models should be interpreted as learning linguistic patterns associated with the constructed headline dataset rather than performing direct factual verification of news claims.
Modeling Car Driver Selection Using an Integrated Hybrid AHP-MOORA Approach Resiwara Cinantya Kusuma Kawekas; Heru Purwanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13333

Abstract

In transport management, traditional driver selection methods suffer from subjectivity and reactiveness, which directly increase traffic hazards, asset liabilities, and corporate operational expenditures. To address human cognitive limitations in concurrently analyzing complex multi criteria (physical, personality, and technical facets), this research deploys a computational Decision Support System (DSS) to secure objective, measurable quality standards and operational safety. The primary objective is to develop an integrated AHP-MOORA framework to optimize the recruitment pipeline. By leveraging AHP for criteria prioritization and MOORA for alternative evaluation, the proposed system yields optimized driver recommendations tailored to specific user requirements. The model incorporates seven distinct criteria: working experience, license classification, age, personality, educational background, residential proximity, and examination scores. Performance testing via a confusion matrix reveals that the constructed DSS delivers high robustness, achieving 87.5% Accuracy, 83.3% Recall, and a 90.9% F1-Score. Remarkably, the system attains a flawless 100% Precision rate, substantiating its optimal ability to yield highly targeted driver recommendations while entirely eliminating false-positive classifications.
Indonesia–English Bilingual Visual Question Answering Using Partial Fine-Tuning on a ViT-GPT2 Architecture Anas Anas; Hazriani Hazriani; Yuyun Yuyun; Syamsul Rijal; Tirta Chiantalia Sharief
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13336

Abstract

Visual Question Answering (VQA) is a multimodal task that integrates visual understanding and natural language processing to generate answers based on information contained in an image. Most existing VQA research focuses on the English language and general-domain datasets, limiting its applicability to bilingual environments and domain-specific scenarios. This study proposes an Indonesia–English bilingual VQA model based on the VisionEncoderDecoderModel architecture, which combines a Vision Transformer (ViT) as the visual encoder and an Indonesian GPT-2 model as the language decoder. Bilingual capability is achieved through the introduction of special language tokens, <id> and <en>. The model is trained using a combination of the bilingual VQAv2 and bilingual LosariVQAv1 datasets, representing general-domain and local tourism-domain knowledge, respectively. Four fine-tuning strategies are evaluated: Encoder Freeze, Full Fine-Tuning, Partial-4, and Partial-6. Experimental results show that the Partial-6 strategy achieves the best performance on LosariVQAv1, obtaining an Exact Match score of 26.67%, a BLEU score of 26.80%, and a CIDEr score of 292.67, while maintaining competitive performance on VQAv2 with an Exact Match score of 41.26%. Cross-language evaluation reveals only a small performance gap between Indonesian and English. The findings indicate that partial fine-tuning provides a better balance between generalization capability and domain adaptation than the other fine-tuning strategies evaluated in this study.
The Effect of Head-Up Display on Game Player’s Eye Movements: Systematic Literature Review Arief Hartanto; Hafiz Aziz Ahmad; Etika Vidyarini
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13341

Abstract

Head-up display (HUD) is a part of the game interface that is important as a medium of information for players. HUD generally takes the form of numerical, textual, or symbolic indicators in games. Interaction between game players and the HUD produces measurable differences in eye-movement behaviour, operationalized here as fixation count, fixation duration, and saccade velocity/amplitude. While several systematic literature reviews (SLRs) have examined eye tracking in games, none has isolated the HUD as the primary explanatory variable or reconciled the conflicting expert-novice findings that recur across this literature. This article addresses that gap by mapping research trends on HUD-driven eye movement and by synthesizing the recurring methodological practices used to collect eye-tracking data in this domain, to inform, rather than certify, future protocol design. Guided by the PRISMA approach, records were drawn from Google Scholar, Scopus, and ScienceDirect; ten studies published between 2015 and 2025 met the inclusion criteria and were analyzed through qualitative narrative synthesis (no meta-analysis was performed, given cross-study heterogeneity in eye trackers, tasks, and outcome metrics). The synthesis shows that fixation- and saccade-based outcomes diverge between expert and novice players in direction as well as magnitude, and that this divergence tracks genre-specific task demands (e.g., FPS/RTS versus MOBA versus racing) rather than a single uniform pattern. These findings are offered as an evidence base for eye-tracking protocol design in game-HUD research, not as a validated standard.
Performance and Security Analysis of MQTT over TLS on ESP32-Based IoT Systems M. Azhar Azhar; Muhamad Azwar; Ondi Asroni
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13355

Abstract

The Message Queuing Telemetry Transport (MQTT) protocol is widely used in Internet of Things (IoT) systems due to its lightweight communication mechanism and low resource consumption. However, MQTT does not provide built-in security features, making data transmission vulnerable to packet sniffing and data manipulation attacks. This study aims to analyze the impact of Transport Layer Security (TLS) implementation on the security and performance of MQTT communication in an ESP32-C3 Super Mini-based IoT system. An experimental method was employed by comparing MQTT communication without TLS (port 1883) and with TLS (port 8883) using the HiveMQ Cloud broker. Performance evaluation was conducted by measuring latency and packet size, while security analysis was performed through packet sniffing using Wireshark. The results show that TLS successfully encrypts MQTT payloads, preventing unauthorized access to transmitted data. However, TLS introduces communication overhead, increasing the average latency from 45.25 ms to 51.76 ms (14.39%) and the average packet size from 79 bytes to 103.2 bytes (30.63%) due to the TLS handshake process and encryption mechanism. Despite these increases, the communication performance remained stable and consistent, indicating an acceptable trade-off between security and performance. These findings demonstrate that TLS is a feasible solution for securing MQTT communication in ESP32-based IoT systems while maintaining reliable communication performance.
Automatic Classification and Semantic Retrieval for Low-Resource Parliamentary Archives: A Case Study of the DRC Senate Otshudiakoy Jean; Djungu Ahuka Saint Jean
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13356

Abstract

Parliamentary archives preserve the administrative, legal and political memory of legislative institutions. At the Senate of the Democratic Republic of the Congo, many documents are scanned PDF files, which makes manual indexing, classification and retrieval slow. This study does not propose a new algorithm. Its contribution is the applied integration of automatic classification and hybrid semantic retrieval in a French, OCR-based and low-resource parliamentary archive. The corpus contains 420 documents classified into five categories: orders, decrees, laws, ordinances and minutes. The models include TF-IDF with linear SVM, logistic regression and Naive Bayes, CamemBERT embeddings with logistic regression, and a weighted probabilistic fusion: P_fusion = alpha P_TFIDF + (1-alpha) P_CamemBERT, with alpha = 0.55. On the fixed test set, the hybrid model obtains accuracy = 0.786 and macro F1 = 0.815. Under stratified five-fold cross-validation, TF-IDF + linear SVM is the most stable lexical baseline, with accuracy = 0.821 +/- 0.052 and macro F1 = 0.834 +/- 0.050. These protocols are interpreted separately. For thirteen curated archival queries, hybrid retrieval performs best at k = 10, while TF-IDF obtains the highest MRR. The results show that lexical models are strong for categories with stable legal markers, whereas hybrid retrieval is useful for broader semantic access. The proposed system is a human-validated archival workflow, not a fully autonomous decision tool.
Web-Based Geographic Decision Support System for Boarding House Recommendation Using SMART and A* Muslimatul Magfirah; Rizal Rizal; Safwandi Safwandi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13361

Abstract

Selecting a boarding house is an important need for incoming students, especially in urban areas that offer many housing alternatives. The large number of available options often creates difficulties in choosing accommodation that matches user preferences. This study aims to develop a WebGIS-based boarding house recommendation system in Lhokseumawe by integrating the SMART method with the A* algorithm, where the travel distance generated by the A* algorithm is incorporated directly into the SMART evaluation process. The SMART method was used to evaluate and rank boarding house alternatives based on the criteria of price, distance, room size, bathroom type, Wi-Fi, air conditioning, and bed availability, while the A* algorithm was employed to determine the shortest travel route. The system was evaluated through calculation validation by comparing the SMART scores generated by the system with manual calculations, as well as through a preliminary user evaluation. The results showed that the system produced recommendation scores consistent with manual calculations, and the user evaluation indicated that the recommended boarding houses matched user preferences in the evaluated scenarios.

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