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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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Comparison of KNN and Naïve Bayes Classification Algorithms for Predicting Stunting in Toddlers in Banjaran District Fauzan Mu'taz, Rivaldy; Rosita, Ai
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.v9i5.9703

Abstract

Stunting is a chronic nutritional problem that seriously impacts child growth and development. This study aims to compare the performance of the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms in predicting stunting in toddlers in Banjaran District. The dataset consists of 12,000 toddler data points with three main features: age, gender, and height. The research employed a quantitative approach by applying machine learning algorithms. The SMOTE oversampling technique was applied only to the training data to avoid data leakage, and 5-fold cross-validation was used. A K-value of 3 was selected for the final KNN model based on validation curve analysis to prevent overfitting. The results show that KNN significantly outperformed Naïve Bayes across all evaluation metrics. The Naïve Bayes model yielded an accuracy of 67.50%, precision of 50.87%, recall of 61.38%, F1-score of 55.63%, specificity of 70.54%, and an AUC score of 75.71%. Meanwhile, the KNN (K=3) model achieved an accuracy of 99.11%, precision of 98.08%, recall of 99.25%, F1-score of 98.66%, specificity of 99.03%, and an AUC score of 99.65%. The performance difference between the two models was confirmed by McNemar's Test with a p-value < 0.05, indicating a statistically significant difference. The low performance of Naïve Bayes was attributed to the violation of the feature independence assumption, particularly the high correlation between age and height (r ≈ 0.87). In conclusion, KNN is the more appropriate algorithm for stunting prediction on this dataset. However, the limitation of features suggests the need for further research with additional variables and external validation before wider-scale implementation.
Innovative Mobile Application UI/UX for Gestari Waste Bank Administration Using Activity-Centered Design Charisma, Dhea Intan; Agusdin, Riza Prapascatama
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.v9i5.9913

Abstract

Bank Sampah Gestari, located in Dusun Gesikan, Panggungharjo, Sewon, Bantul, runs a routine household waste sorting program every “Minggu Legi.” In its operation, administrative officers still rely on manual data recording using notes and books, leading to data accumulation and inefficient recap processes, especially with the growing number of customers. The absence of a digital system to support the recording process presents a major challenge in achieving optimal administration. This study aims to design the User Interface (UI) and User Experience (UX) of a mobile-based administrative system for Bank Sampah Gestari using the Activity Centered Design approach. The approach focuses on the core activities performed by users during administrative tasks. The design process was informed by observations and interviews with administrative staff and was used to develop application flows and interfaces aligned with user needs and the bank’s business processes. The prototype was evaluated through two usability testing iterations, measuring five usability aspects: learnability, efficiency, memorability, errors, and satisfaction. Results showed notable improvements in all aspects. Learnability increased from 78% to 94%, efficiency from 0.0155 to 0.0440 goals/sec, memorability from 2.75 to 3.85, error rate decreased from 0.44 to 0.12, and satisfaction rose from 25.5 to 84.5. In conclusion, the proposed interface design significantly enhances ease of use, operational efficiency, and user satisfaction. The design can serve as a recommendation for developing a more structured, effective, and user-friendly digital administrative system for Bank Sampah Gestari.
Optimizing Digital Transformation Through AI and Cloud Technology Integration for Innovation in Big Data-Driven Information Systems Giantri, Lilik Tiara; Hudzaifah, Muhammad; Suciana, Ewin; Arief Christanto, Duta; Cahyadi, Dede
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.v9i5.10067

Abstract

Digital transformation has become critical for SMEs in emerging economies, yet integrated studies combining AI, cloud computing, and human factors remain scarce. This study addresses this gap by developing a holistic framework for Indonesian SMEs through a systematic literature review. Using PRISMA protocol, we analyzed 46 peer-reviewed articles (2020-2025) from Scopus, IEEE Xplore, and Google Scholar, with thematic synthesis in NVivo 12.  The proposed framework reveals three interdependent dimensions: (1) technological integration (AI-cloud-big data synergy), (2) process optimization (automation and analytics), and (3) human-digital leadership (competency and cultural readiness). Case studies show 35% operational efficiency gains but highlight infrastructure and skill gaps. This study contributes a novel integration of TOGAF and DCMM theories, offering policymakers a roadmap for SME digitalization while cautioning against one-size-fits-all solutions.
A Conceptual Hybrid AI-Cloud Model for Government Information Systems: A Structured Literature Review Widya, Tilly Raycitra; Cahyadi, Dede; Arief Christanto, Duta; Giantri, Lilik Tiara; Hudzaifah, Muhammad
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.v9i5.10082

Abstract

This study develops a comprehensive Hybrid AI-Cloud conceptual model to enhance government information systems through digital transformation. Using a systematic literature review (PRISMA protocol) of 51 publications (2020-2025) from Scopus, IEEE Xplore, and ScienceDirect, we identify four critical components: a hybrid architecture combining private and public clouds achieves 97.46% prediction accuracy but faces interoperability challenges in Indonesia where 85% of agencies use disparate systems; layered security with Hyperledger Fabric blockchain reduces data breaches by 72%, though 65% of Indonesian institutions lack CSIRT teams; user-centric designs score 76.88 on SUS scales yet encounter 71% civil servant resistance to AI automation; and organizational adoption strategies based on UTAUT frameworks are hindered by only 12% of civil servants having digital certifications. The research reveals Indonesia's significant gaps in system integration, cybersecurity preparedness, and digital literacy compared to global leaders like Estonia and Singapore. Successful implementation requires standardized cloud architectures with API gateways, mandatory cybersecurity audits, comprehensive digital training programs, and phased adoption roadmaps with change management components. While offering a holistic framework for digital government transformation, the study acknowledges limitations including literature bias toward developed nations and the need for local empirical validation through pilot projects, suggesting future research should incorporate ethical AI governance considerations alongside technical implementations.
Evaluating the Effectiveness of the SIGNAL Digital Samsat Application Using the PIECES Framework and Technical Testing Syakhila, Amanda; Fitria, Rahma; Yulisda, Desvina
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.v9i5.10086

Abstract

The SIGNAL application is a digital service provided by the Indonesian National Police's Traffic Corps (Korlantas Polri) to facilitate online STNK validation. However, several users have reported issues such as unsuccessful verification processes, delays in the delivery of physical documents, slow customer service responses, and login difficulties. This study aims to evaluate the performance of the SIGNAL application using the PIECES framework, which covers six dimensions: Performance, Information, Economic, Control, Efficiency, and Service. Data were collected through questionnaires distributed to 300 users of the SIGNAL application. Each questionnaire indicator was developed based on PIECES theory and statistically tested for validity and reliability using AVE and Composite Reliability. Descriptive and inferential analyses were conducted, including a one-sample t-test to assess user satisfaction. The results show that the average satisfaction score was 3.9 out of 5, with 76% of respondents expressing satisfaction or high satisfaction, 15% neutral, and 9% dissatisfied. The highest satisfaction was recorded in the Economic aspect (mean 4.06), while the lowest was in Control (mean 3.94). Technical testing using Apptim showed the app performed well, with an average response time of 2.4 seconds, CPU usage at 18%, memory usage at 170MB, and no crashes (0% error rate). These findings indicate that SIGNAL is generally effective and stable, though improvements are needed, particularly in service responsiveness and cross-device performance. This research contributes to the theoretical application of the PIECES model in evaluating public digital services and offers insights for improving the quality of e-government systems in Indonesia.
Green Technology Adoption: A Systematic Review of Key Trends and Challenges Cahyadi, Dede; Widya, Tilly Raycitra; Christanto, Duta Arief; Nasrullah, Muhammad Hudzaifah; Giantri, Lilik Tiara
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.v9i5.10164

Abstract

This study systematically reviews the trends, drivers, and barriers of green technology adoption, synthesizing insights from 81 articles indexed in the Scopus database from 2019 to 2025. Employing the PRISMA framework and bibliometric analysis, the research aims to provide a comprehensive overview of the academic landscape and offer evidence-based guidance for stakeholders. The findings reveal a growing, albeit limited, academic interest, with a research peak in 2024. Geographically, the discourse is led by developed nations and emerging economies, notably China, while research predominantly focuses on high-impact sectors such as transportation, energy, and manufacturing, leaving critical sectors like agriculture under-examined. Furthermore, this review provides a theoretical contribution by mapping empirical findings onto the Green Innovation Cycle and the Stimulus-Organism-Response (S-O-R) model, thereby strengthening the explanatory power of existing frameworks. We identify key challenges spanning infrastructure, policy, and user behavior, and provide specific recommendations for policymakers, industry leaders, and researchers to foster a more equitable and effective green transition. This research serves as a robust scientific foundation for future studies and strategic initiatives to accelerate global green technology adoption.
Development of MobileNetV2 for CT-Scan Lung Classification Using Transfer Learning Rajendra, Daffa Fadhil; Wardhana, Ajie Kusuma
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.v9i5.10282

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide, making early detection crucial for improving patient survival rates. This study proposes an automated classification approach based on deep learning using the MobileNetV2 architecture to identify three categories of lung CT scan images: normal, benign, and malignant. The dataset used is the augmented IQ-OTH/NCCD Lung Cancer Dataset, consisting of 3,609 images with a resolution of 224×224 pixels. All images underwent preprocessing steps including RGB conversion, pixel rescaling, and normalization. The MobileNetV2 model was modified by adding a GlobalAveragePooling2D layer, a dense layer, and dropout to reduce overfitting risk. Training was conducted for 28 epochs using the optimizer Adam, followed by evaluation using accuracy, precision, recall, and F1-score metrics. The model was tested on unseen data and validated using Stratified 5-Fold Cross Validation. The testing results showed an overall accuracy of 97%, with a perfect recall score (1.00) for the malignant class. The cross-validation yielded an average accuracy of 97.26% with a standard deviation of ±0.66%, indicating consistent model performance. Given its lightweight architecture and high accuracy, MobileNetV2 has the potential to be implemented as a decision support system in medical image analysis.
Evaluating the Usability of Canva Among University Students in Pekanbaru Using the WEBUSE Method Hidayat, Rahmat; Andriyani, Yanti; Husnah, Mirdatul; Sinaga, Irfan; Avelia, Ririn; Maisarah, Dina; Fentika, Winda; Wulandari, Nindya
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.v9i5.10351

Abstract

Canva is a design platform used to create social media graphics, presentations, posters, documents, and other visual content. This study aims to evaluate user satisfaction with the web-based version of Canva using the WEBUSE method, which covers four main aspects: Content, Organization and Readability, Navigation and Links, User Interface Design, and Performance and Effectiveness. Data were collected through an online questionnaire distributed to university students in the Pekanbaru area via WhatsApp and Instagram. A total of 65 respondents were obtained through the data collection and screening process. The evaluation results show that all aspects fall into the "Good" usability category, with the highest average score in User Interface Design (0.75) and the lowest in Performance and Effectiveness (0.70), resulting in an overall average score of 0.725. Therefore, Canva’s website is considered to have good usability according to user perceptions. This study is expected to serve as input for feature development and service quality improvement of Canva in the future.
Sentiment Classification Analysis of Tokopedia Reviews Using TF-IDF, SMOTE, and Traditional Machine Learning Models Barus, Herianta; Fajri, Ika Nur; Pristyanto, Yoga
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.v9i5.10524

Abstract

This study explores sentiment classification on Tokopedia user reviews using TF-IDF for feature extraction and SMOTE to handle class imbalance. From nearly one million raw reviews sourced from Kaggle ("E-Commerce Ratings and Reviews in Bahasa Indonesia"), a final set of 6,477 relevant entries was obtained after rigorous preprocessing, including case folding, noise removal (emojis, URLs, numbers), normalization to KBBI standards, tokenization, stopword removal, and stemming with Sastrawi. The dataset consisted of 5,213 positive and 1,264 negative reviews (80.4% positive). SMOTE balanced the classes to 10,426 reviews with a 1:1 ratio for training. Five traditional machine learning models were evaluated: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Random Forest. Assessments were based on accuracy, precision, recall, F1-score, ROC-AUC, and computational time, using an 80:20 stratified split and 5-fold cross-validation. Random Forest achieved the best overall performance (accuracy: 0.9163, F1-score: 0.9133, ROC-AUC: 0.9784), while tuned SVM (C=10, RBF kernel) attained the highest accuracy of 0.9473 and F1-score of 0.9321. Cross-validation on Naive Bayes showed consistent results with an average accuracy of 88.09%. Further analysis using Logistic Regression coefficients identified influential features: positive sentiment associated with words like "mantap", "mudah", and "sukses", while negative sentiment correlated with "kecewa", "parah", and "lemot". These insights provide practical value for Tokopedia's teams to enhance user experience, such as improving app speed and addressing complaints. The findings demonstrate the effectiveness and efficiency of traditional machine learning techniques for sentiment analysis in Bahasa Indonesia contexts.
Public Sentiment Analysis on Corruption Issues in Indonesia Using IndoBERT Fine-Tuning, Logistic Regression, and Linear SVM Kono, Maria Fatima; Fajri, Ika Nur; Pristyanto, Yoga
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.v9i5.10537

Abstract

Sentiment analysis is a method in Natural Language Processing (NLP) that aims to understand public perceptions based on textual data from social media. Opinions expressed in digital platforms play an important role as they reflect public trust and attitudes toward strategic issues in Indonesia. This study aims to compare the performance of three IndoBERT-based approaches for sentiment classification, namely IndoBERT with full fine-tuning, IndoBERT as a feature extractor combined with Logistic Regression, and IndoBERT as a feature extractor combined with Linear SVM. The dataset was collected through the Twitter API, consisting of 2,012 tweets, which after preprocessing and balancing resulted in 2,252 labeled data for positive and negative sentiments. The preprocessing stage included cleansing, normalization, tokenization, stopword removal, and stemming. The dataset was then split into 80% training data, 10% validation data, and 10% testing data. Experimental results show that IndoBERT with full fine-tuning achieved the best performance, with an accuracy of 82.67%, an F1-score of 82.35%, and an AUC value of 0.87. Logistic Regression and Linear SVM produced lower accuracies of 80.20% and 78.22%, respectively. These findings indicate that fine-tuned IndoBERT is more effective in capturing the semantic nuances of the Indonesian language, while the non fine-tuning approaches offer better computational efficiency at the cost of reduced accuracy. This study contributes to the development of NLP methods for the Indonesian language, particularly in sentiment analysis, and highlights the potential of transformer-based models for analyzing strategic issues in social media.

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