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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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Hybrid Sentiment Analysis of Public Perception on Indonesia’s Role in the Board of Peace Using Inset Lexicon and Support Vector Machine Rahmat Hidayat
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.v10i4.12507

Abstract

Although sentiment analysis is increasingly utilised in Indonesian social media, there is a paucity of rigorous studies assessing hybrid lexicon–machine learning frameworks in the realm of foreign policy debate. This study fills this gap by presenting a hybrid sentiment analysis model that combines the domain-specific InSet Lexicon with Support Vector Machine (SVM) classification to analyse public perception of Indonesia’s involvement in the Board of Peace program—a United States-led international peace initiative in the Middle East region. This research utilises computational sentiment modelling in international peace diplomacy, a largely neglected area in Indonesian text mining, in contrast to prior studies that primarily concentrate on product reviews or local policy issues. A dataset comprising 1,454 Twitter (X) postings was collected and subjected to systematic preprocessing including case folding, tokenisation, slang normalisation, stopword removal, and Sastrawi-based stemming. The preprocessed data was automatically annotated utilising the InSet lexicon to produce pseudo-labels and subsequently classified employing SVM with two feature representation methodologies: TF-IDF and Word2Vec. Experimental findings indicate that the TF-IDF-based hybrid model attained the highest classification accuracy of 84% on the testing dataset, correctly predicting 1,221 out of 1,454 instances, surpassing the Word2Vec method (69%). Detailed per-class evaluation using precision, recall, and F1-score revealed strong negative-class performance (F1 = 0.91) while the neutral class remained most challenging due to class imbalance, with a macro-F1 of 0.70. A single 60:40 train-test split was applied; the pseudo-labelled nature of the dataset is acknowledged as a limitation requiring future manual validation. The results indicate that statistical term-weighting techniques are more resilient than semantic embedding representations in the context of domain-specific Indonesian policy discourse. This study methodologically contributes by empirically comparing feature representation options within a hybrid lexicon–SVM framework and substantively by offering computational evidence of polarised public attitude toward Indonesia’s diplomatic engagement. The findings underscore the significance of domain-specific lexicons in enhancing sentiment classification efficacy in low-resource language settings.
Unraveling Insights from User Reviews of Sapawarga – Jabar Super Apps Through Topic Modeling Basrah Nasution; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
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.v10i4.12624

Abstract

This study conducted topic modeling on user reviews of the Sapawarga – Jabar Super App application on the Google Play Store. The dataset comprised user reviews submitted since the date application released until January 7, 2026 yielded 5.082 user reviews. Collected data processed through a series of data processing pipeline namely case folding, remove punctuation, normalization, tokenization, stop word removal and stemming. Topic modeling was conducted using Latent Dirichlet Allocation (LDA) algorithm grouped based on year of data. Topic distribution was visualized using the PyLDAvis to facilitate analysis and interpretation. This study found there are 9 topic after analysing year by year data namely Appreciation of the usefulness of the application in helping residents (1), Technical issues in terms of how to log in to the application (2), Application improvement proposal (3), Ease of information and citizen empowerment (4), General expressions of appreciation and criticism from citizens towards the application (5), Technical issues with payment features (6), Application error problem (7), User appreciation for the ease of vehicle tax payments from the application (8) and Criticism of application constraints in vehicle tax payments (9). The interpretation results indicate that the application is widely used by residents for online tax payments, particularly vehicle tax services, compared to other features or uses of the application. The findings of this study are expected to serve as a valuable insight for relevant stakeholders to improve the quality, performance, and overall effectiveness of the application as a public service platform.
Divorce Determinants Clustering in Indonesia Using K-Means and Agglomerative Hierarchical Methods (AHC) Danang Hilal Kurniawan; Nurul Alfajar Gumel; David Boby C. Nainggolan; M. Syamsuddin Wisnubroto; Fajri Farid
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.v10i4.12657

Abstract

This study evaluates the spatial typologies of marital dissolution across 34 Indonesian provinces through a dual algorithm computational framework. By applying a proportional ratio transformation, demographic bias generated by absolute population disparities is mathematically eliminated, enabling an objective spatial analysis of 13 specific divorce determinants. Agglomerative Hierarchical Clustering (AHC) and K-Means algorithms are integrated to assess structural validity. The optimal spatial partitioning is established at k=4, empirically supported by the inflection point on the Within Cluster Sum of Squares (WCSS) curve at 9.7238 and a stable Silhouette Score of 0.2417. Structural consistency between the hierarchical and partitional models is validated by an Adjusted Rand Index (ARI) of 0.7931. Statistical profiling based on centroid matrices stratifies the regions into four distinct typologies, Cluster 0 (Moderate Multi Factor) exhibiting moderately distributed determinants, Cluster 1 (High Risk Deviant Behavior) characterized by average centroid scores exceeding 0.70 for variables including drug abuse, gambling, and forced marriage, Cluster 2 (Economic Driven) demonstrating an absolute dominance in economic factors with a centroid score of 0.9152, and Cluster 3 (Conflict Pure) fundamentally driven by continuous disputes with a centroid score of 0.7787. These structural configurations mathematically verify that marital dissolution in Indonesia is geographically stratified by highly specific socioeconomic, behavioral, and relational variables.
A Blockchain-Based Traceability System To Mitigate Counterfeit Cosmetics In E-Commerce: Proposed Model And Experimental Validation Dinh Thien Tri; Hoang Gia Han; Phan-Anh-Huy Nguyen
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.v10i4.12787

Abstract

The rapid expansion of e-commerce has intensified the proliferation of counterfeit cosmetics, posing serious risks to consumer health and brand integrity, particularly in emerging markets such as Vietnam. While blockchain-based traceability has been studied extensively, existing solutions rarely integrate dual-layer verification, privacy-preserving mechanisms, and hybrid off-chain storage into a cohesive, experimentally validated prototype. This paper proposes VeriGlow, a blockchain-based decentralized application (DApp) that delivers transparent, tamper-proof, and user-verifiable traceability for cosmetic products throughout the supply chain. The system integrates: (1) Ethereum smart contracts written in Solidity ^0.8.0 with structured functions including createProduct, transferOwnership, and getProductJourney for immutable lifecycle management; (2) a hybrid IPFS-on-chain storage model using Pinata-hosted Content Identifiers (CIDs) to synchronize off-chain media assets with on-chain metadata, significantly reducing gas consumption; (3) a dual verification mechanism combining dynamic QR codes with physical Batch IDs to prevent QR cloning and relay attacks; and (4) a privacy-preserving historySnapshot mechanism that selectively conceals intermediate ownership records from public view while retaining full on-chain auditability. A functional prototype was developed using Solidity, Web3.js, and MetaMask and validated against six test scenarios on the Ethereum Sepolia testnet and Ganache local network. Experimental results demonstrate product creation gas costs in the range of 245,000–310,000 gas units (approximately 0.0014–0.0018 ETH), ownership transfer costs of 58,000–72,000 gas units (approximately 0.0008 ETH), and zero-cost read operations, comparing favorably with prior Ethereum-based traceability systems. Transaction confirmation times averaged 5–7 seconds for creation and 3–5 seconds for transfers on the local network. All six test scenarios passed with 100% success rate across ten repeated executions. These findings validate VeriGlow as a technically feasible and practically applicable solution for combating counterfeit cosmetics in e-commerce environments and offer a replicable framework for blockchain-based traceability in similar high-risk consumer sectors.
Problem Statement Formulation in Computing Research: A Systematic Review and a Proposed Computing-Specific Framework Tirivashe Mafuhure; Mampilo Phahlane; Charles Mbohwa
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.v10i4.12849

Abstract

The development of a well-formulated problem statement is still considered an integral but often insufficiently addressed step of the research process within computing research. Although essential for creating coherent research, selecting adequate methodology, and making scientific contributions, there is still no specific guidance on the development of computing-relevant research problem formulations, structuring, and formulation practices. This paper provides a systematic review of how the research problem is conceptualized, formulated, and operationalized in computing research. It aims to determine the common methodological problems of problem formulation, and to explore the methodological practices, recurrent strengths, weaknesses, and uniqueness of problem formulation in computing research. The systematic review was performed following the PRISMA protocol by analysing peer-reviewed journal articles published between 2014 and 2026 in major scientific databases such as IEEE Xplore, ScienceDirect, SpringerLink, and DOAJ. The search resulted in the identification of 57 papers. In turn, 16 articles met the inclusion and exclusion criteria after conducting screening, eligibility, and quality assessment. Thematic synthesis using open coding, axial coding, and theme development was utilized in order to analyse the data. Five recurring methodological weaknesses of problem statement formulation in computing research were identified are topic-problem confusion, solution orientation in early stages of problem formulation, poor empirical support, inadequate contextualization, and poor methodological alignment. Moreover, several significant discipline-specific differences in terms of problem conception were found. Some methodological approaches were detected in computing research. At the same time, their use appeared inconsistent and poorly coordinated across computing disciplines. Hence, the research proposes the Problem Statement Formulation Model (PSFM) as a structured methodological framework for problem formulation within computing research. PSFM consists of five consecutive steps that are context analysis, evidence-based gap recognition, problem structure creation, significance justification, and problem formulation. In contrast to existing generic frameworks, the presented model introduces an integrated methodology specifically adapted to the computing field. The paper concludes by discussing the findings' contribution to methodological guidance, postgraduate supervision, and training of researchers in computing disciplines.
Numerical Study of Secant Method for Finding the Optimum RF Coil Length in MRI Systems Using Python Tatik Juwariyah; Silvia Anggraeni; Henry Binsar Hamonangan Sitorus
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.v10i4.12989

Abstract

Determining the coil length l to achieve the desired target inductance Ltarget involves nonlinear and transcendental RF coil design equations. The ideal solenoid coil model does not align with real-world conditions, therefore, it is corrected using the Wheeler and Nagaoka models. From a mathematical perspective, these two models are nonlinear, making it highly difficult to determine the coil length analytically. This study presents the application of the Secant method to solve the coil length optimization across three models: Ideal Solenoid, Wheeler, and Nagaoka. By applying the Secant method with a case study involving a target inductance Ltarget = 10 µH, radius r = 5 cm, and number of coil turns N = 15, an optimum length of 17.71 cm was obtained with an error tolerance of 10-6. The convergence rates of the three models were evaluated to obtain error value data at each iteration. Based on the relationship between the convergence rate and iteration steps, the Wheeler and Nagaoka models yielded identical data at every iteration. The Secant method proved effective in solving nonlinear function root-finding cases, demonstrating a logarithmic convergence rate. This study is expected to provide a reliable computational framework for medical device engineers to ensure manufacturing accuracy in the design of RF coils in MRI systems.
AI-Augmented Fleet Intelligence in a GPS-Based Mobility SaaS: Predictive Fare Modeling, Demand Forecasting, and Driver Performance Scoring in Digikab Jai Chandra Mouli Langoju
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.v10i4.13000

Abstract

Small taxi fleet operators operating in emerging markets generate rich GPS telemetry through every completed trip, yet that data is rarely fed back into operational decisions. This study evaluates three AI-augmented capabilities, demand forecasting, predictive fare modeling, and driver performance scoring, deployed within Digikab, a SaaS platform built on GPS-enabled Android devices, across 47 independent fleet operators managing 284 vehicles over a six-month observation window. Using a quasi-experimental design with 29 AI-enabled operators and 18 controls on the same platform, this study applies gradient-boosted tree models (XGBoost/LightGBM) for zone-level demand forecasting, achieving a mean absolute error (MAE) of 2.14 trips per zone per hour (RMSE = 3.87, R² = 0.81) against held-out validation data. Demand elasticity estimation for fare optimization is formalized through an arc elasticity framework applied to operator-specific tariff history. Driver performance is decomposed into five weighted behavioral dimensions derived exclusively from the existing trip record, with coaching feedback generated via a large language model (LLM). Operators in the AI-enabled cohort demonstrated a 14.3% improvement in revenue per driver-hour (p = 0.003), a 22.4% reduction in idle time proportion (p = 0.007), an 8.9 percentage-point increase in trip completion rate (p = 0.014), and a 19.0% reduction in average passenger wait time (p = 0.012). The cold-start challenge for new operators is addressed through a progressive blending architecture that phases per-operator training signal in over a minimum three-month accumulation period. Findings demonstrate that meaningful predictive fleet intelligence is achievable at trip-history volumes in the thousands rather than millions, provided model architecture, output design, and operator interface are co-designed for the small-fleet context.
Aspect-Based Sentiment Analysis of Indonesian Electric Vehicles on Media Social Muh Hanafi Halik; Hazriani Hazriani; Nasrullah Nasrullah; Andani Achmad; Ingrid Nurtanio; Wardi Wardi
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.v10i4.13055

Abstract

The increasing adoption of electric vehicles (EVs) in Indonesia has generated diverse and unstructured public opinions across social media platforms. This study applies Aspect-Based Sentiment Analysis (ABSA) to classify Indonesian EV opinions across six predefined aspects: battery, design, price, performance, infrastructure, and general perception. The ABSA process was conducted using manual multi-label aspect-sentiment annotation rather than fully automatic aspect extraction, allowing one review to contain multiple EV aspects with corresponding sentiment labels. A total of 3,000 reviews were collected from YouTube, Instagram, and TikTok and used to evaluate three deep learning architectures: Bi-LSTM, IndoBERT, and Hybrid IndoBERT-BiLSTM. In the Hybrid architecture, IndoBERT was used as a Transformer-based contextual feature extractor, and the resulting contextual embeddings were passed into a Bi-LSTM layer to capture bidirectional sequential dependencies before final sentiment classification. The dataset was divided into training, validation, and testing sets using an 80:10:10 split. Model performance was measured using accuracy, precision, recall, and F1-score. The results show that IndoBERT achieved the highest average accuracy of 80.50%, followed by Hybrid IndoBERT-BiLSTM with 78.83% and Bi-LSTM with 71.67%. Although IndoBERT performed best overall, the Hybrid model showed competitive performance and better validation-loss stability in selected aspect-level contexts. These findings indicate the effectiveness of Transformer-based models for Indonesian EV sentiment analysis, while hybrid sequential modeling can provide a stable alternative for handling informal social media text.
Mapping Urban Vegetation Change Using RGB-Based K-Means Clustering In Baubau City Afriningsih Harjunianti; Syafruddin Syarif; Yuyun Wabula; Abdul Latief Arda; Supriadi Sahibu; Ingrid Nurtanio
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.v10i4.13056

Abstract

Urban ecological monitoring is increasingly essential in rapidly developing tropical regions. This study investigates land-cover changes and vegetation dynamics in Baubau City, Indonesia, from 2019 to 2025 using an RGB-based unsupervised classification framework. The primary aim is to develop a practical, cloud-enabled approach for analyzing multitemporal satellite imagery where access to multispectral data is limited. RGB composites were processed using K-Means clustering, Cosine Similarity matrices, and Elbow validation to extract dominant land-cover classes over time. The analysis revealed four major land-cover classes: dense vegetation, mixed vegetation, open land, and built-up areas. A significant ecological disturbance was identified in 2022, characterized by a sudden decline in vegetation and increased spectral similarity across clusters, suggesting both urban expansion and atmospheric interference. By 2023–2025, vegetation began to recover, though in a more fragmented pattern. Sub-clustering of dense vegetation confirmed this shift, with very dense classes giving way to transitional zones. The findings demonstrate the capability of RGB-only methods to produce ecologically meaningful classifications when paired with robust analytical techniques. The study also highlights the benefits of using cloud platforms like Google Earth Engine and Colab for accessible environmental monitoring. These insights support urban planning efforts and encourage the integration of lightweight remote sensing approaches into decision-making processes in resource-constrained settings.
Implementation of Stacking Ensemble Learning on Decision Tree Regressor for Food Commodity Price Prediction in Indonesia Nukman Solikhudin; Mega Novita; Ramadhan Renaldy
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.v10i4.13092

Abstract

Fluctuations in staple food prices across Indonesian regions exhibit complex, non-linear patterns vulnerable to market shocks. This study aims to construct an accurate, stable food price prediction model utilizing a Stacking Ensemble Learning approach. A raw dataset of 27,722 records from the National Food Agency was cleaned by removing invalid data and zero values, yielding 27,270 well-indexed observations. To address severe scale disparity between commodities and heteroscedasticity effects, a natural logarithm transformation was applied to the target variable. Time-series features, specifically Lag 1 and Moving Average 3, were locally constructed based on commodity-province groups to capture temporal dependencies. The proposed Stacking Ensemble model integrates four multi-architecture base learners Ridge Regression, AdaBoost, Gradient Boosting, and Extra Tree with a Decision Tree Regressor acting as the meta-learner. Model evaluation was conducted using a temporal split method with an 80:20 ratio to strictly prevent data leakage. Experimental results demonstrate that the proposed Stacking Ensemble model achieves superior performance on nominal test data compared to baseline models, securing an R^2of 0.895, RMSE of 2,531, and MAE of 1,461. Furthermore, the model proved highly robust in balancing bias and variance, yielding the smallest R^2Gap of 0.035. Model transparency analysis reveals a powerful temporal inertia, where historical features dominate the decision weight by up to 87.55%. However, per-commodity performance analysis highlights a performance limitation on subsidized commodities (Minyak Kita) due to data distortion caused by non-market Price Ceiling regulations. This study provides critical implications for food authorities to formulate data-driven, responsive market interventions.  

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