JIKO (Jurnal Informatika dan Komputer) is a scientific journal that began to publish volume 1 number 1 in 2018, then in 2020 JIKO began to be published as many as 3, namely in April, August, and December. Journals with P-ISSN numbers 2614-8897 and E-ISSN 2656-1948 have been accredited SINTA 3 by the Ministry of higher education, science and technologywith the number 0173/C3/DT.05.00/2025. This accreditation is valid for 5 years, namely volume 7 number 1 in 2024 to volume 11 number 2 in 2028. The Digital Object Identifier (DOI) address of JIKO is http://dx.doi.org/10.33387/jiko. This journal contains the results of research on Intelligent Systems, Image Processing, Computer Systems, Social Informatics, Digital Forensic, Health Informatics, Game Media and Mobile Technology, Data Mining, Human and Computer Interaction, Machine Learning, Natural Language Processing, Virtual Reality, E-Learning and related fields of computer science. Manuscript submission could be done by registering and creating an account at http://ejournal.unkhair.ac.id/index.php/jiko or if you get a problem in the system, you can send it via email to the journal editorial at if_jiko@unkhair.ac.id The manuscripts are written following the author guidelines. No Year Acceptance Rate 1 2021 57% 2 2022 55% 3 2023 48% 4 2024 38%
SENTIMENT ANALYSIS OF PUBLIC HEALTH APP REVIEWS USING INDOBERT AND XLM-ROBERTA: A STUDY ON SATUSEHAT MOBILE APP
Sentiment analysis is a key method for deriving insights from user-generated content, particularly in evaluating public satisfaction with digital health services. This study conducts a comparative analysis of sentiment polarity classification models on 34,178 Indonesian-language reviews from SATUSEHAT Mobile, a national health application by the Indonesian Ministry of Health. The dataset was manually annotated into positive, neutral, and negative classes. Three model categories were evaluated: classical machine learning (Support Vector Machine, XGBoost), baseline neural networks (Multilayer Perceptron, Convolutional Neural Network), and pretrained transformer-based models (IndoBERT, XLM-RoBERTa). All models were trained using stratified 5-fold cross-validation and tested on a held-out set. Results show that transformer-based models significantly outperform others in all metrics. IndoBERT achieved the highest weighted F1-score (0.8555), followed closely by XLM-RoBERTa (0.8552). Despite the similar average performance, XLM-RoBERTa exhibited the lowest performance variance across folds, making it the most stable and effective model overall. Statistical validation using Friedman and Nemenyi tests confirmed these differences as significant. However, all models struggled with neutral sentiment detection due to data imbalance. Although computationally more expensive than IndoBERT, XLM-RoBERTa offers superior robustness for sentiment classification in Indonesian health-related text. These findings support the integration of transformer-based sentiment monitoring into public health dashboards to enable timely, data-driven service improvements
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COMPARING REGRESSION METHODS FOR ASSESSING AND PREDICTION THE IMPACT OF SALARY INCREASES ON EMPLOYEE PERFOMANCE
In today’s competitive digital era, data-driven decision-making is key to enhancing the efficiency of human resource management. One of the main challenges is objectively assessing the impact of salary increases on employee performance, which is often assumed to be a primary motivator but rarely proven quantitatively. This study conducts a comparative analysis of two data mining methods, Linear Regression and Decision Tree Regression, to assessing and predicting the impact of salary increases on employee performance. A case study was conducted at PT. Taipan Agro Mulia using the company’s internal historical data. The analysis shows that Linear Regression performed better with an R-Square value of 0.731 or 73.1%, indicating that 73.1% of the variation in employee performance can be explained by salary increases. In comparison, Decision Tree Regression achieved an R-Square value of 0.700 or 70.0%. Additionally, Linear Regression recorded lower prediction errors (MAE = 4.78; MSE = 38.60; RMSE = 6.21) than Decision Tree (MAE = 5.61; MSE = 66.41; RMSE = 8.15). These findings demonstrate that data analysis approaches can serve as a strong foundation for formulating strategic salary policies aimed at improving employee performance
EVALUATION OF MATURITY LEVEL INFORMATION SECURITY USING COBIT 2019 AND ISO/IEC 27001:2022
Information security plays a vital role in maintaining the reliability and continuity of business processes, particularly in the retail sector where data integrity is crucial for claim validation and payment systems. PT XYZ developed a Claim Management System to enhance transparency and efficiency in managing incentive claims. However, recurring challenges such as frequent data loss and weak access control disrupted operations and posed risks to business continuity. This study aims to evaluate the maturity level of information security management at PT XYZ to address these issues. COBIT 2019 was selected as the primary framework because it offers a structured and measurable approach for assessing IT governance maturity, while ISO/IEC 27001:2022 was applied to identify relevant security controls for further improvement. A descriptive comparative method was employed, utilizing questionnaires, interviews, and domain mapping. The findings indicate that PT XYZ achieved its targeted maturity level across all assessed domains, with some processes exceeding expectations. Although no significant gaps were identified, several recommendations were proposed, including regular business continuity and disaster recovery testing, integration of security controls into the ISMS, enhanced real time monitoring, and regulatory compliance mapping. The study concludes that combining COBIT 2019 and ISO/IEC 27001:2022 provides a comprehensive framework for strengthening IT governance and information security, with practical implications for improving organizational resilience.
IMPLEMENTING SHA-256 IN BLOCKCHAIN FOR SECURE AND TRUSTED ONLINE TRANSACTIONS OF MSMEs
The advancement of information technology has driven digital transformation in various sectors, including Micro, Small, and Medium Enterprises (MSMEs), which are vital to Indonesia's economy. However, local MSMEs still face challenges in online transactions, especially related to data security and low consumer trust. Issues like data manipulation, lack of transparency, and weak security systems hinder optimal digitalization. This study implements the SHA-256 cryptographic algorithm in a blockchain system to enhance security and trust in local MSMEs' online transactions. SHA-256 is chosen for its ability to produce unique, permanent, and tamper-resistant hashes. The system adopts a decentralized blockchain model, where transactions are recorded in encrypted, chronologically linked blocks. The testing results show that the SHA-256-based blockchain system functions effectively in maintaining data integrity and preventing manipulation. Black Box Testing confirmed that the system operates correctly from the user's perspective, including login validation, transaction recording, manipulation detection, and transaction history retrieval. White Box Testing validated the internal logic of the system, proving the correct implementation of SHA-256 hashing, block linking, Proof of Work (PoW), and transaction validation mechanisms. All test cases passed successfully, demonstrating that the system is stable, functional, and secure.
HUMAN DIGITAL TWIN MODELING FOR ADVANCING ARRHYTHMIA TREATMENT
Heart disease in all its forms remains a significant health threat. Arrhythmia is a type of heart disease whose diagnosis and treatment still primarily rely on conventional electrocardiogram-based diagnosis. However, this approach is limited, as it is reactive and captures cardiac conditions only at the time of electrocardiogram measurement, making it unable to continuously and individually monitor arrhythmia progression for each patient. This study explores digital twin technology and develops human digital twin models for the treatment of arrhythmia patients. The modeling framework integrates three core components: geometrical modeling, physical modeling, and data-driven modeling to represent the human heart and cardiovascular system in a digital environment. The output of this integrative process has been implemented in the initial prototype of the Human Digital Twin Cockpit, which is designed to treat arrhythmia. This prototype enhances the existing diagnosis and treatment, and also incorporates a proactive simulation system. Evaluation and system testing have successfully demonstrated their ability to integrate geometric data from medical imaging and physical data from electrophysiological sensors to predict arrhythmia in various scenarios
THE BEST TOURISM RECOMMENDATION SYSTEM IN YOGYAKARTA CITY WITH THE INTEGRATION OF MFEP AND K-MEANS CLUSTERING METHODS
Yogyakarta is one of the leading tourist destinations in Indonesia with diverse attractions, ranging from cultural, historical, to natural attrac-tions, which often pose challenges for tourists in determining tourist destinations according to their preferences. This study aims to develop a recommendation system for the best tourist destinations in Yogya-karta City through the integration of the Multifactor Evaluation Pro-cess (MFEP) and K-Means Clustering methods. MFEP is used to rank destinations based on five main criteria, namely location, accessibility, facilities, cost, and uniqueness, with weights obtained from the results of tourist preference surveys. The ranking results are then analyzed using K-Means Clustering to group destinations into three categories of tourism potential, namely high, medium, and low, based on addi-tional parameters such as strategic location, number of attractions, and number of visitors. The results of this study prove that the integration of MFEP and K-Means is able to produce fast, accurate, and informa-tive recommendations, and can be used by tourists and tourism manag-ers in strategic planning and decision-making. System functionality testing using the black box method shows that all features run as need-ed, while clustering quality testing using the Silhouette Coefficient method produces very good clustering quality with an average score of 0.9552.
DESIGN OF A WEB-BASED VILLAGE INCOME MAPPING SYSTEM USING HIERARCHICAL CLUSTERING IN CILETUH GEOPARK
Cikelat Village, located within the Ciletuh–Palabuhanratu UNESCO Global Geopark, has significant potential in agriculture, MSMEs, and tourism. However, the absence of a structured information system limits the optimal utilization of these resources. This study maps the income distribution of Cikelat residents using the Hierarchical Clustering method and designs a web-based information system as a decision-support tool. Data from 438 respondents were preprocessed, normalized, and analyzed using Ward’s linkage and Euclidean distance, producing a dendrogram that identified three distinct socio-economic clusters: (1) Cluster 1 (212 respondents, 48.4%) – educated, self-employed residents with moderate income and high technology adoption; (2) Cluster 2 (131 respondents, 29.9%) – predominantly farmers with low income but positive perceptions of the Geopark’s benefits; and (3) Cluster 3 (95 respondents, 21.7%) – low-income groups with limited education and technology use. ANOVA confirmed significant differences among clusters (p < 0.05). The system design follows the waterfall model and includes class diagrams and a prototype interface developed in Figma. Although still at the design stage, the proposed system provides a practical blueprint for future implementation and supports data-driven policymaking and sustainable rural development.
COMPARISON OF ECDSA DAN EDDSA ALGORITHMS IN BLOCKCHAIN-BASED HEALTH RECORDS SECURITY
The development of blockchain technology presents significant opportunities for the management of Electronic Health Records (EHR), owing to its decentralized, transparent, and tamper-resistant characteristics. However, security challenges remain, particularly regarding the use of the Elliptic Curve Digital Signature Algorithm (ECDSA), which, despite being compact and secure, has limitations in efficiency and potential vulnerabilities related to random nonce usage. This study aims to compare the effectiveness, efficiency, and security of ECDSA with the Edwards-curve Digital Signature Algorithm (EdDSA) in safeguarding the integrity and confidentiality of blockchain-based EHR systems. The research methodology involved simulations and evaluations of digital signature algorithms using an EHR dataset from Kaggle, focusing on performance testing, data validation, and the implementation of the Proof-of-Work (PoW) consensus mechanism. The results indicate that EdDSA outperforms ECDSA in terms of both speed and security. EdDSA achieved a signing time of 0.000180 seconds and a verification time of 0.000200 seconds, compared to ECDSA's 0.000962 seconds and 0.003204 seconds, respectively. While both algorithms successfully validated the data, neither was able to detect data alterations. From a blockchain perspective, PoW demonstrated high computational resistance, as evidenced by increased mining times—from 1,504 seconds for 4,000 blocks (difficulty target = 5) to 7,702 seconds for 20,000 blocks (difficulty target = 5)—thereby enhancing system integrity. Overall, EdDSA is considered more suitable for modern blockchain-based EHR implementations, although further research is needed to develop mechanisms for detecting data alteration.
DECISION SUPPORT SYSTEM FOR OPTIMIZING FINISHED GOODS INVENTORY AT PT. HESED INDONESIA USING THE EOQ (ECONOMIC ORDER QUANTITY) METHOD
This study aims to design and implement a Decision Support System (DSS) to optimize the management of finished goods inventory at PT. Hesed Indonesia using the Economic Order Quantity (EOQ) method. In the manufacturing industry, one of the main challenges in the supply chain is maintaining the availability of finished goods at optimal levels to avoid overstocking which results in excessive storage costs and understocking, which can impede distribution processes. To address this challenge, the EOQ method is employed for its effectiveness in determining optimal order quantities, annual demand, and per-unit storage costs. This research adopts a case study approach with a quantitative methodology. The data collected includes annual demand for finished goods, ordering costs, and storage costs provided by the company. The processed data using the EOQ formula serves as the basis for developing a system capable of generating recommendations for optimal order quantities and ordering frequencies. The DSS is designed to deliver timely and accurate information to assist managerial decision-making regarding inventory control. The results demonstrate that the implementation of the EOQ-based DSS significantly reduces total inventory costs and enhances the company’s operational efficiency. Moreover, the system facilitates data-driven decision-making and minimizes subjectivity in inventory management. With the implementation of this system, PT. Hesed Indonesia is expected to manage its finished goods inventory more effectively and adaptively in response to market demand fluctuations.
SECURE DOCUMENT NOTARIZATION: A BLOCKCHAIN-BASED DIGITAL SIGNATURE VERIFICATION SYSTEM
The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
IMPROVING INDONESIAN SPEECH EMOTION CLASSIFICATION USING MFCC AND BILSTM WITH AUDIO AUGMENTATION
Emotion classification from speech has become an important technology in the modern artificial intelligence era. However, research for the Indonesian language is still limited, with existing methods predominantly relying on conventional machine learning approaches that achieve a maximum accuracy of only 90%. These traditional methods face challenges in capturing complex temporal dependencies and bidirectional contextual patterns inherent in emotional speech, particularly for Indonesian prosodic characteristics. To address this limitation, this study uses a combination of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) model with audio augmentation techniques for Indonesian speech emotion classification. The IndoWaveSentiment dataset contains 300 audio recordings from 10 respondents with five emotion classes: neutral, happy, surprised, disgusted, and disappointed. Audio augmentation techniques with a 2:1 ratio using five methods generated 900 samples. MFCC feature extraction produced 40 coefficients that were processed using BiLSTM architecture with two bidirectional layers (256 and 128 units). The model was trained using Adam optimizer with early stopping. Research results show the highest accuracy of 93.33% with precision of 93.7%, recall of 93.3%, and F1-score of 93.3%. The "surprised" emotion achieved perfect performance (100%), while "happy" had the lowest accuracy (88.89%). This result surpasses previous benchmarks on the same dataset, which utilized Random Forest (90%) and Gradient Boosting (85%). This study demonstrates the effectiveness of combining MFCC, BiLSTM, and audio augmentation in capturing Indonesian speech emotion characteristics for the development of voice-based emotion recognition systems.
Performance Analysis of Machine Learning Model Combination for Spaceship Titanic Classification using Voting Classifier
The Spaceship Titanic dataset is fictional yet complex and challenging, featuring a mix of numerical and categorical features and missing values. This study aims to evaluate the performance of three machine learning model scenarios for classifying passenger status as “Transported” or “not”. The three scenarios implemented include linear-like models, a combination of the Top 5 Diverse models, and tree-based/ensemble models, each using a voting classifier approach. The voting model is employed because it can combine the strengths of multiple algorithms to reduce bias and variance, thus improving overall prediction accuracy and stability. The voting mechanism aggregates predictions from several base classifiers using two strategies: hard voting, which selects the majority class, and soft voting, which averages the predicted probabilities across models. The dataset was obtained from Kaggle and processed through several stages: data preprocessing, data splitting, model training, and evaluation. The evaluation results show that the tree-based/ensemble scenario achieved the highest accuracy of 90.38%, followed by the Top 5 Diverse model combination at 87.31% and the Linear-like model at 76.51%. Visualization using the confusion matrix, ROC Curve, and Feature importance analysis further supports the claim that ensemble models are superior at detecting complex classification patterns. These findings suggest that tree-based ensemble models provide the most optimal approach for classification tasks on a dataset like Spaceship Titanic.
Implementation of Semi-Supervised Learning with YOLOv11 for On-Shelf Availability Detection of Retail
On-Shelf Availability (OSA) is a critical aspect of retail operations that affects customer satisfaction and potential sales. Computer vision–based systems have emerged as a promising solution to monitor product availability on store shelves. However, their implementation faces the challenge of limited labeled data, which requires time-consuming manual annotation with precise bounding boxes. This study proposes a semi-supervised learning approach based on pseudo-labeling using the YOLOv11n architecture to address the scarcity of labeled data. We utilized a dataset of 918 retail product images with 174 classes, divided into four proportions of labeled data (20%, 40%, 60%, and 80%). The research stages included training a teacher model, generating pseudo-labels with a confidence threshold of 0.5, and training a student model using a combination of labeled and pseudo-labeled data. Experimental results show that this approach effectively improves detection performance. With 60% labeled data, the model achieved an mAP50 of 0.931 and an mAP50-95 of 0.864, along with high-quality pseudo-labels (F1-Score 0.727; IoU 0.819). This significant improvement indicates that pseudo-labels can enrich data variation without introducing excessive noise. The study demonstrates that semi-supervised learning can reduce dependence on large labeled datasets while offering a practical and efficient solution for OSA detection systems in retail environments
AN LSTM-BASED APPROACH FOR INDONESIAN NEWS CATEGORIZATION: PERFORMANCE ANALYSIS OF HYPERPARAMETER TUNING AND PREPROCESSING
News disseminated through internet-based systems or news portals is generally classified into specific categories, such as politics, sports, economy, entertainment, technology, health, and others. Currently, this categorization is performed manually, requiring a thorough reading of the entire news content. To address this inefficiency, an automatic classification system for Indonesian news articles is necessary to categorize them based on predetermined categories. This research employs a Natural Language Processing (NLP) approach and implements the Long Short-Term Memory (LSTM) architecture. The study was conducted using several testing scenarios, including (1) hyperparameter tuning of the learning rate to 0.01 and 0.001, (2) the application and omission of stemming, and (3) various dataset comparison ratios of 60:40, 70:30, 80:20, and 90:10. The evaluation utilized a dataset of 10,000 articles across 5 categories and was measured using accuracy, precision, recall, and f-measure metrics. From the three scenarios, seven training models were generated. The second model, with a learning rate of 0.001, without stemming, and a 90:10 dataset ratio, achieved the highest accuracy of 90.7%, with average precision, recall, and f-measure scores of 91%. The third and fourth models, which applied stemming, did not demonstrate a performance improvement, both yielding an accuracy of 89%. The fifth model, with a 60:40 dataset ratio, produced an accuracy of 90%, while the sixth and seventh models, with 70:30 and 80:20 ratios, resulted in accuracies of 79% and 88%, respectively.
DETECTION AND CLASSIFICATION OF GRAM-STAINED BACTERIA IN MICROSCOPIC IMAGES USING YOLOV8 WITH CBAM
Bloodstream infection accounts for approximately 11 million deaths annually, and yet conventional blood culture methods require 40-48 hours to complete pathogen identification which delays definitive therapeutic decisions. Gram staining does provide preliminary bacterial classification within hours, but manual interpretation still remains a labor-intensive task and is prone to variability. This study develops an automated bacterial detection and classification system by integrating CBAM into the YOLOv8 architecture. The model was trained on Gram-stained microscopic images across four bacterial categories: Gram-positive cocci, Gram-negative cocci, Gram-positive bacilli, and Gram-negative bacilli. Dataset preprocessing involved quality selection, noise reduction, and targeted augmentation to address severe class imbalances. The inclusion of CBAM improved feature discrimination and localization performance, with an increase of 1.4% in mAP@0.5:0.95 (from 70.8% to 72.2%). The proposed model also reduced cross-class misclassifications, particularly among morphologically similar cocci. These findings demonstrate that integrating lightweight attention mechanisms can enhance bacterial detection reliability in microscopic imaging and support the development of automated systems for faster, more consistent preliminary bacterial identification.
CLUSTERING ANALYSIS OF SIGNIFICANT WAVE HEIGHT DYNAMICS USING K-MEANS ALGORITHM IN THE SEMARANG–DEMAK COASTAL WATERS
Global climate change has led to an increase in the frequency and intensity of extreme events at sea, including in the Semarang-Demak coastal area. This region is highly vulnerable to the dynamics of Significant Wave Height (SWH), sea level rise, and coastal land subsidence. As a result, in addition to disrupting maritime navigation, frequent occurrences of tidal flooding (rob) have caused significant disturbances to economic activities and settlements in the coastal area. This study aims to develop a clustering model for SWH in the Semarang-Demak waters using the K-Means algorithm. The data used includes oceanographic and meteorological parameters from the Tanjung Emas Semarang Maritime Meteorological Station (BMKG) for the period 2019-2024. The clustering results show that K-Means successfully formed three clusters of sea waves representing calm, moderate, and high waves. Model evaluation using the Silhouette Score with a value of 0.725 and the Davies-Bouldin Index (DBI) of 0.425 indicates good performance, with K=3 as the optimal cluster. Temporal analysis reveals a clear seasonal pattern, where high energy conditions dominate during the west season (December-February), while calm conditions are prevalent during the east season (June-August). These findings provide a foundation for early warning systems and disaster risk management in this region, with further clustering tests using other algorithms and the need for improved data quality.