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Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 287 Documents
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COMPARING REGRESSION METHODS FOR ASSESSING AND PREDICTION THE IMPACT OF SALARY INCREASES ON EMPLOYEE PERFOMANCE Juanta, Palma; Djuli, Zachary; Tifanny, Tifanny; Sitanggang, Delima; Anita, Anita
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10098

Abstract

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
DESIGN OF A WEB-BASED VILLAGE INCOME MAPPING SYSTEM USING HIERARCHICAL CLUSTERING IN CILETUH GEOPARK Jannah, Gina Raodotul; Gustian, Dudih; Rosita, Moneyta Dholah
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10694

Abstract

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.
Performance Analysis of Machine Learning Model Combination for Spaceship Titanic Classification using Voting Classifier Wirawan, Haria; Robet, Robet; Hendrik, Jackri
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10866

Abstract

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.
EVALUATION OF MATURITY LEVEL INFORMATION SECURITY USING COBIT 2019 AND ISO/IEC 27001:2022 Artamevia, Zahrach; Triayudi, Agung
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10704

Abstract

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.
COMPARISON OF ECDSA DAN EDDSA ALGORITHMS IN BLOCKCHAIN-BASED HEALTH RECORDS SECURITY Alexander, Nicholas; Wijaya, Bayu Angga; Dewantoro, Rico Wijaya; Monalisa, Monalisa
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10709

Abstract

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.
Implementation of Semi-Supervised Learning with YOLOv11 for On-Shelf Availability Detection of Retail Avilba, Pandu; Kurniawardhani, Arrie; Fudholi, Dhomas Hatta
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10881

Abstract

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 Udin, Iwan La
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10783

Abstract

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.

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