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FIRMAN TEMPOLA
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INDONESIA
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 15 Documents
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Search results for , issue "Vol 8, No 3 (2025)" : 15 Documents clear
IMPLEMENTING SHA-256 IN BLOCKCHAIN FOR SECURE AND TRUSTED ONLINE TRANSACTIONS OF MSMEs Luis, Matthew; Dewantoro, Rico Wijaya; Crispin, Andrian Reinaldo; Putra, Adya Zizwan; Dharma, Abdi; Chrysia, Celine; Vagga, Cherry Piya
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.v8i3.10606

Abstract

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.
DECISION SUPPORT SYSTEM FOR OPTIMIZING FINISHED GOODS INVENTORY AT PT. HESED INDONESIA USING THE EOQ (ECONOMIC ORDER QUANTITY) METHOD Elbaraka, Keysa Kalina; Sipayung, Yoannes Romando
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.v8i3.10725

Abstract

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.
DETECTION AND CLASSIFICATION OF GRAM-STAINED BACTERIA IN MICROSCOPIC IMAGES USING YOLOV8 WITH CBAM Sanjaya, Karyna Budi; Wonohadidjojo, Daniel Martomanggolo
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.v8i3.10891

Abstract

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.
HUMAN DIGITAL TWIN MODELING FOR ADVANCING ARRHYTHMIA TREATMENT herman, herman; Annafii, Moch. Nasheh; Biddinika, Muhammad Kunta
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.v8i3.10656

Abstract

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
SECURE DOCUMENT NOTARIZATION: A BLOCKCHAIN-BASED DIGITAL SIGNATURE VERIFICATION SYSTEM Tio, Nicholas; Pribadi, Octara; Robet, Robet
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.v8i3.10811

Abstract

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
SENTIMENT ANALYSIS OF PUBLIC HEALTH APP REVIEWS USING INDOBERT AND XLM-ROBERTA: A STUDY ON SATUSEHAT MOBILE APP Ananda, Dimas; Budi, Indra; Santoso, Aris Budi; Qureshi, Ali Adil
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.v8i3.10083

Abstract

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
THE BEST TOURISM RECOMMENDATION SYSTEM IN YOGYAKARTA CITY WITH THE INTEGRATION OF MFEP AND K-MEANS CLUSTERING METHODS Setiya Putra, Yusuf Wahyu; Muqorobin, Muqorobin
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.v8i3.10670

Abstract

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.
IMPROVING INDONESIAN SPEECH EMOTION CLASSIFICATION USING MFCC AND BILSTM WITH AUDIO AUGMENTATION Septiyanto, Muhammad; Susanto, Eko Budi; Sugianti, Devi
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.v8i3.10820

Abstract

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.
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.

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