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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 287 Documents
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CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Fitriani, Lely Mustikasari Mahardhika; Litanianda, Yovi
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
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.

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