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Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 454 Documents
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PRODUCT SALES PREDICTION USING XGBOOST WITH FEATURE IMPORTANCE ANALYSIS FOR ADVERTISING MEDIA EVALUATION Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; Fanny Soewignyo
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.537

Abstract

Product sales prediction plays a crucial role in supporting data-driven marketing strategies and optimizing advertising expenditures. Although previous studies have demonstrated the effectiveness of machine learning techniques for sales forecasting, most of them primarily focus on prediction accuracy and provide limited insights into the contribution of individual advertising channels to sales performance. This limitation reduces the interpretability and practical value of predictive models for business decision-making. Therefore, this study proposes a product sales prediction framework using Linear Regression as a baseline model and XGBoost Regression combined with Feature Importance Analysis for advertising media evaluation. The novelty of this study lies in integrating predictive modeling and interpretable analysis within a single framework, enabling both accurate sales prediction and the identification of influential advertising factors. Hyperparameter optimization and five-fold cross validation were employed to improve model reliability and robustness. Experimental results show that Linear Regression outperformed XGBoost, achieving an R² score close to 1.0, while XGBoost achieved an R² score of 0.953 with a mean cross-validation R² score of 0.950, indicating stable predictive performance. Feature Importance Analysis revealed that Affiliate Marketing was the most influential factor, followed by Billboards and Social Media. These findings contribute to marketing analytics by providing interpretable insights that support advertising budget optimization and more effective data-driven business decision-making.
MACHINE LEARNING APPROACH FOR TRANSFORMER CONDITION ASSESSMENT USING K-MEANS CLUSTERING AND MULTI-CLASSIFIER MODELS Zulfiana Safitri Majid; Andarini Asri; Musfirah Putri Lukman; Wisna Saputri Alfira WS; Auliya Nabila
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.539

Abstract

Transformers play a critical role in power systems, yet their degradation is often difficult to detect due to complex influencing factors. Conventional diagnostic methods, such as Dissolved Gas Analysis (DGA), are time-consuming and rely heavily on expert interpretation. This study proposes a machine learning approach for transformer condition assessment by combining clustering and classification techniques. K-Means clustering is first applied to identify patterns in transformer condition data without prior labeling, with the optimal number of clusters determined as three using the Elbow Method. The resulting clusters are then used as pseudo-labels to train multiple classification models, including KNN, Decision Tree, SVM, Gradient Boosting, Extra Trees, and Voting Classifier. The results show that all models achieve high performance, with accuracy above 94%. Ensemble methods, particularly Gradient Boosting and Voting Classifier, achieve the best performance with an accuracy of 98.30%. These findings demonstrate that the proposed approach effectively improves transformer condition assessment and supports faster and more reliable maintenance decision-making.
ARRHYTHMIA DETECTION FROM IMBALANCED ECG DATA USING CNN, FOCAL LOSS, AND RR INTERVAL FEATURES WITH INTER-PATIENT EVALUATION Ilham Maulana; Nita Merlina
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.514

Abstract

Detection of arrhythmias from electrocardiogram (ECG) signals is crucial for early diagnosis of heart rhythm disorders. However, the imbalanced distribution of arrhythmia classes poses challenges for classification models. This study proposes a Convolutional Neural Network (CNN) model combined with Focal Loss and RR features to address class imbalance using an inter-patient evaluation approach. The dataset includes three classes: N (Normal), SVEB (Supraventricular Ectopic Beats), and VEB (Ventricular Ectopic Beats), divided into training (DS1), validation (DS1-Val), and testing (DS2) sets. The proposed CNN + Focal Loss + RR model achieved an accuracy of 95.75% and an F1-score of 95.19%. Additionally, Explainable AI (XAI) using LIME is applied to improve model interpretability by identifying important features influencing predictions. The results demonstrate that the proposed approach provides accurate and interpretable arrhythmia detection, supporting reliable medical decision-making. However, the model's performance on the minority SVEB class remains limited, with a recall of only 34.24%, indicating that SVEB beats are frequently misclassified as Normal due to morphological similarities and severe class imbalance. This limitation highlights the need for further improvements in minority-class detection for clinical applicability.
DEEP LEARNING-BASED INTRUSION DETECTION SYSTEMS: ARCHITECTURES, IMPLEMENTATIONS, AND CHALLENGES IN MODERN NETWORK ANOMALY DETECTION Ahmad Zulham Fahamsyah Havy Havy; Muhammad Faishol Amrulloh
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.552

Abstract

The increasing complexity of cyber threats has strengthened the need for adaptive network intrusion detection systems (IDS). This systematic literature review (SLR) synthesizes nine peer-reviewed studies published from 2024 to 2026 on deep learning (DL)-based network anomaly detection. The review follows a PRISMA 2020-aligned process covering database selection, eligibility screening, quality assessment, and structured data extraction. Five research questions examine DL architectures, preprocessing and class-imbalance handling, comparative performance, deployment challenges, and the basis for a conceptual framework. The synthesis compares preprocessing, classification strategy, Accuracy, Precision, Recall, and F1-Score where reported. The evidence shows that hybrid architectures can achieve very high benchmark performance, but the results are dataset- and experimental-setting dependent; therefore, the claim that hybrid models universally exceed 95% is not supported. Across the included studies, the recurring technical pattern is: representative data collection, preprocessing and imbalance mitigation, architecture selection according to spatial or temporal characteristics, and multi-metric evaluation. The revised framework extends this four-layer pipeline by explicitly integrating Explainable AI (XAI) and edge-oriented deployment as cross-cutting operational requirements. The review also identifies limitations in dataset realism, cross-dataset validation, reporting consistency, computational efficiency, and explainability.
HYBRID SOBEL-BASED CONVOLUTIONAL NEURAL NETWORK AND LEARNING VECTOR QUANTIZATION FOR CHILI SEED IMAGE CLASSIFICATION Gaudensia Asni Makul; Supatma Supatma
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.572

Abstract

This study develops an image classification system for chili pepper seeds using a hybrid approach that combines a manual Convolutional Neural Network (CNN) based on Sobel filters and Learning Vector Quantization (LVQ). The dataset consists of 900 images from three chili pepper varieties, namely gendot, raksasa, and rawit, with 300 images for each class captured using a smartphone camera. Unlike conventional CNN architectures that rely on trainable convolutional kernels, this study adopts predefined Sobel filters as fixed feature extractors combined with Learning Vector Quantization as the classifier, resulting in a lightweight classification framework that does not require a training process in the feature extraction stage. The CNN architecture employs three convolution-pooling layers (depth-3), producing feature maps of size 16×16, which are then transformed into 256-dimensional feature vectors through a flattening process. LVQ training was conducted for 30 epochs with an initial learning rate of 0.001 and five prototypes assigned to each class. The model was evaluated using a 5-fold cross-validation scheme and obtained a mean accuracy of 80.78% with a standard deviation of 4.07% across folds. Based on the combined confusion matrix from all five folds, the gendot class obtained a precision of 1.00 and a recall of 0.93, the raksasa class obtained a precision of 0.75, a recall of 0.69, and an F1-score of 0.72, and the rawit class obtained a precision of 0.71, a recall of 0.80, and an F1-score of 0.75, with most misclassifications occurring between the raksasa and rawit classes due to their morphological similarity. These findings show that the hybrid CNN–LVQ approach can be applied to chili seed classification without requiring specialized hardware or a pre-trained model.
COMPARISON OF PERFORMANCE AND COMPUTATIONAL COMPLEXITY OF CNN AND RESNET50 FOR PNEUMONIA CLASSIFICATION tam pran noto NOTO; supatman supatman
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.578

Abstract

Pneumonia is an acute infection of the lung tissue and the leading cause of death among children under five years of age worldwide. Diagnosis based on chest X-ray images is considered prone to misinterpretation, particularly in mild cases that appear similar to normal lung conditions. CNN is one of the most widely used deep learning models in medical image analysis; however, selecting the appropriate architecture between scratch-built models and transfer learning requires further evaluation, particularly regarding the trade-off between classification performance and computational complexity, an aspect that remains largely unaddressed in prior studies on medical image classification. This study aims to compare the classification performance and computational complexity of a CNN and a pre-trained ResNet50 model using transfer learning for binary pneumonia classification on chest X-ray images. The dataset used is the Chest X-Ray Images (Pneumonia) from Kaggle (Mooney, 2018), comprising 5,856 images: 1,583 in the NORMAL class and 4,273 in the PNEUMONIA class. Both models were trained under the same conditions and evaluated using the metrics accuracy, precision, recall, F1-score, ROC-AUC, and computational complexity, measured by model size, number of parameters, training time, and memory consumption. The results show that the CNN outperforms the ResNet50 across all classification metrics, achieving 98,81% accuracy, 99,07% precision, 99,30% recall, 99,18% F1-score, and 0,9978 AUC. In terms of computational complexity, the CNN has a smaller model size, shorter training time, and lower memory consumption
COMPARATIVE COMPLAINT TEXT CLASSIFICATION USING SVM, CNN, AND BERT WITH PRIORITY SCORING FOR NYC 311 SERVICE REQUESTS Putri Sabilla; Riska Dhenabayu
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.589

Abstract

Abstract Public complaint management systems like NYC 311 generate millions of service requests annually, posing significant challenges regarding manual categorization and resource prioritization. This study has two objectives: (1) to compare the classification performance of Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Bidirectional Encoder Representations from Transformers (BERT) on a large-scale city complaint dataset, and (2) to develop a complaint prioritization framework that combines normalized complaint frequency with average resolution time. The NYC 311 Service Request dataset, comprising 1,203,418 cleaned records, was utilized, with a sample of 20,000 records selected via stratified sampling. Pre-processing steps included converting text to lowercase, removing numbers and special characters, and normalizing whitespace. All models were evaluated without class weighting using a shared test set of 4,000 records. The results indicate that BERT achieved the highest macro F1-score (0.9167), outperforming SVM (0.8861) and CNN (0.8900), while all three models recorded similar aggregate accuracy rates (95.60%–95.83%). Both SVM and CNN yielded a recall of zero for the "Noise – Street/Sidewalk" category, whereas BERT was able to partially recognize it demonstrating that contextual language understanding is essential for distinguishing complaint categories with semantic ambiguity. Priority score analysis identified "Illegal Parking" (0.6007) and "HEAT/HOT WATER" (0.5437) as the highest-priority complaint types, while geographic analysis revealed that Brooklyn and the Bronx bear a disproportionately higher burden of complaints compared to other boroughs. These findings indicate that transformer-based classification combined with composite priority assessment can support data-driven municipal complaint management that accounts for geographical differences.
IMPLEMENTATION OF RETRIEVAL-AUGMENTED GENERATION BASED CHATBOT FOR EXPORT COMPANY CUSTOMER SERVICE AUTOMATION Jenri Dewany Widya Sunandar; Mutaqin Akbar
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.590

Abstract

This study discusses the issue of customer service in export companies, which is still limited in terms of service availability and the ability to handle recurring inquiries from international customers. Limitations in human resources result in responses to customer inquiries not being performed optimally, especially outside of operating hours. This study aims to design, implement, and evaluate a Retrieval-Augmented Generation (RAG)-based chatbot to support the automation of customer service in export companies using the Botika Agentic Platform. The study contributes by implementing and quantitatively evaluating a RAG-based chatbot on a Platform as a Service (PaaS) environment using the DeepEval framework with the LLM-as-a-Judge approach. The methods used in this study include four stages, namely data collection through interviews and analysis of company documents, system design, implementation through the Botika platform, and testing using the DeepEval framework with the LLM-as-a-Judge approach. The testing dataset consists of 93 questions covering four knowledge base domains, namely company profile, export and shipping procedures, partnership information, and product information. Evaluation results indicate that the chatbot achieved an average Answer Relevancy score of 0.98, Faithfulness of 1.00, and Contextual Relevancy of 0.88. The system performs well, with high Answer Relevancy and Faithfulness scores, while Contextual Relevancy is relatively lower, particularly in the product information domain. This is due to the dense structure of product documents, which contain many product variations within a single document, thereby affecting the accuracy of the retrieval process. Overall, the proposed RAG-based chatbot demonstrates its potential to support customer service automation in export companies by providing accurate and contextually grounded responses, while highlighting the importance of knowledge base organization for improving retrieval performance.
DESIGN OF AN IOT-BASED GREENHOUSE MICROCLIMATE MONITORING SYSTEM WITH REAL-TIME NOTIFICATION AND THRESHOLD ALERT Azyanul Muhammadiy; Dibyo Susanto; Agustina Rachmawardani; Anton Widodo
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.607

Abstract

Greenhouse microclimate monitoring is essential to maintain plant productivity, yet it is still commonly performed manually or with poorly integrated systems. This research designs and implements an IoT-based Greenhouse microclimate monitoring system using an ESP32 microcontroller integrated with five sensors (DHT22, BMP280, DS18B20, BH1750, and MQ-135), equipped with an LCD display, MicroSD data storage, a web dashboard, and real-time notifications via Telegram and buzzer as threshold alerts. Testing was carried out through sensor calibration/comparison against standard instruments, functional testing, and a 7-day field test at Hydroponic Greenhouse of KWT Mawar 8, Tangerang City, with most sensors well calibrated against the standard instruments. The field test shows high system reliability, with 98.81% data completeness and no significant downtime, and successfully detected microclimate conditions outside the ideal range, particularly air and water temperature during the day and humidity at night, which could potentially cause heat stress, root rot, and fungal disease risk in lettuce plants. The threshold alert feature via Telegram and buzzer is validated to function properly in providing early warnings under real operating conditions Keywords: IoT, Greenhouse, microclimate monitoring, ESP32, threshold alert, Telegram .
DEVELOPMENT OF A WEB-BASED EMPLOYEE ASSET INVENTORY INFORMATION SYSTEM FOR KEMENTERIAN PELINDUNGAN PEKERJA MIGRAN INDONESIA Siti Ernawati; Alfian Alesandro; Yuyun Anggraeni; Bagus Setia Aji
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.610

Abstract

Asset inventory management in government agencies requires a system that is able to ensure data accuracy, ease of monitoring, and presentation of information quickly and accurately. In the Kementerian Pelindungan Pekerja Migran Indonesia (KP2MI), the inventory process of assets used by employees is still carried out manually, potentially causing data duplication, recording errors, difficulty in tracking the whereabouts of assets, and delays in preparing reports. This study aims to develop a web-based employee asset inventory information system to improve the effectiveness, efficiency, and transparency of Barang Milik Negara (BMN) in the KP2MI environment. System development using the Waterfall method which includes the stages of needs analysis, design, implementation, testing, and maintenance. The system is developed using PHP programming language with MySQL database, while data collection is done through observation, interview, and literature study. Evaluation of the system is carried out using Black Box Testing to test the functionality of the system and the system Usability Scale (SUS) to measure the level of ease of use based on user perceptions. The test results showed that all 11 functional test scenarios were successfully executed, so that all system functions met the specified needs. In addition, the usability test obtained a SUS score of 82, which belongs to the category of Excellent with Grade A, indicating that the system has a very good level of ease of Use and can be accepted by users. The system developed is able to integrate asset data management, employee data, lending, returns, asset mutations, and presentation of reports in a centralized and real-time manner, thereby increasing data accuracy, accelerating report preparation, and supporting decision-making in asset management in the KP2MI environment.

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