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INDONESIA
JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,099 Documents
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Pemodelan Intrusion Detection System Menggunakan CNN-LSTM dengan Selective SMOTE Untuk Deteksi Serangan Pada Data Tidak Seimbang Wicaksana, Arya Satrya; Robert Marco
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9662

Abstract

An Intrusion Detection System (IDS) is a critical component in safeguarding network security against increasingly complex cyberattacks. One of the main challenges in developing machine learning-based IDS is data imbalance, which reduces the model’s ability to detect attacks, particularly in the minority class. This study proposes improving the performance of deep learning-based IDS using a hybrid CNN-LSTM architecture combined with the prevalence ratio-based Selective SMOTE method, which is an oversampling approach performed selectively based on the imbalance level of each class. The dataset used is NSL-KDD, with preprocessing steps including categorical feature encoding and numerical feature normalization. Evaluation was conducted by comparing the baseline CNN–LSTM model and the CNN-LSTM with Selective SMOTE using the metrics accuracy, precision, recall, specificity, and F1-score. Experimental results show that the baseline model achieved an accuracy of 0.9947 with a macro recall of 0.8080, while the application of Selective SMOTE improved the macro recall to 0.8929 and the F1-score to 0.8515, particularly for minority classes such as U2R and R2L. Although accuracy decreased slightly to 0.9946, the specificity remained high at 0.9981 with a low false positive rate. These results indicate that the Selective SMOTE method is effective in improving attack detection sensitivity without significantly degrading the overall performance of the IDS system.
Analisis Perbandingan Model Machine Learning menggunakan Teknik Stratified K-Fold Cross Validation untuk Klasifikasi Penyakit Jantung Pratama, Avrilyan Putra Bintang; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9670

Abstract

Heart disease is one of the leading causes of death worldwide. Conventional approaches still have limitations, such as subjectivity in interpretation and relatively long analysis times. Therefore, this study proposes using machine learning to improve the accuracy of heart disease risk prediction by comparing the performance of Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and hyperparameter optimization using Stratified K-Fold Cross Validation with variations of K = 5, 10, 15, and 20. Model evaluation is conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics to comprehensively and objectively measure classification performance. The results show that the Random Forest algorithm achieves the best performance. At the optimal configuration of K = 15, the model attains an accuracy of 93.17%, a precision of 0.92, a recall of 0.95, an F1-score of 0.94, and an ROC-AUC of 0.97. In addition, this model minimizes classification errors, particularly False Negatives, making it more effective at identifying at-risk patients. The main contribution of this study is demonstrating that the combination of Random Forest and Stratified K-Fold Cross Validation can significantly improve classification performance and produce a model that is accurate, stable, and reliable for implementation in medical decision support systems.
Impementasi Metode SAW pada Sistem Pendukung Keputusan untuk Penentuan Prioritas Pendampingan Bayi Dibawah Dua Tahun di Kota Kediri Wawan Darmawan; Wildan Mahmud; Galuh Wilujeng Saraswati
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9671

Abstract

Determining the priority of assistance for children under two years old (Baduta) is a crucial step in accelerating stunting reduction programs. However, in practice, this process is still conducted manually, leading to potential subjectivity and inaccurate targeting. This study aims to implement the Simple Additive Weighting (SAW) method within a Decision Support System to determine the priority of Baduta assistance in Kediri City in an objective and systematic manner. The dataset consists of 300 Baduta, evaluated based on criteria including body weight, height, attendance at community health posts, and access to health service referrals. Preliminary results indicate that the normalization process successfully transformed the data into a standardized scale (0–1), enabling proportional comparison across all criteria. The weighting process shows that height and body weight have dominant contributions to the preference value calculation. The final results demonstrate that the system produces preference values ranging from 0.46 to 0.83, where lower values indicate higher priority for assistance. Furthermore, the system successfully identifies the top 10 priority Baduta as the primary targets for intervention. The implementation of the system also improves decision-making efficiency compared to manual methods and produces more consistent and objective rankings. The main contribution of this study lies in the integration of the SAW method into a dashboard-based system to support more accurate and measurable decision-making for prioritizing Baduta assistance.
Implementasi Algoritma Jaringan Saraf Tiruan untuk Prediksi Hasil Panen Kakao Desa Minanga Nur, Nahya; Sulfayanti; Andriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9694

Abstract

Cocoa production in Desa Minanga exhibits unstable fluctuations, which directly impact the uncertainty of farmers' incomes and pose challenges in financial planning. This study aims to apply the Backpropagation Artificial Neural Network (ANN) algorithm as an instrument for predicting cocoa yields. The research data covers the period from 2019 to 2023, comprising a total of 2,980 data points. The predictive model was developed using eight main criteria: land area, number of plants, seed type, fertilizer type, pest/disease attacks, mitigation efforts, rainfall levels, and previous harvest yields. Testing was conducted using three training-to-testing data ratio scenarios: 70:30, 80:20, and 90:10. These variations were used to evaluate the model's stability and performance in identifying data patterns. Furthermore, comprehensive testing was performed on various network architecture parameters, learning rates, and target errors, utilizing the binary sigmoid activation function to assess the model's stability and accuracy in recognizing complex data patterns. The research results indicate that the optimal model configuration was achieved with a 90:10 data ratio, a 7-6-1 network architecture, a learning rate of 0.4, and a target error of 0.0001. This model achieved an accuracy rate of 98.09% with a Mean Absolute Percentage Error (MAPE) of 1.91%. The findings demonstrate that the Backpropagation ANN is effective and can serve as an alternative method for predicting cocoa yields in Desa Minanga.
Implementasi BERT dan IndoRoBERTa untuk Klasifikasi Sentimen Opini Publik tentang Kecerdasan Buatan dalam Pendidikan di YouTube Muhammad Mutawakkil Alallah; Mokhammad Amin Hariyadi; Triyo Supriyatno; Riana, Eri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9697

Abstract

This study aims to analyze the sentiment of Indonesian-language YouTube comments related to artificial intelligence (AI) in the field of education using a Transformer-based deep learning approach, namely Bidirectional Encoder Representations from Transformers (BERT) and IndoRoBERTa. The research data were obtained through the YouTube Data API, consisting of 10,834 comments reflecting public opinion on the implementation of AI in education. The dataset was manually labeled into three sentiment categories: positive, neutral, and negative, followed by a preprocessing stage including case folding, text cleaning, text normalization, tokenization, and topic filtering. The experimental results show that in the baseline scenario without fine-tuning, both models achieved low performance with accuracy below 41%. However, after fine-tuning, a significant improvement was observed, where IndoRoBERTa achieved an accuracy of 91.54% with an F1-score of 0.9134, while BERT reached an accuracy of 84.63% with an F1-score of 0.8413. These results indicate that Transformer-based models adapted to specific datasets are capable of better capturing the contextual and linguistic characteristics of informal and unstructured Indonesian text. In addition, IndoRoBERTa demonstrates more stable performance in handling class imbalance compared to BERT. Overall, this study demonstrates that Transformer-based approaches are effective for sentiment analysis in social media and can be used to more accurately and comprehensively understand public perceptions of the implementation of artificial intelligence in education.
Analisis Sentimen Kritik Dan Saran Peserta Pelatihan Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine Ade Rudi Masa'id; Aldi Rosyid
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9700

Abstract

Participant feedback in the form of comments, criticisms, and suggestions is generally unstructured text data, which has not been optimally utilized as supporting information for training evaluation. This study aims to classify the sentiment of training participants’ feedback into positive, negative, and neutral categories using a machine learning approach, as well as to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms. In addition, this study examines the effect of hyperparameter optimization on the performance of sentiment analysis models. The research methodology includes text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), sentiment classification modeling, and performance evaluation using accuracy, precision, recall, and F1-score metrics. Model optimization is conducted through hyperparameter tuning using Grid Search and Random Search methods. The results show that, out of 487 participant feedback comments, the sentiment distribution is dominated by positive sentiment. Model evaluation indicates that the Support Vector Machine algorithm consistently achieves higher accuracy than Naïve Bayes, with the highest accuracy reaching 79.0%, while Naïve Bayes achieves a maximum accuracy of 65.3%. Furthermore, hyperparameter optimization is shown to improve the performance of both algorithms, particularly for Naïve Bayes. However, the findings are descriptive in nature and are intended to complement, rather than replace, existing survey-based methods or training management evaluation processes.
Pendekatan Machine Learning Berbasis Fitur Geospasial Imputasi Nilai AADT yang Hilang: Studi Kasus Texas Afridayani; Afrisawati; Rizky Maulidya Afifa; Cut Try Utari
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9703

Abstract

Annual Average Daily Traffic (AADT) is an important indicator in transportation planning and road network performance evaluation. However, missing values ​​due to sensor interference or unrecorded data can reduce the quality of the analysis. This study aims to estimate missing AADT values ​​using a geospatial feature-based machine learning approach with a case study in Texas, United States. Automatic Traffic Recorder (ATR) data is integrated with road network attributes from OpenStreetMap (OSM) through a spatial join process to produce features such as road classification, number of lanes, and speed limits. A Random Forest model is used to build an estimation model based on valid data (AADT > 0). The evaluation results show a coefficient of determination (R²) of 0.548, indicating that geospatial features can significantly explain variations in AADT. The imputation process successfully produced a dataset with a 100% convenience level and a spatial distribution pattern consistent with the road network hierarchy and metropolitan area. This approach demonstrates that the integration of spatial data and machine learning is effective in improving the integrity of traffic data to support data-driven decision making.
Sistem Pendukung Keputusan Berdasarkan Profil Keuangan Pribadi di Aplikasi SiKencur Haryoko, Andy
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9705

Abstract

Many individuals struggle to manage personal finances due to a lack of tools that provide advice tailored to their actual spending habits. This study develops and evaluates a Decision Support System (DSS) module within a mobile personal finance application called SiKencur. The system classifies users into four financial behavior profiles based on three transaction indicators computed over a six-month window. Testing with 30 users over four weeks yielded a classification accuracy of 82.6%, a usability score of 78.4 out of 100 (“Good” category), and a user satisfaction rating of 4.2 out of 5. The system's key advantage is the integration of personalized financial guidance directly within a single platform a capability not found in comparable applications.
Prediksi Harga Tandan Buah Segar Kelapa Sawit Menggunakan Fuzzy Mamdani Herman Santoso Pakpahan; Nilda Amriani; Yuniarta Basani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9707

Abstract

Fluctuations in the price of Fresh Fruit Bunches (FFB) of palm oil remain a problem in the pricing process because they are influenced by several dynamically changing economic factors. This study aims to apply the Mamdani Fuzzy Logic method to predict FFB prices by considering data uncertainty and relationships between variables that are not always linear. The variables used include the K Index, Crude Palm Oil (CPO) price, CPO ratio (R-CPO), palm kernel price (IS), and palm kernel ratio (R-IS). All variables are processed in a rule-based fuzzy inference system using MATLAB, then validated through a comparison between manual calculations and simulation results. The scientific contribution of this study lies in the application of a FFB price prediction system that integrates several key economic indicators in one Mamdani Fuzzy model, as well as the use of double validation to ensure the consistency of the calculation process. The test results show a Mean Absolute Percentage Error (MAPE) value of 18.7%, which is included in the good category and indicates that the model is able to follow the actual data pattern with an acceptable error rate. The comparison of the manual calculation result of 2499.13 and the MATLAB result of 2570 shows a relatively small difference, thus supporting the consistency of the method used. With its rule-based and easily traceable characteristics, the Fuzzy Mamdani method can be the basis for an interpretive and applicable decision support system to help predict FFB prices in the palm oil plantation sector.

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