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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 10 Documents
 1 
Search results for , issue "Vol 12, No 2 (2025): April 2025" : 10 Documents clear
Deteksi Kualitas Buah Sawo dengan Pendekatan Ekstraksi Fitur GLCM dan Algoritma Support Vector Machine Karisma Risma Fidiya; Muhammad Jauhar Vikri; Alif Yuanita Kartini
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8519

Abstract

The quality of sapodilla fruit is a crucial factor in ensuring product standards and consumer satisfaction. This study aims to detect the quality of sapodilla fruit using the Gray Level Co-occurrence Matrix (GLCM) method for texture feature extraction and Support Vector Machine (SVM) as the classification algorithm. A dataset of sapodilla fruit images was collected and processed using data augmentation techniques to enhance image variation. Extracted features, including contrast, homogeneity, energy, and correlation, were used as input for the SVM model. The model was developed using a train-test split approach and evaluated based on accuracy, precision, recall, and F1-score. Experimental results show that the proposed method successfully classifies sapodilla fruit into three categories—raw, ripe, and damaged—with an accuracy of 85%. This model was implemented in a MATLAB-based Graphical User Interface (GUI), enabling users to automatically classify sapodilla quality easily and efficiently.
Implementasi Deep Neural Network untuk Prediksi Harga Saham PT Bank Central Asia Tbk Muhammad Rakha Almasah; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8544

Abstract

Stock price prediction is one of the main challenges in financial market analysis. This study develops a Deep Neural Network (DNN) model using TensorFlow to predict the stock price of PT Bank Central Asia Tbk (BBCA) based on historical stock price data, the Jakarta Composite Index (IHSG), and the USD/IDR exchange rate. The model is optimized through hyperparameter tuning using KerasTuner with the Hyperband method, allowing for more efficient exploration of hyperparameter combinations. The tuning process yielded the best model configuration with a learning rate of 0.001269, four hidden layers, and the application of Batch Normalization, L2 kernel regularization, and dropout in each hidden layer.The model was evaluated using various batch sizes (4, 16, 32, 64, and 128) with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as performance metrics. The results show that batch size 128 provides the best performance, with MSE of 0.0094 and MAE of 0.0490 on the test data, indicating high accuracy and good generalization on unseen data. The best model was then implemented as an API using Flask, deployed to Google Cloud Run, and integrated with a Flutter-based mobile application. Testing confirmed that the API can handle requests quickly using TensorFlow and produce accurate predictions.Thus, this study successfully developed a DNN-based BBCA stock price prediction system that can be applied to data-driven investment decision-making.
Sistem Klasifikasi Tingkat Kerusakan Kunci Motor Menggunakan Random Forest dengan Hyperparameter Tuning Jalu Wira Yuda; Hastie Audytra; Nur Mahmudah
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8517

Abstract

Motorcycle key damage is often a problem for users, while the identification process still relies on technicians, which can be time-consuming and subjective. This study develops a classification system for motorcycle key damage levels using the Random Forest method with hyperparameter optimization. The dataset consists of 1,000 samples collected through observation and technician interviews, with data preprocessing using the SMOTE technique to address class imbalance. The model is trained and optimized with Random Forest using GridSearchCV and evaluated based on accuracy, precision, recall, and F1-score. The results show that the optimized Random Forest model achieves an accuracy of 85.5%, an improvement from 82% before tuning, enabling faster and more accurate identification of motorcycle key damage levels. The implementation of this system is expected to improve repair service efficiency and help users take action before the damage worsens.
Sentiment Analysis of Youtube Comments on Indonesian Presidential Candidates in 2024 using Naïve Bayes Classifier Nurbaiti Mahfudza; Muhammad Ihksan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8538

Abstract

The 2024 Indonesian presidential election is one of the most talked about topics on various social media platforms, including YouTube. The comments that appear on political-themed videos can reflect public opinion towards presidential candidates. This research aims to conduct sentiment analysis of YouTube comments related to Indonesian presidential candidates in 2024 using the Naïve Bayes Classifier method. This method was chosen due to its ability to classify text data effectively and efficiently. Data was collected from a number of relevant Kompas tv videos on YouTube, then text preprocessing stages such as data cleaning, tokenization, and stemming were performed. Next, the data was classified into three sentiment categories, namely positive, negative, and neutral. The research shows that the Naïve Bayes model is able to classify sentiment with sufficient accuracy.  This finding can provide an overview of public perceptions of each presidential candidate as well as input for interested parties in the fields of politics and public communication. The results of this study show that the naïve bayes classifier algorithm can analyze with an accuracy of 61 % in the evaluation process using confusion matrix. The results of this study indicate that the naïve bayes classifier algorithm can be an effective alternative for analyzing the sentiment of YouTube comments on presidential candidates.
Implementasi Time Series Forecasting dengan Algoritma LSTM untuk Pemantauan dan Prediksi Produktivitas Kelapa Sawit Berdasarkan Hasil Panen Ardian Pramana Putra; Aninda Muliani Harahap
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8495

Abstract

Palm oil productivity is a key factor in maintaining the stability and sustainability of Indonesia's agribusiness industry. The fluctuation in yield at PTPN IV Kebun Bah Birung Ulu, which increased from 43,308 tons in 2020 to 44,028 tons in 2022 and then decreased to 34,643 tons in 2024, highlights the need for a more accurate monitoring system. These fluctuations are influenced by weather, fertilizer usage, plant infections, and plant age. Manual record-keeping without digital system support also increases the risk of errors and complicates production monitoring. This study aims to develop a web-based palm oil productivity prediction system using the Long Short-Term Memory (LSTM) algorithm. Five years of daily historical data, including plant age, fertilizer usage, rainfall, infection rates, and harvest results per afdeling, were used as model input. The research process includes data collection, preprocessing with Min-Max normalization, data splitting into 80% training and 20% testing, and training the LSTM model with two LSTM layers, two dropout layers, and one Dense layer. Model evaluation using Root Mean Squared Error (RMSE) shows that the model can predict productivity with good accuracy, with the best RMSE for each target variable achieved at different epochs. The 2025 prediction results indicate a stable or declining trend influenced by plant age, fertilizer application, rainfall, and infection rates. The developed web-based system features real-time monitoring and data visualization, providing a more efficient solution for production management and strategic decision-making in palm oil plantations.
Aplikasi Absensi Berbasis Multiplatform Dengan Penerapan Location Based Service Dan Face Recognition Menggunakan Framework Flutter Esa Surya Anggraini; Muhamad Alda
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8521

Abstract

Manual attendance systems have many limitations, such as being susceptible to data manipulation, time consuming, and inefficient in managing attendance data. This study aims to build a Multiplatform-based attendance application implementing Location-Based Service (LBS) and Face Recognition technology by utilizing the Flutter framework. This application is designed to improve the accuracy, security, and efficiency of the attendance process at the Nur Adia Education Foundation. LBS technology is used to ensure that the user's location when taking attendance is in a predetermined area, while Face Recognition ensures that the user's face matches the facial data registered in the system. This study utilizes the Research and Development (R&D) method which begins with problem identification, needs analysis, system design, implementation, and application testing. The test results show that the application can validate the location and identity of the user quickly with a good level of accuracy. The Flutter framework enables cross-platform development with a user-friendly interface and optimal performance. This technology integration has successfully replaced the manual attendance method, providing a modern solution to support digital transformation in educational organizations. This research is expected to be the first step in adopting a technology-based system for more efficient and effective operational needs.
Detection of Diseases and Dry Leaves in Corn Plants Using YOLOv8 Aditya Dwi Putro W; Cahyo Prihantoro; Queenta Paradissa; Wahyu Nurfida A
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8504

Abstract

Corn plants can grow well in areas with hot or tropical temperatures as long as there is adequate rainfall and an adequate irrigation system. Corn is a strategic agricultural commodity that plays an important role in the economy, both on a national and global scale. According to data from the official website satudata.pertanian.go.id, the projection of corn production in Indonesia in the period 2020 to 2024 is estimated to experience a stable annual increase, ranging from 0.94% to 0.97%. However, during its life cycle from seed to seed, every part of the corn is susceptible to a number of diseases that can reduce the quantity and quality of the results. Therefore, the problem of disease is one of the factors that constrains the production and quality of seeds. In this study, detection of types of diseases and pests in corn plants was carried out using YOLOV8 technology as a form of innovation in corn agricultural intelligence. The dataset used in this study consists of four classes of corn leaf images, namely dry spots, blight, rust and healthy plants with a total of 1162 datasets. The dataset was taken at the same time using the POVA Pro5 smartphone. Based on the results of model training and evaluation, it was obtained that with a batch size of 32 and epoch 64, the precision value reached 0.67, recall 0.78, f1 score 0.67, Map0.5 0.701, and Map0.5:0.95 0.295. Meanwhile, with a batch size of 64 and epoch 100, the precision value increased to 0.75, recall 0.79, f1 score 0.75, Map0.5 0.792, and Map0.5:0.95 0.343. These findings indicate that the application of YOLOv8 technology has the potential to provide significant contributions to the development of smart farming systems, especially in efforts to detect early disturbances in corn plants automatically and efficiently. The availability of accurate information on the types of diseases and pests that attack corn plants allows farmers to respond quickly and appropriately, including through the selection of more targeted pesticide use or the application of organic control methods that are appropriate to field conditions.
Analisis Sentimen Pada Komentar Mengenai Kartu Indonesia Pintar Menggunakan Metode Naïve Bayes Dava Sindy; Sriani Sriani
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8528

Abstract

The Indonesia Smart Card (Kartu Indonesia Pintar – KIP) is a government program aimed at providing educational assistance to students from underprivileged families. This program seeks to improve access to education and increase learning opportunities for children, enabling them to complete their education up to the secondary or vocational level. In the digital era, public opinion regarding government policies, including KIP, is often expressed through social media platforms such as X. Sentiment analysis is a technique in natural language processing (NLP) used to identify, extract, and classify opinions from text. One of the commonly used algorithms for sentiment analysis is Naïve Bayes, which operates based on Bayes' Theorem with the assumption of feature independence. This algorithm is effective in text classification due to its simplicity and ability to handle large datasets.By utilizing the Naïve Bayes algorithm, sentiment analysis of the KIP program can provide deep insights into public responses. The results of this analysis can assist the government in evaluating policies, understanding public perceptions, and optimizing program implementation to ensure it effectively reaches its intended beneficiaries.
Kombinasi K-Nearest Neighbor dengan K-Means Clustering Klasifikasi Stunting pada Bayi Berbasis Website Muhammad Aprilsyah; Raissa Amanda Putri
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8498

Abstract

Stunting is a serious health issue caused by insufficient nutrition over an extended period, especially in young children. This study aims to develop a web-based stunting data classification system using a combination of K-Means Clustering and K-Nearest Neighbors (K-NN) algorithms. The dataset used is sourced from the Health Department of Medan City in 2021-2024, consisting of 683 data entries. The research process includes problem identification, data gathering conducted through observations and interviews, data preprocessing using StandardScaler, and splitting the dataset into 70% training and 30% testing datasets. The K-Means technique is utilized for data segmentation based on z-score values. The clustering results are then used as labels for classification with K-NN. The system implementation shows a classification result with a distribution of 6.9% for mild stunting, 25.8% for moderate stunting, and 67.3% for severe stunting. The results indicate that the combination of K-Means and K-NN produces more accurate classification compared to using a single method. This study is expected to assist the Health Department of Medan City in analyzing stunting data more efficiently and contribute to the future development of stunting classification systems.
Sistem Absensi Guru Berbasis Pengenalan Wajah Menggunakan Algoritma YOLOv8 Satria Putra Dharma Prayudha; Aditya Dwi Putro
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8511

Abstract

This research aims to design and implement a web-based teacher attendance system that utilizes facial recognition technology through the YOLOv8 algorithm as a solution to the conventional paper-based attendance system, which is prone to recording errors, data manipulation, and potential information loss. Data collection was conducted by recording short videos of 24 teachers and staff at SD Negeri 1 Purbalingga Lor under various lighting conditions and viewpoints, which were then converted into an image dataset using the Roboflow platform. The dataset was processed through several preprocessing stages including video-to-image conversion, image resizing, augmentation, and data splitting for training, validation, and testing purposes. The YOLOv8s model was chosen due to its ability to detect faces in real time with high accuracy, as demonstrated by training results showing an mAP of 98.6%, Precision of 97.8%, and Recall of 98.5%. The integration of the model into a backend Flask-based application enables the attendance process to be carried out automatically and in real time, while functional testing using the Black Box Testing method confirms that the face detection feature operates as designed, achieving an accuracy of 93% under optimal lighting conditions. Consequently, this research successfully presents an innovative digital solution that not only enhances the efficiency of attendance administration but also minimizes the risks of data manipulation and recording errors in educational environments.

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