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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 24 Documents
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Search results for , issue "Vol 12, No 3 (2025): Juni 2025" : 24 Documents clear
Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Ilham, Ahmad; Suseno, Dimas Adi
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8696

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Design Development of the JekNyong Application Using the Design Thinking Method Pambudi, Wendri Tri; Wardhana, Ariq Cahya
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8539

Abstract

The JekNyong application is a platform that allows the people of Banyumas Regency to sell household waste for recycling. However, the application’s user adoption rate remains low, with only 3.33% of families using it and a rating of 3.4 on the Google Play Store. This is due to an unintuitive interface design and limited feature accessibility. An initial usability test showed a task success rate of 75%, a time-based efficiency of 0.0206 goals/second, and a System Usability Scale (SUS) score of 63. The design development process followed the Design Thinking methodology through the stages of empathy, problem definition, ideation, prototyping, and testing. Several improvements were made to navigation, feature accessibility, and app layout. The second round of testing revealed significant improvements: the task success rate increased to 95.83%, time-based efficiency rose to 0.0382 goals/second, and the SUS score jumped to 86. These results indicate that the design improvements successfully enhanced the application's effectiveness and efficiency in accessing features.
Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes Sumantri, Fithra Aditya; Chrisnanto, Yulison Herry; Melina, Melina
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8648

Abstract

The mental health of university students is a growing concern as academic, emotional, and social pressures contribute to increased psychological risks. This study aims to classify mental health risk levels Low, Medium, and High among students using the Naïve Bayes classification algorithm. A dataset consisting of 1,000 entries and 11 key variables was utilized, covering academic, psychological, and behavioral factors. The preprocessing stage included data cleaning, label encoding, normalization, and rule-based labeling to determine the target classes. Model training and testing were conducted using stratified data splitting to preserve class distribution. The initial model achieved a classification accuracy of 88,67%, with macro average F1-score of 0.87 and weighted average F1-score of 0.88. Grid Search optimization with k-fold cross-validation was applied but showed no significant improvement, indicating the model was already in optimal configuration. Furthermore, probabilistic analysis revealed that Sleep Quality and Study Stress Level were the most influential features in predicting mental health risks. The findings suggest that Naïve Bayes is effective for multi-class classification with interpretable results. This research contributes to early detection efforts and offers a foundation for targeted interventions in university mental health management.
Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8668

Abstract

This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.
Fuzzy Tsukamoto Untuk Merekomendasikan Pembelian Barang Berdasarkan Data Penjualan Purba, Siti Aisyah; Sriani, Sriani
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8631

Abstract

The development of the sports industry, especially in futsal shoe sales, requires an inventory management strategy that is able to anticipate fluctuations in market demand. The main problem in this study is how to overcome the uncertainty of futsal shoe demand caused by variables such as trends, competition seasons, and changing consumer preferences. This study aims to develop a fuzzy logic-based purchase recommendation system using the Fuzzy Tsukamoto method to improve stock management efficiency. This study uses a quantitative approach with fuzzy method stages consisting of fuzzification, rule formation, inference, and defuzzification. The tools used are MATLAB software that supports the creation of fuzzy inference systems and graphic modeling. The study was conducted at the Pasifik Club Sports Sibolga Futsal Shoe Store by processing 1,098 historical sales data. The results of the study showed that the system built was able to recommend futsal shoe purchases with good accuracy, indicated by the Mean Absolute Percentage Error (MAPE) value of 15%. This system not only provides a popularity value for each shoe variant, but also helps stores avoid excess or shortage of stock. Thus, the Fuzzy Tsukamoto method is proven to be feasible to be used as a decision-making tool in retail inventory management.
Analisis Klusterisasi Stunting Pada Balita Menggunakan Algoritma K-Medoids Untuk Mengidentifikasi Faktor Dominan Saragih, Leonardo; Pasaribu, Nanda Sabrina; Harefa, Novi Karlianti; Tajrin, Tajrin
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8713

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use.
Klasifikasi Penyakit Diabetes Menggunakan Pendekatan Pembelajaran Mesin dengan Model Non-linier Adi, Ilham Arif Kuncoro; Prabowo, Wahyu Aji Eko
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8586

Abstract

The increasing prevalence of diabetes mellitus highlights the need for accurate early detection methods. This study proposes a classification model for diabetes prediction using non-linear machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (K-NN). The dataset, obtained from Kaggle, includes clinical features such as glucose levels, BMI, blood pressure, and insulin. The methodology comprises data preprocessing, partitioning the data into training and testing sets, and evaluating the model’s using accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest algorithm achieved the highest performance, followed by SVM and K-NN. We attribute Random Forest’s superior performance to its robustness in handling complex patterns and minimizing overfitting. We expect this research to contribute to developing practical early detection tools for diabetes, thereby supporting timely and data-driven medical decision-making.
Identifikasi Polaritas Sikap Pengguna Aplikasi X terhadap Coretax di Indonesia Menggunakan Algoritma Naïve Bayes Prasilda, Dina Rahma; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8548

Abstract

The Core Tax Administration System (Coretax) was launched by the Directorate General of Taxes (DGT) in January 2025 as a technology-based integrated tax system. While its initial goal was to improve tax efficiency and compliance, Coretax faced technical challenges, including system errors, slow processing speed, and criticism from the public. The main platform used to address these challenges is the X app (formerly known as Twitter). This research aims to understand the public's views and responses to Coretax's services by analyzing user sentiment patterns seen on social media. The research identifies the polarity of user attitudes by utilizing natural language processing (NLP) and Naïve Bayes algorithms, applied to a dataset of 1,628 tweets collected between January and March 2025. The analyzed data reflects a wide range of public reactions that include both positive and negative opinions towards the Coretax implementation, both in terms of functionality and ease of use. The results show that the model has an accuracy rate of 93.07%, a precision value of 95%, a recall value of 96%, and an F1-Score value of 96%. The results of this study are expected to be able to provide precise mapping related to changes in public opinion towards Coretax, so that it can be a valuable source of information for application developers, policy makers in the field of taxation, and analysis in the technology sector in responding to the needs and expectations of society in the digital era.
Implementasi Metode Recurrent Neural Network Untuk Prediksi Kejang Pada Penderita Epilepsi Berdasarkan Data Electroenephalogram Febiyane, Raisya; Chrisnanto, Yulison Harry; Abdillah, Gunawan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8656

Abstract

Epilepsy is a chronic neurological disorder that causes patients to experience recurrent seizures. Seizures are one of the main symptoms of epilepsy, requiring medical treatment and close monitoring. A major challenge in epilepsy management is the difficulty in predicting when seizures will occur. Electroencephalogram (EEG) can detect seizures as it contains physiological information about brain neural activity. This study aims to predict seizures using a Recurrent Neural Network (RNN) method based on EEG data. Deep Learning is a branch of Machine Learning that uses artificial neural networks to solve problems involving large datasets. The data used in this research is the Epileptic Seizure Recognition dataset obtained from Kaggle. It consists of patient ID attributes, 178 numerical attributes representing EEG signals, and a label y indicating conditions during the recording, including eyes open, eyes closed, healthy brain, tumor location, and seizure activity. The deep learning model tested is a Recurrent Neural Network (RNN) designed to learn patterns in the data. Performance evaluation was conducted using metrics including accuracy, precision, recall, and F1-Score. Based on the application of the RNN method and testing using EEG data, the best condition was achieved with a three-layer Long Short-Term Memory architecture and optimal training parameters, resulting in a seizure prediction accuracy of 98.6%. This result demonstrates that the model is capable of effectively and efficiently predicting the likelihood of seizure occurrences.

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