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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 1,069 Documents
 88   89   90   91   92 
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Sistem Deteksi Dini Gangguan Mental Menggunakan Algoritma Random Forest 'Aziiz Alfarobi, Muhammad Ilham; Tariq, Tariq; Romadona, Romadona; Sari, Aprilisa Arum
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8857

Abstract

Early detection of mental health disorders poses a significant challenge in primary care, often hindered by conventional assessment methods that are subjective and time-consuming. This research aims to design and evaluate an intelligent system prototype for predicting mental health risks. Adopting the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework , this study utilized 1,000 medical record datasets from Clinic. A predictive model was developed using the Random Forest algorithm, which is known for its robustness in handling complex data. Evaluation results indicate exceptional model performance, achieving a weighted accuracy of 99.67% on the test dataset. Feature importance analysis confirmed that social support, sleep quality, and physical activity variables are the most significant predictors. The prototype was successfully implemented as an interactive web application using Streamlit, demonstrating the feasibility of using machine learning as a rapid and accurate clinical decision support tool for mental health screening at the primary care level.
Visualisasi Dan Perbandingan Efisiensi Algoritma A*, Greedy, Dan Dijkstra Dalam Mencari Rute Terpendek Di Kota Medan Menggunakan Openstreetmap Alvito, Paris; Ikhsan, Muhammad
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8869

Abstract

This study compares the efficiency of A*, Greedy, and Dijkstra algorithms in finding the shortest path on the road network of Medan City using geospatial data from OpenStreetMap. An interactive visualization system was developed using web-based technologies such as React.js, MapLibre GL, and Deck.gl to display the pathfinding process in real-time. The evaluation was conducted on two graph scales using six parameters: execution time, number of explored nodes, path length, memory usage, number of nodes in the path, and scalability. The results show that the A* algorithm is the most efficient overall, achieving 0.13 seconds with 17 nodes explored on the small graph, and 0.29 seconds with 52 nodes on the large graph. Dijkstra yields the most accurate paths but with significantly more node exploration and memory consumption, while Greedy is the fastest (0.11 seconds) but less accurate. This research contributes to the understanding of pathfinding algorithms and their implementation in map-based systems.
Kombinasi Rank Order Centroid dan Additive Ratio Assesment Untuk Rekomendasi Calon Penerima Program Indonesia Pintar Sinaga, Yulia Alfi; Sriani, Sriani
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8879

Abstract

The Indonesia Smart Program is a cash assistance for education aimed at elementary, middle, and high school students aged 6-21 who come from underprivileged families. In the selection process for prospective recipients of the Indonesia Smart Program at SMA Negeri 1 Kutalimbaru, several problems arose due to the still conventional data processing. This issue is primarily caused by technological limitations and the absence of a computerized system to process that data. As a result, the process is time-consuming and prone to high error rates. Therefore, a computer-based system is needed to assist in the Recommendation of Prospective Recipients of the Indonesia Smart Program. In this study, a combination of two methods was used, namely the Rank Order Centroid (ROC) method for criterion weighting and Additive Ratio Assessment (ARAS) for ranking. Based on the tests conducted on 180 data of students eligible to receive the Program Indonesia Pintar at SMA Negeri 1 Kutalimbaru, the ranking results established Restu (A142) in the first position as the most recommended Candidate Recipient of the Program Indonesia Pintar with a final score of “1”, with criteria including having a KIP Card “Yes”, having a KKS Card “Yes”, being an orphan “Orphan”, number of dependents “2”, parental income “No Income’. Thus, the ROC and ARAS methods prove to be suitable in assisting decision-making for recommendations for Candidates to Receive the Program Indonesia Pintar.
Classification of Lung TB Levels by Region in Medan City Using Logistic Regression Algorithm Purnamawati, Sri; Zufria, Ilka
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8914

Abstract

The Indonesia Smart Program is a cash assistance for education aimed at elementary, middle, and high school students aged 6-21 who come from underprivileged families. In the selection process for prospective recipients of the Indonesia Smart Program at SMA Negeri 1 Kutalimbaru, several problems arose due to the still conventional data processing. This issue is primarily caused by technological limitations and the absence of a computerized system to process that data. As a result, the process is time-consuming and prone to high error rates. Therefore, a computer-based system is needed to assist in the Recommendation of Prospective Recipients of the Indonesia Smart Program. In this study, a combination of two methods was used, namely the Rank Order Centroid (ROC) method for criterion weighting and Additive Ratio Assessment (ARAS) for ranking. Based on the tests conducted on 180 data of students eligible to receive the Program Indonesia Pintar at SMA Negeri 1 Kutalimbaru, the ranking results established Restu (A142) in the first position as the most recommended Candidate Recipient of the Program Indonesia Pintar with a final score of “1”, with criteria including having a KIP Card “Yes”, having a KKS Card “Yes”, being an orphan “Orphan”, number of dependents “2”, parental income “No Income’. Thus, the ROC and ARAS methods prove to be suitable in assisting decision-making for recommendations for Candidates to Receive the Program Indonesia Pintar.
Analysis of Public Sentiment Towards Tax Increases Impacting Unemployment Using SVM and Multinomial Naive Bayes Methods Haliza, Siti Nur; Sitorus, Zulham; Muhammad Iqbal
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8922

Abstract

Tax increase policies often generate pros and cons among the public, especially when perceived as having an impact on increasing unemployment. This study aims to analyze public sentiment regarding the issue of tax increases impacting unemployment by utilizing Machine Learning classification methods, namely Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The data used comes from social media platform X in the form of public opinions collected online and then categorized into three sentiments: positive, negative, and neutral, with a total of 1,000 sentiment data points. The analysis process included text preprocessing, feature extraction with TF-IDF, and classification using both methods. In the Test and Score algorithm, the SVM algorithm produced an AUC of 0.660, CA of 0.694, F1 of 0.569, and Recall of 0.694, while the MNB algorithm produced an AUC of 0.586, CA of 0.198, F1 of 0.105, and Recall of 0.198. The study concluded that Support Vector Machines (SVMs) had a higher level of accuracy than Multinominal Naïve Bayes in classifying public sentiment. The majority of public opinion tended to be negative, indicating concern about the impact of tax increases on the workforce. These findings provide important insights for policymakers to consider public perception when establishing future fiscal policy.
Analisis Sentimen Terhadap Kinerja Wakil Presiden Pada Tahun 2025 Menggunakan Metode Support Vector Machine Fani, Try; Nasution, Yusuf Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.9021

Abstract

This study examines public perception of the performance of Indonesia’s Vice President in 2025 by utilizing opinion data from social media X/Twitter. The research addresses the lack of up-to-date quantitative insights into public sentiment polarity following the inauguration, particularly regarding Gibran Rakabuming Raka, whose appointment has sparked mixed reactions. The objective of this study is to classify sentiments as positive or negative and to evaluate the performance of the classification model on a corpus of user posts. The dataset consists of 898 tweets collected using the hashtags #wapres, #Gibran, and #WapresGibran. Data processing involved cleaning the text, converting all characters to lowercase (case folding), tokenization, normalization, removal of stopwords, and stemming. Feature representation was carried out using Term Frequency–Inverse Document Frequency (TF-IDF), while modeling was performed with the Support Vector Machine (SVM) algorithm. Results show 647 tweets with positive sentiment and 251 tweets with negative sentiment, indicating a generally positive tendency while maintaining some diversity of opinion. The SVM model achieved an accuracy of 80.68%, demonstrating reliable performance on high-dimensional textual data. These findings provide a concise overview of public opinion that can serve as a reference for policymakers and government communication strategies. The study’s main contribution lies in offering empirical evidence from social media on sentiment dynamics toward the Vice President’s performance, while also highlighting the effectiveness of combining TF-IDF and SVM in contemporary political sentiment analysis.
Perbandingan Model Decision Tree dan Random Forest untuk Penentuan Kesesuaian Lahan Budidaya Cabai dan Terong Amir, Astiah; Fachruddin, Fachruddin; Idris, Fadli; Safriani, Meylis; Saefuddin, Reskiana; Nasution, Indera Sakti; Sanusi, Sanusi; Arisma Siregar, Mawaddah Putri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.8672

Abstract

Kabupaten Aceh Barat memiliki potensi besar dalam budidaya tanaman hortikultura seperti cabai dan terong, meskipun karakteristik tanah gambut dengan tingkat keasaman tinggi dan variabilitas lingkungan menjadi tantangan utama. Penentuan kesesuaian lahan yang akurat memerlukan analisis berbagai variabel seperti pH tanah, kelembaban tanah dan udara, curah hujan, serta tekstur tanah. Penelitian ini bertujuan mengembangkan model klasifikasi kesesuaian lahan menggunakan algoritma Decision Tree dan Random Forest untuk tanaman cabai dan terong di wilayah tersebut. Data lingkungan dan karakteristik tanah dianalisis menggunakan kedua metode tersebut untuk mengevaluasi performa klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Random Forest unggul dengan akurasi mencapai 99% pada klasifikasi lahan cabai, serta nilai precision dan recall yang lebih tinggi dibandingkan Decision Tree. Untuk klasifikasi lahan terong, kedua algoritma menunjukkan performa sempurna dengan akurasi dan metrik evaluasi mencapai 1.00 tanpa kesalahan klasifikasi. Keunggulan Random Forest terletak pada kemampuannya menangani variabel input yang kompleks dan mengurangi risiko overfitting melalui ensemble pohon keputusan, sehingga menghasilkan prediksi yang lebih stabil dan andal. Dengan demikian, Random Forest sangat cocok digunakan dalam sistem klasifikasi kesesuaian lahan berbasis data lingkungan di Aceh Barat, mendukung pengambilan keputusan budidaya yang lebih optimal dan berkelanjutan. Penelitian ini memberikan kontribusi penting dalam penerapan teknologi machine learning untuk meningkatkan efisiensi dan hasil produksi pertanian di wilayah dengan karakteristik tanah gambut yang menantang.Abstract. Kabupaten Aceh Barat has great potential for cultivating horticultural crops such as chili peppers and eggplants, despite the challenges posed by peat soil characteristics with high acidity levels and environmental variability. Accurate land suitability determination requires analysis of various variables such as soil pH, soil and air moisture, rainfall, and soil texture. This study aims to develop land suitability classification models using Decision Tree and Random Forest algorithms for chili and eggplant crops in the region. Environmental data and soil characteristics were analyzed using both methods to evaluate classification performance. The results show that the Random Forest algorithm outperforms with an accuracy of up to 99% in chili land classification, as well as higher precision and recall values compared to Decision Tree. For eggplant land classification, both algorithms demonstrated perfect performance with accuracy and evaluation metrics reaching 1.00 without any misclassification. The advantage of Random Forest lies in its ability to handle complex input variables and reduce the risk of overfitting through ensemble decision trees, resulting in more stable and reliable predictions. Therefore, Random Forest is highly suitable for use in land suitability classification systems based on environmental data in West Aceh, supporting more optimal and sustainable cultivation decision-making. This study makes an important contribution to the application of machine learning technology to improve agricultural efficiency and production outcomes in regions with challenging peat soil characteristics.
Implementasi Algoritma C4.5 pada Analisis Faktor Risiko Penyakit Jantung Koroner Yestina, Elsa Adinda; Prakoso, Bakhtiyar Hadi; Selviyanti, Erna; Suyoso, Gandu Eko Julianto
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.8852

Abstract

Coronary heart disease is a non-communicable disease due to the process of atherosclerosis resulting in blockage or narrowing of the coronary blood vessels which causes reduced oxygen supply to the heart. In the morbidity and mortality data at RSD dr. Soebandi Jember from 2020-2024, there were fluctuations. In terms of age, the majority of sufferers are productive aged between 15-64 years. The impact that has occurred is an increade in cases of coronary heart disease which is getting higher and decreasing productivity at a productive age. The purpose of this studywas to analyze risk factors for coronary heart disease based on medical records of inpatients using the C4.5 algorithm at RSD dr. Soebandi Jember. This research uses the C4.5 algorithm method with RapidMiner tools and impelents k-fold cross validation. The selection of the C4.5 algorithm is because is produces classification basen on the gain ratio value so that the classification results can be analyzed for risk factor attributes. Determination of risk factors is known in the decision results in the form of classification rules. The implementation k-fold cross validation produces the highest accuracy at k=8, namely accuracy of 86,09%, precision of 82,63%, and recall of 91,39%. Based on the results, diabetes mellitus is the most influential risk factor for coronary heart disease because it has the highest gain ratio value. Other risk factors are physical inactivity, gender, obesity, hugh blood pressure, age, and smoking. Suggestions for dr. Soebandi Hospital are to improve Communication, Information, Education (IEC), especially for diabetes mellitus patients because it affects coronary heart disease.
Analysis of the K-Nearest Neighbor (KNN) Algorithm for Gender Classification Based on Voice Characteristics Hutagalung, Bintang; Sriani, Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9117

Abstract

The gender recognition system based on voice still faces challenges such as dependence on MFCC (Mel Frequency Cepstral Coefficients) features, which are not yet able to fully represent the complexity of human voice patterns. To overcome this, this study uses 20 voice characteristics and the K-Nearest Neighbor (KNN) algorithm because it is non-parametric, capable of handling non-linear relationships between features, and works intuitively by grouping data based on similarity of distance in the feature space, making it suitable for voice patterns that are not always linearly distributed. The purpose of this study is to analyze and develop a KNN model in classifying gender based on voice characteristics. Based on testing 50 variations of K values using K-Fold Cross Validation and Euclidean Distance, the evaluation results at K = 3, 5, and 7 showed average accuracies of 0.9740, 0.9700, and 0.9712. K = 3 was selected as the optimal parameter because it produced the highest accuracy. The results show that testing on 634 test data samples using K = 3 produced 619 correct predictions and 15 incorrect predictions, with an accuracy of 98% (0.9740), as well as precision, recall, and F1-score for the Female class of 0.98, 0.97, and 0.98, while for the Male class they were 0.97, 0.98, and 0.98.

Page 90 of 107 | Total Record : 1069

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