cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurikom.stmikbd@gmail.com
Editorial Address
STMIK Budi Darma Jalan Sisingamangaraja No. 338 Simpang Limun Medan - Sumatera Utara
Location
Kota medan,
Sumatera utara
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 897 Documents
 86   87   88   89   90 
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.

Page 90 of 90 | Total Record : 897

 86   87   88   89   90 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 4 (2025): Agustus 2025 Vol 12, No 3 (2025): Juni 2025 Vol 12, No 2 (2025): April 2025 Vol 12, No 1 (2025): Februari 2025 Vol 11, No 6 (2024): Desember 2024 Vol 11, No 5 (2024): Oktober 2024 Vol 11, No 4 (2024): Augustus 2024 Vol 11, No 3 (2024): Juni 2024 Vol 11, No 2 (2024): April 2024 Vol 10, No 3 (2023): Juni 2023 Vol 10, No 2 (2023): April 2023 Vol 10, No 1 (2023): Februari 2023 Vol 9, No 6 (2022): Desember 2022 Vol 9, No 5 (2022): Oktober 2022 Vol 9, No 4 (2022): Agustus 2022 Vol 9, No 3 (2022): Juni 2022 Vol 9, No 2 (2022): April 2022 Vol 9, No 1 (2022): Februari 2022 Vol 8, No 6 (2021): Desember 2021 Vol 8, No 5 (2021): Oktober 2021 Vol 8, No 4 (2021): Agustus 2021 Vol 8, No 3 (2021): Juni 2021 Vol 8, No 2 (2021): April 2021 Vol 8, No 1 (2021): Februari 2021 Vol 7, No 6 (2020): Desember 2020 Vol 7, No 5 (2020): Oktober 2020 Vol 7, No 4 (2020): Agustus 2020 Vol 7, No 3 (2020): Juni 2020 Vol 7, No 2 (2020): April 2020 Vol 7, No 1 (2020): Februari 2020 Vol 6, No 6 (2019): Desember 2019 Vol 6, No 5 (2019): Oktober 2019 Vol 6, No 4 (2019): Agustus 2019 Vol 6, No 3 (2019): Juni 2019 Vol 6, No 2 (2019): April 2019 Vol 6, No 1 (2019): Februari 2019 Vol 5, No 6 (2018): Desember 2018 Vol 5, No 5 (2018): Oktober 2018 Vol 5, No 4 (2018): Agustus 2018 Vol 5, No 3 (2018): Juni 2018 Vol 5, No 2 (2018): April 2018 Vol 5, No 1 (2018): Februari 2018 Vol 4, No 5 (2017): Oktober 2017 Vol 4, No 4 (2017): Agustus 2017 Vol 3, No 6 (2016): Desember 2016 Vol 3, No 5 (2016): Oktober 2016 Vol 3, No 4 (2016): Agustus 2016 Vol 3, No 1 (2016): Februari 2016 Vol 2, No 6 (2015): Desember 2015 More Issue