cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yosep Septiana
Contact Email
yseptiana@itg.ac.id
Phone
+6282124588750
Journal Mail Official
algoritma@itg.ac.id
Editorial Address
Jl. Mayor Syamsu No.1, Jayaraga, Kec. Tarogong Kidul, Kabupaten Garut, Jawa Barat 44151
Location
Kab. garut,
Jawa barat
INDONESIA
Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,150 Documents
 109   110   111   112   113 
Manajemen Kapasitas Jaringan Berbasis Mikrotik Untuk Meningkatkan Kestabilan Internet Di Jaringan Lokal Pada Balai Desa Silo Bonto Nur Haisyah; Jhonson Efendi Hutagalung; Risnawati
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3386

Abstract

The main issue with the internet network at the Silo Bonto Village Hall is connection instability caused by a lack of bandwidth management and uncontrolled network usage. This study aims to design and evaluate a MikroTik-based network capacity management system to improve local network performance. The method used was an experiment comparing network conditions before and after the implementation of Simple Queue and Per Connection Queue (PCQ) on MikroTik RouterOS. Testing was conducted using the parameters of throughput, delay, packet loss, and jitter. The results of the study showed an improvement in network performance, marked by an increase in throughput from 8.5 Mbps to 24.6 Mbps, a decrease in delay from 152 ms to 48 ms, a reduction in packet loss from 7.8% to 1.2%, and a decrease in jitter from 38 ms to 9 ms. The main contribution of this research is the application of a combination of the Simple Queue and PCQ methods, which has proven effective in improving stability and bandwidth fairness in networks with limited capacity.
Integrasi YOLOv12 dan Konveyor Belt Untuk Mendeteksi dan Menyortir Kerusakan Kaleng Cat Secara Otomatis Usama; Deny Wiria Nugraha
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3390

Abstract

This study aims to develop an automated system for inspecting and sorting defects in paint cans based on the integration of deep learning and a conveyor belt to improve the efficiency and consistency of quality control in the industry. The methods used include the design of a mechanical conveyor system, the integration of electronic circuits, and the development of an object detection model using YOLOv12 with a multi-camera configuration to minimize blind spots. The dataset consists of 1,209 images divided into training, validation, and test sets, with data augmentation applied to improve model robustness. Evaluation was conducted using precision, recall, F1-score, and mAP metrics, along with end-to-end system testing based on sorting accuracy, latency, and throughput under various lighting conditions and conveyor speeds. The research results show that the model achieved a precision of 0.98, a recall of 0.96, an F1-score of 0.97, and an mAP50–95 of 0.96. However, the system implementation yielded a sorting accuracy of 55.6% with optimal performance at moderate speeds, indicating a significant influence of operational factors on the system’s overall performance.
Penilaian Risiko Fraud Transaksi Digital menggunakan Hybrid Machine Learning dengan Clustering dan Klasifikasi Hendra Wijaya; Naek Parulian Hutagalung; Mira Afrina; Ali Ibrahim; Fathoni
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3398

Abstract

Credit card transaction fraud detection is commonly treated as a binary classification problem, whereas operational risk management requires more detailed risk-level information to support investigation prioritization. This study proposes a hybrid machine learning framework for transaction risk stratification. In the first stage, the K-Means algorithm was applied to the training set to discover latent risk structures and generate cluster-based risk labels. Subsequently, a Random Forest model was trained to predict risk levels for new transaction data. To maintain evaluation objectivity, the dataset was divided into training, validation, and testing sets, and data leakage prevention mechanisms were implemented. The testing results show that the model was able to consistently classify two levels of risk with stable precision, recall, and F1-score values. In the binary fraud detection scenario, the model achieved an accuracy of 0.8831. These findings indicate that separating latent risk exploration from predictive classification can produce a more informative risk representation compared to conventional binary approaches. However, this study is still limited to a single public dataset and one classification model. Therefore, the generalizability and potential performance improvements of the model still need to be evaluated by experimenting with other algorithms.
Implementasi CRM Berbasis Web Untuk Meningkatkan Kepuasan Pelanggan di Toko Adi Glass Muhammad Fahrozi; Jhonson Efendi Hutagalung; Ruri Ashari Dalimunthe
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3401

Abstract

Advances in information technology in the era of digital transformation are driving businesses to leverage technology to enhance competitiveness and service quality. Adi Glass, a business in the home furnishings sector, still faces challenges in managing customer data and sales transactions, which are currently handled manually, thereby reducing service effectiveness and sales performance. This study aims to design and implement a web-based Customer Relationship Management (CRM) system and evaluate its impact on sales performance and customer satisfaction. The method used is a mixed-methods approach involving interviews, observations, literature review, and questionnaires. The results show that the CRM system is capable of integrating customer and sales data and supports structured promotion management. The evaluation indicates a 30% increase in data management efficiency, a 25% increase in service speed, and a 27% increase in customer satisfaction. The contribution of this research lies in the development of an integrated web-based CRM system with promotional features as a sales improvement strategy for small businesses.
Klasifikasi Pola Serangan Keamanan Jaringan pada UNSW-NB15 Menggunakan Pendekatan Machine Learning Afrizal Dzikri Arifah; Diah Risqiwati
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3403

Abstract

The increasing complexity and volume of network traffic have created challenges for attack detection systems in recognizing diverse attack patterns and handling imbalanced class distributions. This study aims to detect network attacks on the UNSW-NB15 dataset using supervised learning methods, namely Logistic Regression and Random Forest. The research stages include data preprocessing, model development, and evaluation using Stratified 5-Fold Cross Validation with Accuracy, Precision, Recall, F1-Score, and AUC metrics. The results show that Random Forest achieved better performance than Logistic Regression, with an accuracy of 0.9515, precision of 0.9631, and AUC of 0.9924. Cross-validation results also showed that the average accuracy of Random Forest was 0.9504, higher than Logistic Regression at 0.9038. Feature analysis indicates that attributes based on Time to Live, traffic, data volume, and temporal characteristics contribute significantly to the detection process. Therefore, Random Forest demonstrates more optimal and stable performance in detecting network attacks on the UNSW-NB15 dataset, while Logistic Regression remains relevant as a simple and interpretable comparison model.
Peningkatan Knowledge Capture dan Knowledge Sharing dalam KMS Tools dengan Kaizen Form Faisal Hakim Amrullah; Hendry; Irwan Sembiring
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3409

Abstract

This study discusses the improvement of knowledge capture and knowledge sharing through the strengthening of a Kaizen-based Knowledge Management System (KMS) in the footwear manufacturing industry. The main problems include the suboptimal management of tacit knowledge and the limitations of document search based on simple keywords. This study applies an information retrieval method using TF-IDF and Cosine Similarity on 800 validated Kaizen documents through preprocessing, weighting, and document similarity measurement stages. The test results show that the proposed method performs better than conventional keyword-based search, with a precision value of 0.60, recall of 0.75, and F1-score of 0.67. The contribution of this study lies in the application of information retrieval methods to improve the effectiveness of knowledge retrieval in a Kaizen-based KMS, thereby supporting continuous improvement and organizational learning.
Model Rekomendasi Karier Lulusan Sekolah Menengah Kejuruan Berdasarkan Kompetensi dan Bakat Menggunakan Perbandingan Algoritma Apriori dan FP-Growth Tarwan; Eko Aji Putra; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3410

Abstract

The increasingly dynamic development of the job market requires Vocational High School (SMK) graduates to possess adaptive abilities, not only in mastering vocational competencies but also in determining appropriate career paths. However, in reality, many SMK graduates still experience difficulties in choosing careers that align with their competencies and talents. This condition highlights the need for a systematic approach capable of providing data-driven career recommendations. This study aims to develop a data-based career recommendation system using the Apriori and FP-Growth algorithms to identify relationship patterns among vocational competencies, students’ academic talents, and alumni tracer study data. The study offers a new approach to career recommendation systems for Vocational High Schools by integrating students’ academic data and alumni post-graduation histories (tracer studies) within a single pattern analysis framework. In addition to generating association rules that can easily be used as a basis for decision-making, the system also incorporates validation from guidance and counseling teachers (BK teachers) to strengthen data-driven career decisions. Talents are classified into two categories, namely exact/science-oriented and non-exact/non-science-oriented, based on comparisons between average Mathematics grades and non-science subject grades from semesters 1 to 6. Alumni tracer data include post-graduation status (employment, higher education, or others), job relevance, competency certificates, and the positions or work sectors pursued. Subsequently, each student and alumni entity was transformed into transactional data analyzed using the Apriori and FP-Growth algorithms to discover association rules between student profiles and career recommendations. The analysis results indicate strong relationships between combinations of talents and vocational competencies with specific career choices. The inclusion of data from guidance and counseling teachers serves as qualitative input that strengthens the validity of the system’s results. This system can be utilized by schools, guidance counselors, and students as a decision-support tool for making more objective and data-driven career decisions. Therefore, the system supports a vocational education direction that is more integrated with labor market needs.
Analisis Komparatif Overhead Kinerja CPU pada Lingkungan Bare Metal, Docker Container, dan KVM Virtual Machine Berbasis Linux Ubuntu Ammanda Putri Nurhalizah; Ilham Nur Ramdani; Imelda Imelda
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3411

Abstract

Virtualization and containerization technologies are key solutions for improving the efficiency of modern computing infrastructure. This study aims to analyze a comparison of CPU performance overhead across three computing environments: Bare Metal, Docker Containers, and KVM Virtual Machines running on Ubuntu 24.04 LTS. Testing was conducted using Sysbench with the Events Per Second (EPS) metric as an indicator of throughput, with Bare Metal serving as the baseline. The results show that Docker Containers exhibit near-native performance with an overhead of -4.44%, indicating a slight performance improvement over the baseline due to efficient resource management. Conversely, the KVM Virtual Machine experienced a significant performance drop with an overhead of 81.78%. These findings suggest that containers are more efficient in utilizing CPU resources, while KVM Virtual Machines offer higher system isolation at the cost of reduced performance. This study provides a quantitative evaluation to serve as a basis for selecting computing technologies based on performance and isolation requirements.
Implementasi Algoritma First Come First Serve (FCFS) pada Sistem Manajemen Pengaduan Online APTIKA Rizka Annisa; Anita Ahmad Kasim
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3412

Abstract

The high demand for digital services, which is not yet supported by an integrated complaint management system, has led to low transparency and slow handling of reports. This study developed a web- and mobile-based complaint management system by implementing the First Come First Serve (FCFS) algorithm for queue management. The research method uses a mixed method with black-box functional testing and performance evaluation through a pre-test and post-test scheme analyzed using the Paired Sample T-Test. The results show an acceleration in resolution time across all service categories with efficiency levels ranging from 60.9% to 91.3%. Statistically, the significance value (p-value < 0.05) proves an improvement in performance compared to the manual method. The contribution of this research is the integration of FCFS and Role-Based Access Control (RBAC) into a structured and efficient multi-service-based government complaint system. The limitations of this study lie in the use of simulation data in the post-test as well as pre-test data that is estimative with a limited sample, so the results do not yet fully represent real operational conditions and still require further testing.
A Analisis Sentimen Publik terhadap Program Makan Bergizi Pemerintah (MBG) pada Media Sosial X Menggunakan Metode Support Vector Machine Windi Haria Ningsi; Ucta Pradema Sanjaya
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3413

Abstract

The Nutritious Meal Program (MBG) is one of the government’s initiatives to improve the nutritional quality of society. This study aims to analyze public sentiment toward the MBG program on social media platform X in order to provide a quantitative overview of public perception. The data used consisted of 1,938 public posts containing keywords related to the MBG program. The analysis stages included text preprocessing, namely data cleaning, tokenization, stopword removal, and stemming. Furthermore, feature representation was carried out using the TF-IDF method, while sentiment classification was performed using the Support Vector Machine (SVM) algorithm to categorize the data into positive, neutral, and negative sentiments. The results indicate that the classification model achieved an accuracy rate of 96.69 percent, demonstrating excellent model performance. Based on the classification results, sentiment distribution was dominated by negative sentiment, followed by neutral and positive sentiments, indicating that public responses toward the MBG program tend to be critical. These findings suggest that although the MBG program has received significant public attention, there are still various criticisms and feedback that can serve as evaluation material for the government in improving the effectiveness and implementation of the program.

Page 111 of 115 | Total Record : 1150

 109   110   111   112   113 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 23 No 1 (2026): Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma Vol 20 No 1 (2023): Jurnal Algoritma Vol 19 No 2 (2022): Jurnal Algoritma Vol 19 No 1 (2022): Jurnal Algoritma Vol 18 No 2 (2021): Jurnal Algoritma Vol 18 No 1 (2021): Jurnal Algoritma Vol 17 No 2 (2020): Jurnal Algoritma Vol 17 No 1 (2020): Jurnal Algoritma Vol 16 No 2 (2019): Jurnal Algoritma Vol 16 No 1 (2019): Jurnal Algoritma Vol 15 No 2 (2018): Jurnal Algoritma Vol 15 No 1 (2018): Jurnal Algoritma Vol 14 No 2 (2017): Jurnal Algoritma Vol 14 No 1 (2017): Jurnal Algoritma Vol 13 No 2 (2016): Jurnal Algoritma Vol 13 No 1 (2016): Jurnal Algoritma Vol 12 No 2 (2015): Jurnal Algoritma Vol 12 No 1 (2015): Jurnal Algoritma Vol 11 No 2 (2014): Jurnal Algoritma Vol 11 No 1 (2014): Jurnal Algoritma Vol 10 No 2 (2013): Jurnal Algoritma Vol 10 No 1 (2013): Jurnal Algoritma Vol 9 No 2 (2012): Jurnal Algoritma Vol 9 No 1 (2012): Jurnal Algoritma More Issue