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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
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Efisiensi Layanan Publik Melalui BPR: Digitalisasi Proses Mutasi Pegawai di Disdikbud Tabalong Muhammad Rafly Hidayat; Wildan Suharso
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.23-1.3150

Abstract

The employee transfer process at the Tabalong Regency Education and Culture Office (Disdikbud) is still manual, resulting in completion times of up to one month, susceptibility to errors, and reduced applicant satisfaction. This study applies digitalization-based Business Process Reengineering (BPR) to redesign the process flow. The research method uses Business Process Model and Notation (BPMN) 2.0 for process modeling and ASME standards for throughput efficiency testing. The results show that the existing process throughput efficiency is only 1.05% with a total time of 69,005 minutes. The proposed web-based system with real-time document verification automation, electronic signatures, and automatic notifications increases efficiency to 100% in just 110 seconds (1.8 minutes). This implementation is in line with the National SPBE agenda and can be replicated in other agencies.
Prediksi Tingkat Kepadatan Kendaraan Menggunakan Metode YOLO Berbasis Image Processing pada Video Jalan Raya Nitral Sejak Terang Waruwu; Yoannes Romando Sipayung
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.23-1.3152

Abstract

Traffic congestion in urban areas causes time losses, increased fuel consumption, and a decline in environmental quality. Therefore, a reliable visual data-based traffic monitoring system is needed. This study develops a system for detecting and analyzing traffic density and identifying peak hours by utilizing the You Only Look Once (YOLO) algorithm as a deep learning approach. YOLO is used to detect and count vehicles from highway video data, and the detection results are stored in a database for temporal analysis using historical vehicle volume data. This analysis aims to identify traffic density patterns and rush hour periods without applying a time-series-based temporal prediction model. The system's performance is evaluated using precision, recall, and mean Average Precision (mAP) metrics, while the rush hour identification results are validated through comparison with field observations. Test results show that YOLO is capable of accurately detecting vehicles and that the developed system can consistently identify periods of traffic density. The integration of YOLO-based vehicle detection with web-based temporal analysis is expected to support travel decision-making in urban environments.
Sistem penunjang keputusan pemilihan karyawan garmen terbaik Menggunakan metode Weighted Product Cahya Annisyah Waruwu; 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.23-1.3158

Abstract

The garment industry sector is highly dependent on human resources to play a role in maintaining quality at every stage of the production process and final products. Employee assessment methods in this sector tend to rely on subjective evaluations, which can lead to potential unfairness in decision-making. This study discusses the development of a decision support system that can be used by management to identify employees in a more objective manner. The method applied in this research is the Weighted Product method, which evaluates a number of alternatives by multiplying rating values derived from several criteria, namely punctuality, discipline, work quality, responsibility level, and teamwork. Employee data were obtained through interviews and observations in the production department. The calculation results indicate that the Weighted Product method is able to generate more consistent and objective employee rankings. The system is implemented as a web-based application so that it can be accessed by management. With the implementation of this system, the employee evaluation process becomes more efficient and transparent and can be used as a basis for awarding incentives and job promotions.
Implementasi Augmented Reality Untuk Media Promosi Program Gaya Hidup Sehat di Santiana Nutrition Club Menggunakan Metode MDLC Asep Deddy Supriatna; Erika Puspa Dewi
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.23-1.3159

Abstract

Driven by the need for more effective promotion at Santiana Nutrition Club (Santiana NC), this study aims to design and develop an Augmented Reality (AR) application as a promotional tool for healthy living programs at Santiana Nutrition Club (Santiana NC) using the Multimedia Development Life Cycle (MDLC) method, which includes the concept, design, content collection, application development, testing, and distribution phases. The application was developed to provide more interactive promotions compared to conventional methods. Testing was conducted via Black Box Testing to ensure the system functions properly, followed by Alpha Testing by the developers and Beta Testing using the System Usability Scale (SUS) with Santiana NC customers and owners. The test results showed that all features functioned optimally with a SUS score of 72.67, which falls into the “good” category; thus, the application was deemed suitable for use as an effective and easily accessible interactive promotional medium for the public.
Penerapan Decision Support System (DSS) menggunakan Metode TOPSIS untuk Seleksi Mahasiswa Berprestasi Irawati; Sugiarti; Lilis Nur Hayati; Herman; Siti Safira Tawetubun; Nur Asy Syams Sam Ahmad
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.23-1.3200

Abstract

The diversity of students in Indonesian universities requires an objective and transparent selection mechanism for high-achieving students, while manual selection practices remain prone to subjectivity and inconsistency in assessment. This study developed a Decision Support System (DSS) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with a methodological innovation in the form of integrating eight multidimensional criteria that combine academic and non-academic aspects into a single structured decision-making framework. The implementation results show that the system is capable of increasing the consistency of selection decisions by up to 87% and reducing the selection process time by around 60% compared to conventional methods. These findings confirm that the TOPSIS-based DSS not only improves the objectivity of assessments but also provides significant operational efficiency, thus having the potential to become an adaptive and applicable decision support model for standardizing the selection of outstanding students in higher education.
Perbandingan Genetic Algorithm, Nearest Neighbour, dan Particle Swarm Optimization untuk Penentuan Rute Pengiriman Barang Sandy Achmadi; Prabowo Murti Saputro; Luhur Bayuaji
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.23-1.3201

Abstract

The goods distribution process carried out by PT Saqo Putra Utama, a logistics transportation service company that delivers goods from warehouses to customers in the Jabodetabek area, is still performed manually. As a result, its effectiveness cannot be measured based on the travel distance from one location to another, leading to high operational costs for the company. This study aims to determine the shortest route for goods delivery by minimizing travel distance. The study compares and analyzes route determination results using three methods: Genetic Algorithm, Nearest Neighbour, and Particle Swarm Optimization. The comparison of these three algorithms in goods distribution routing aims to find a balance between processing speed and solution quality, namely the shortest distance or lowest cost. This research was conducted in the Jabodetabek area at PT Saqo Putra Utama, a logistics transportation service company that distributes goods from warehouses to customers. Based on the average calculation results of the three compared methods, it can be concluded that the best method for determining goods delivery routes at PT Saqo Putra Utama is the Genetic Algorithm method, with an average total distance of 222.57 km and an average total cost of IDR 355,809.66.
Penerapan Algoritma C4.5 dalam Memprediksi Kepuasan Siswa Terhadap Kinerja Guru SMK Fitria Nur Anifah; Abdul Rohman
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.23-1.3203

Abstract

This study aims to predict student satisfaction levels with teacher performance at vocational high schools (SMK) using data mining methods with the C4.5 algorithm. The research data was obtained through a teacher performance assessment questionnaire, which was processed using RapidMiner software to build a decision tree-based classification model. The model performance was evaluated using a Confusion Matrix with accuracy, precision, recall, and F1-score metrics, as well as Cohen's Kappa measurement to assess the suitability of the classification results. In addition, a cross-validation scheme was used to ensure the stability of the model's performance. The results showed that the C4.5 algorithm was able to classify student satisfaction levels with a very high degree of accuracy and a Cohen's Kappa value that indicated perfect classification consistency. The resulting model was able to clearly identify patterns of student satisfaction based on teacher performance indicators. This study shows that the C4.5 algorithm has the potential to be applied as a tool to support student satisfaction analysis in the field of education and can be further developed with more diverse datasets and comparison methods.
Analisis Sentimen Ulasan Produk Marketplace Indonesia Menggunakan Naive Bayes dan SVM dengan Label Berdasarkan Rating Levi Ardin Gulo; Agung 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.23-1.3206

Abstract

Sentiment analysis aims to identify user opinions about products on marketplaces such as Shopee and Tokopedia. This study classifies product review sentiment using Naive Bayes (NB) and Support Vector Machine (SVM). The dataset underwent text preprocessing, including case folding, tokenization, stopword removal, and stemming, then was represented using TF-IDF. The results show that Support Vector Machine (SVM) achieved the highest accuracy of 94.54%, but had a very low negative class recall (5.71%), indicating a strong bias towards the majority class. In contrast, Naïve Bayes (NB) recorded a lower accuracy of 67.88%, but showed more balanced performance with a negative class recall of 48.57%. Conversely, NB provided more balanced performance between positive and negative classes despite its slightly lower accuracy. These findings emphasize the importance of considering class imbalance in sentiment analysis, especially for applications that require consumer complaint detection. This research is expected to serve as a reference for the development of automatic sentiment analysis systems on marketplace platforms with a focus on performance balance between classes.
Analisis Probabilistik Kepuasan Guru Terhadap Sistem Absensi QR Code Menggunakan Naïve Bayes Pada Data Kuesioner Irfan Fakhruddin; Abdul Rohman
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.23-1.3215

Abstract

The study aims to predict the satisfaction outcomes of teachers at SMA 1 Purwantoro regarding the implementation of a web-based QR Code attendance system using the Naïve Bayes data processing method. With the rapid development of the digital era, particularly in education, institutions are required to utilize technology. However, one of the public high schools in Wonogiri still uses a conventional attendance system, which is considered inefficient and time-consuming. The method used by the authors for data processing is Naïve Bayes, an algorithm based on statistical probability and Bayes’ theorem to predict probabilities. Data collection was conducted through a Google Form questionnaire involving 20 respondents, covering aspects of ease of use, speed, reliability, and security. Based on the probability table calculations, the level of teacher satisfaction in using the QR Code attendance system can be categorized as predominantly satisfied, with 65% of respondents stating they are satisfied and 35% indicating dissatisfaction. Furthermore, the conditional probability analysis within the satisfied category shows that the ease of use aspect has a value of 0.92, speed 1.00, reliability 0.92, and security 1.00 based on the overall respondent data. From these findings, it can be concluded that the implementation of a web-based QR Code attendance system is considered capable of facilitating teachers and staff in managing student attendance processes. The system helps reduce delays in attendance recording and simplifies the process of entering attendance data more efficiently.
Rancang Bangun Virtual Tour Sebagai Media Promosi Wisata Infinity Pool Di Agrowisata Tepas Papandayan (ATP) Berbasis Web Dini Destiani Siti Fatimah; Yeni Pariyatin; Dikri Ramadhan
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.23-1.3217

Abstract

Tepas Papandayan Agrotourism is one of the lesser-known natural tourist destinations in Garut Regency, largely due to a lack of informational and promotional materials capable of attracting tourists. This study aims to develop an informative promotional medium based on 360-degree virtual tour technology that can be accessed via a website, with the goal of providing an engaging and informative virtual exploration experience. The application was developed using the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, content gathering, development, testing, and distribution. Testing employed a black-box testing approach using decision tables with test cases to ensure all functions operate correctly without errors as per the design, followed by beta testing using User Acceptance Testing (UAT) with purposive sampling to ensure the application meets user needs. The results of this study indicate that the interactive virtual tour application is now accessible and provides tourism information, serving as a promotional tool. A survey of 139 prospective tourists revealed that 97.1% of those who used the application expressed interest in visiting.

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