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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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Hyperparameter Optimization pada Algoritma Decision Tree untuk Klasifikasi Penyakit Jantungd Taufik Hidayat; Mula Agung Barata; Ita Aristia Sa’ida
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.23-1.3297

Abstract

Heart disease is one of the leading causes of death globally, making the development of accurate classification models based on clinical data essential to support early risk stratification. The Decision Tree algorithm is widely applied in medical analysis due to its interpretability; however, its performance is often limited by the use of default hyperparameters. This study aims to improve the performance of the Decision Tree algorithm through the application of hyperparameter optimization using a two-stage strategy. Experiments were conducted using a Kaggle dataset consisting of 918 patients with 12 clinical attributes. The data preparation stage included encoding categorical variables and evaluation using stratified 10-fold cross-validation. The baseline Decision Tree model achieved an accuracy of 79.20%, precision of 83.16%, recall of 78.76%, and an F1-score of 80.68%. The two-stage optimization involved Random Search cross-validation to explore the parameter space, followed by refinement using Grid Search cross-validation. The optimized model showed improved performance, achieving an accuracy of 83.66%, precision of 84.17%, recall of 86.42%, and an F1-score of 85.13%. To test the statistical significance of the performance improvement, a Shapiro-Wilk normality test was conducted on the difference in F1-scores, indicating a normal distribution (p = 0.233). A paired t-test showed that the increase in F1-score was statistically significant (t(9) = 4.60, p = 0.0016) with a very large effect size (Cohen’s d = 1.45).
Analisis Komparatif Naive Bayes dan K-Nearest Neighbor dalam Klasifikasi Kinerja Produk Marketplace Berbasis Data Mining dengan Performa Optimal pada Naive Bayes Lucyana Desy Anggraeni; Kustiyono
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.23-1.3300

Abstract

Product performance classification in marketplaces is a challenge in data analysis because it involves heterogeneous numerical and categorical attributes and an uneven class distribution. This study compares the performance of the Naive Bayes (NB) and K-Nearest Neighbor (KNN) algorithms using a data mining approach on 1,200 data points that have undergone pre-processing. The evaluation was conducted using a cross-validation scheme with accuracy, kappa, precision, and recall metrics. The test results showed that NB was significantly superior with an average accuracy of 80.10% ± 0.61%, while KNN only reached 43.85% ± 1.36%. The kappa, precision, and recall values also showed the consistency of NB's superiority in capturing class distribution patterns. These findings confirm that the probabilistic approach is more effective than distance-based methods that are sensitive to data overlap and feature distribution. Theoretically, this study confirms the importance of algorithm suitability with data structure characteristics in determining classification performance in the marketplace context.
Penerapan SAW Method Untuk Menentukan Produk Unggulan Berdasarkan Penjualan Nisa Collection Dini Selfidayanti; William Ramadhan; Sudarmin
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.23-1.3304

Abstract

This study aims to apply the Simple Additive Weighting (SAW) method in a web-based Decision Support System (DSS) to determine the leading products in the Nisa Collection. The problem faced is that the determination of leading products is still subjective and only considers one sales indicator. This study analyzes 10 product alternatives based on four criteria, namely sales volume (0.40), transaction frequency (0.15), stock turnover (0.20), and price (0.25). The calculation process is carried out through the formation of a decision matrix, normalization of benefits and costs, and calculation of preference values. The results show that alternative A7 (Men's T-shirts) obtained the highest preference value of 1.00, followed by A1 and A2 with a value of 0.92. The sensitivity test shows that the top ranking remains stable against weight changes, confirming the consistency of the method. The scientific contribution of this research lies in the comprehensive application of SAW in the context of MSME retail based on quantitative sales data with decision stability analysis, thus providing an objective, measurable, and transparent product evaluation model compared to previous manual approaches.
Prediksi Hasil Panen Padi Berdasarkan Data Cuaca dan Tanah Menggunakan Metode Regresi Linear Berganda Mita Halimatus Sa'diah; Sri Mujiyono
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.23-1.3306

Abstract

Rice farming plays a strategic role in Indonesia's economy and food security because rice is the main source of energy for the community. Rice yields are greatly influenced by environmental factors, particularly weather and soil conditions. This study aims to predict rice yields by utilizing machine learning technology using multiple linear regression methods. The research was conducted in West Ungaran District with independent variables in the form of weather and soil condition data and dependent variables in the form of rice harvest yields. The analysis results show that the multiple linear regression model is valid and significant, as proven by the ANOVA test with a significance value of 0.03 (< 0.05). The correlation coefficient value of 0.739 indicates a strong relationship, while the coefficient of determination (R square) value of 0.630 indicates that 63% of the variation in yield can be explained by the model. These findings show that machine learning has the potential to support decision-making in maintaining food production stability.
Analisa Inkubasi Desa Wisata Kabupaten Garut Melalui Icon Produk dan Digital Marketing Hani Siti Hanifah; Marissa Disthy Putri; Dwirani Fauzi Lestari; Yosep Septiana
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.23-1.3317

Abstract

Tourism village development is an important strategy for improving the economy and welfare of rural communities through professional, effective, and sustainable tourism destination management. Sustainable tourism is expected to reduce poverty, minimize social inequality, strengthen village development funding, and maintain public order. However, the success of tourism village development greatly depends on the synergy of village institutions, particularly in the implementation of Village-Owned Enterprise (BUMDes) policies, both in terms of strengthening product icons and digital marketing. This study uses a qualitative approach. Primary data were obtained through interviews with BUMDes managers, MSME actors, tourism village managers, and the Village Community Empowerment Agency (BPMD) in Garut Regency, while secondary data were sourced from literature, journals, and relevant websites. The research population included tourism villages and legally registered BUMDes in Garut Regency, with a sample consisting of six tourism villages, 30 respondents, and four informants selected using purposive sampling. Data validity was tested using triangulation techniques. The results show that tourism village development through digital marketing, product icons, and tourism village incubation approaches achieved varying outcomes. The implementation of digital marketing and product icon development remains suboptimal, as indicated by the limited use of digital platforms and the lack of competitive flagship products. In contrast, the tourism village incubation aspect is relatively better, reflected in the fairly well-organized BUMDes organizational structure and relatively active community involvement, although it has not yet been formally accumulated. Therefore, collaboration among stakeholders needs to be strengthened in order to realize sustainable and competitive tourism villages.
Media Online Store Dengan Penerapan E-CRM Di Susi Bucket And Decoration Dian Kartini; Fauriatun Helmiah; Wan Mariatul Kifti
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.23-1.3318

Abstract

The development of digitalization encourages MSMEs to optimize their sales and customer relationship management systems in an integrated manner. Susi Bucket and Decoration still faces obstacles in order management, customer data recording, and market reach limitations due to manual processes. This study aims to design and implement an Electronic Customer Relationship Management (E-CRM)-based Online Store system to improve operational efficiency and customer service quality. The methods used are a descriptive qualitative approach with a Waterfall system development model and testing using the black box method. The results show that the system is capable of centrally integrating transaction and customer data, accelerating the order recording process by an average of ±3 minutes compared to the manual method, and improving customer response effectiveness through chat and structured review features. Additionally, period-based sales reports help business owners systematically evaluate sales trends. The scientific contribution of this research lies in the development of an integrated E-CRM implementation model for creative SMEs that combines digital transactions, customer satisfaction monitoring, and sales analytics in a single integrated platform.
Sistem Pendukung Keputusan Berbasis Metode SAW Untuk Pemilihan Biji Kopi Berkualitas Sebagai Bahan Minuman di Kopi Luar Dalam Nurhidayah; Riki Andri Yusda; Sudarmin
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.23-1.3319

Abstract

Selecting high-quality coffee beans is a critical factor in maintaining flavor consistency in the coffee beverage industry. At Kopi Luar Dalam, the raw material selection process is still conducted subjectively, which can lead to inconsistent decisions. This study aims to design and implement a Decision Support System using the Simple Additive Weighting (SAW) method to determine the best coffee beans based on four criteria: cost, aroma, bean color, and physical form. The study employs a quantitative approach involving decision matrix normalization and preference value calculation. The results show that the African Blue Mountain Arabica (A8) alternative obtained the highest preference value of 0.80 and is therefore recommended as the best raw material. Methodologically, this study demonstrates that the application of SAW is capable of producing an objective, structured, and transparent selection process in the context of raw material selection for the SME-scale coffee industry.
Sistem Pemberian Reward Karyawan Dengan Pembobotan AHP dan MOORA pada CV. Putra Karya Logam Sukses Venty; Riki Andri Yusda; Akmal
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.23-1.3320

Abstract

Determining objective employee rewards is a challenge for manufacturing companies because manual processes are prone to subjectivity and inconsistencies in evaluation. This study aims to develop a Decision Support System based on the integration of the Analytic Hierarchy Process (AHP) and MOORA to improve the accuracy and transparency of performance evaluations. AHP is used to determine the weights of five criteria through a consistency test (CR < 0.1), while MOORA is used for the ranking process of 69 employee alternatives. The results show that the system produces the best alternative with the highest preference value of 0.1474 and demonstrates ranking stability based on a weight sensitivity test (correlation coefficient 0.92). A comparison with the SAW and TOPSIS methods demonstrates the consistency of the best alternative; however, the AHP–MOORA approach provides more structured weight validation and better ranking stability. Scientifically, this study confirms the superiority of integrating weighting and optimization methods in producing a robust and verified decision-making model, particularly in the context of employee reward evaluation in the manufacturing sector.
Penerapan K-Means Clustering Kesiapan Siswa SMA Dalam Pemilihan Jurusan Kuliah Rara Revina; Fauriatun Helmiah; Abdul Karim Syahputra
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.23-1.3323

Abstract

Choosing a college major is an important decision for high school students because it affects their future academic success and career. However, 12th-grade students at Kisaran Regional High School still struggle to determine a major that aligns with their interests and abilities. This study employs a quantitative approach using data mining methods via the K-Means Clustering algorithm to categorize students’ readiness levels in selecting a college major. Research data were obtained from questionnaires completed by 71 students based on five assessment aspects: interest in the major, alignment with academic performance, career information, environmental support, and personal maturity. The research instrument used a 1–5 Likert scale and was tested for reliability using Cronbach’s Alpha, which yielded a value of 0.87, indicating a good level of consistency. The research stages included data selection, transformation of qualitative data into numerical form, determination of the number of clusters (k=3), random initialization of the initial centroids, and calculation of distances using Euclidean Distance, performed iteratively until convergence was achieved. The results of the study indicate that out of 71 students, 23 students (32.39%) fall into the “very ready” category, 28 students (39.44%) into the “fairly ready” category, and 20 students (28.17%) into the “not yet ready” category regarding college major selection.
Perbandingan K-Medoids(PAM) Dan K-Means Untuk Semgentasi Produk Smartphone Di Shoope Indonesia Berdasarkan Harga Rating Dan Jumlah Ulasan: Studi Periode Maret 2026 Amanda Nur Haliza; 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.23-1.3324

Abstract

This study aims to segment smartphone products based on the variables of Price, Rating, and Number of Reviews using the K-Means and K-Medoids methods. The dataset used consists of 400 smartphone products that have undergone data normalization as part of the preprocessing stage. The number of clusters was determined using an internal evaluation method, and the optimal number of clusters was found to be four (K=4). The clustering results show that both methods are capable of forming significantly different product group characteristics based on a combination of price level, user rating quality, and review intensity. The K-Means method produces a more structured cluster separation based on centroid values and is effective in representing the average data distribution. Meanwhile, K-Medoids demonstrate better resilience against outliers because cluster centers are represented by actual objects (medoids), making them more stable on heterogeneous data. Based on a comparative analysis of the methods’ characteristics and cluster evaluation results, K-Medoids demonstrates more robust performance for datasets with significant price variation. The findings of this study can serve as a basis for decision-making in marketing strategies and product clustering on e-commerce platforms.

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