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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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Design and Implementation of Web-Based Inventory Information System dengan Real-Time Stock Monitoring pada Gudang Perkebunan Kelapa Sawit Ahmad Rohim; Indera
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.23-1.3442

Abstract

Warehouse inventory management in palm oil plantations requires a fast, accurate, and integrated system. Manual recording often causes stock discrepancies, delayed data updates, and weak inventory monitoring. This study aims to develop a Web-Based Inventory Information System to support real-time stock monitoring. The novelty of this study lies in automatic stock updates based on integrated inbound and outbound goods transactions with centralized reporting. The system was developed using the Waterfall method, UML design, web-based implementation, and Black Box Testing. The main features include item data management, inventory transactions, automatic stock updates, and inventory reports. Testing conducted on 25 scenarios showed that all functions operated according to requirements with a 100 percent success rate. This system is capable of improving inventory management efficiency, accelerating stock monitoring, reducing recording errors, and supporting data-driven decision-making.
Evaluasi Kinerja Penjualan dan Efisiensi Iklan Kampanye GMV Max pada TikTok Shop Garage Fortress Rachman Hidayat; Jeffri Prayitno Bangkit Saputra; Luzi Dwi Oktaviana
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.23-1.3445

Abstract

The growth of social commerce has driven changes in digital marketing strategies that are increasingly data-driven and automated on e-commerce platforms. One of the features utilized in TikTok Shop is GMV Max, an automated campaign system designed to optimize sales performance through platform-based advertising management. This study aims to describe and evaluate the sales performance of TikTok Shop Garage Fortress under the GMV Max campaign using a descriptive quantitative approach based on secondary data obtained from campaign reports covering the period from October 9, 2025, to April 6, 2026. The analysis focuses on GMV, number of orders, advertising costs, ROAS, and conversion rate indicators without examining causal relationships among variables. The results show that the GMV Max campaign generated a total GMV of IDR 25,608,081 with 772 orders, advertising expenditure of IDR 2,324,547, a weighted ROAS of 11.02×, and a conversion rate of 6.15 percent. The GLASSWOOL product campaign contributed the largest share of sales value and number of orders. Based on advertising content type, video advertisements demonstrated higher performance in terms of GMV and ROAS, while product cards achieved a higher conversion rate. Overall, the findings indicate that the GMV Max campaign within the research dataset produced a positive ROAS and measurable conversion rate, although the interpretation of the results should still consider data quality and potential attribution anomalies within the TikTok Shop platform.
Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Sekolah Dasar Menggunakan Algoritma Naïve Bayes Classifier Aurike Wijaya; Anita; Marchelina Chistina Manurung; Yosef Dwi Santosa Sitanggang
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.23-1.3448

Abstract

Government regulations that heavily influence graduation decisions often lead to data imbalances that obscure the effectiveness of machine learning models in the education sector. This study evaluates the performance of the Naïve Bayes algorithm and compares it with Decision Tree and K-NN on a dataset of 385 students from SD Negeri 067053 Medan Deli, which exhibits extreme label imbalance (with the “Pass” class dominating at 88%). Model evaluation was conducted using Stratified 10-Fold Cross Validation. Test results show that Naïve Bayes achieved a high accuracy of 94.04% and proved to be the most robust in identifying the minority class with a Recall of 91.11%, outperforming other comparison algorithms that suffered from overfitting. However, this high accuracy masked an administrative bias, where the precision of Naïve Bayes in predicting the “Fail” class plummeted to 68.33%. This study confirms that accuracy metrics alone can be misleading on imbalanced data, making the application of resampling techniques during the data preprocessing stage absolutely necessary to address bias in educational data mining implementations.
Pengembangan Sistem Online Public Access Catalog Perpustakaan dengan Fitur Tracking Minat Baca Kordinal Depriansyah; Angga Bayu
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.23-1.3449

Abstract

School library digitalization is generally limited to operational circulation aspects without exploring user behavior data. At SMA IT Quran Qordhova, the conventional system hindered search efficiency and made it difficult to map students’ reading interests. This study aims to develop a web-based Online Public Access Catalog (OPAC) system integrated with a user activity tracking feature. The system was developed using the CodeIgniter 4 framework with the Waterfall methodology. Black Box testing results on 80 functional scenarios showed a 100 percent success rate in interface functionality execution. The main contribution of this study lies in the automatic recording of query logs and click interactions, which are visualized through a descriptive analytics dashboard, as well as the integration of the WhatsApp API to improve the efficiency of circulation administration processes. This system provides an initial empirical foundation for school administrators in formulating library collection procurement policies that are adaptive to students’ reading interests.
Segmentasi Gudang E-Commerce Berdasarkan Biaya Logistik dan Pola Transaksi Menggunakan Metode K-Means dan Fuzzy C-Means Saidina Ali Habib; Aditia Yudhistira
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.23-1.3457

Abstract

E-commerce multi-warehouse distribution systems face the challenge of logistics data segmentation due to heterogeneous and unbalanced transaction characteristics, as well as high overlap between variables. This study aims to segment warehouses based on logistics costs and transaction patterns using the K-Means and Fuzzy C-Means algorithms. The study integrates ratio-based feature engineering through the Logistics Cost Ratio (LCR), Value Density (VD), and Transaction Intensity (TI), and applies robust scaling and 1% outlier trimming to improve clustering stability. The dataset consists of 13,550 transactions from four main warehouses, yielding 13,284 valid data points after preprocessing. Evaluation was conducted using the Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The results show that K-Means with k = 3 yields the best performance with a Silhouette Score of 0.421, a DBI of 0.584, and a CHI of 12,273.98. Ratio-based feature transformation was proven to produce a more balanced and interpretable cluster distribution compared to the use of raw data. The resulting segmentation consists of high-efficiency clusters, regular transaction clusters, and high-logistics-cost clusters that can be used as a basis for data-driven logistics distribution decision-making in multi-warehouse e-commerce systems.
Evaluasi Model BERT Untuk Intent Recognition Pada Chatbot Edukasi Etika Penggunaan AI Dalam Lingkungan Akademik Ayunda Kusuma Wardani; Aris Tjahyanto
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.23-1.3463

Abstract

The development of Artificial Intelligence (AI) technology in education provides convenience in the learning process, but also creates challenges related to understanding the ethics of AI use in academic environments. This study aims to evaluate the performance of several BERT model variants in performing intent recognition for an educational chatbot on the ethical use of AI, particularly in higher education environments. The models used include IndoBERT, Multilingual BERT, and DistilBERT. The dataset consists of 700 data points with 7 intent categories developed using a Retrieval-Augmented Generation (RAG) approach based on the 2024 guideline book on the use of Generative AI from the Ministry of Education, Culture, Research, and Technology. The models were evaluated using accuracy, precision, recall, and F1-score metrics, while also handling out-of-scope (OOS) questions by comparing confidence threshold and entropy-based detection methods. The results show that IndoBERT achieved the best performance, with accuracy, precision, recall, and F1-score values of 97 percent, outperforming Multilingual BERT and DistilBERT. In addition, the entropy-based detection method achieved an accuracy of 95 percent and performed better in detecting out-of-scope questions compared to the confidence threshold method. These findings indicate that IndoBERT is an effective model for intent recognition in an educational chatbot on the ethical use of AI in academic environments.
Pengembangan Sistem Informasi Logistik Berbasis Web Dengan Fitur BAST Menggunakan Metode Waterfall Hamid Abdul Rozak; Giat Karyono
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.23-1.3468

Abstract

The logistics information system at Universitas Amikom Purwokerto does not yet provide a digital Handover Report (BAST) feature. This study develops a BAST module integrated into a web-based logistics system using the Laravel framework and MySQL through the Waterfall method. The BAST feature is designed to be integrated with goods transaction data through a transactional checklist mechanism, allowing users to prepare, save, and print documents without manually re-entering data. Testing was conducted through Black Box Testing with 7 test scenarios and User Acceptance Testing (UAT) involving 2 users, covering aspects of functionality, ease of use, and document printing efficiency. All test scenarios were successfully executed according to requirements, and the system received good to very good ratings across all UAT aspects, indicating that the integration of the BAST feature can significantly improve the efficiency of logistics administration. Future research is recommended to use a more standardized usability measurement method, such as the System Usability Scale (SUS).
Prediksi Harga Cryptocurrency Multi-Aset Menggunakan Machine Learning dan Deep Learning Yusuf Nur Alam; Berlilana
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.23-1.3473

Abstract

Cryptocurrency price volatility requires predictive models capable of accurately capturing non-linear patterns. This study predicts the price of Bitcoin (BTCUSDT) as the main asset, as well as Ethereum (ETHUSDT) and Ripple (XRPUSDT) as comparison assets, using Decision Tree, Random Forest, XGBoost, and LSTM models. The novelty of this study lies in the analysis of temporal data leakage and the evaluation of model extrapolation capability within a uniform experimental framework. Daily historical data were processed through cleaning, correlation analysis, variable selection, and sequential 70:30 data splitting. The prediction target was defined as the next-day closing price to avoid data leakage, and the models were evaluated using time-series cross-validation with RMSE, MAPE, and R² metrics. The results show that the best-performing model differs for each asset: LSTM outperformed other models for BTC and XRP, while Random Forest performed best for ETH, with R² values ranging from 0.60 to 0.98. Tree-based models tended to produce flat predictions when test prices exceeded the training data range. These findings emphasize the importance of defining prediction targets, applying temporal validation, and conducting cross-asset evaluation in selecting appropriate models for cryptocurrency price prediction.
Penerapan Blockchain untuk Verifikasi Sertifikat Akademik di Pendidikan Tinggi: Tinjauan Sistematis Julian Lirama Junior Pandari; Krismiyati; Theophilus Wellem
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.23-1.3475

Abstract

Digital transformation in higher education requires secure and efficient academic certificate verification to address the limitations of conventional systems, such as the risk of forgery and slow processing. Blockchain technology offers a transparent distributed data recording solution; however, its implementation in developing countries still faces challenges related to infrastructure and regulation. This study uses the Systematic Literature Review (SLR) method guided by PRISMA. Literature searches were conducted in Scopus, IEEE Xplore, Google Scholar, ACM Digital Library, and SpringerLink databases to explore publications from 2017 to 2024. From all identified articles, 35 studies were selected through a screening and quality assessment process, then analyzed narratively and synthesized using a thematic approach. This study produces a comprehensive synthesis of the evolution of blockchain implementation in academic certificate verification and identifies a blockchain model that integrates Hyperledger Fabric, Verifiable Credentials, and the InterPlanetary File System as a hybrid architectural approach. The proposed model is not viewed as a replacement for existing national systems, but rather as an additional trust layer that complements them. This approach is considered capable of balancing data security, scalability, and resource efficiency in developing countries, while also identifying research gaps for further empirical validation. The limitation of this study lies in its literature review nature, meaning that its findings remain conceptual.
Analisis Spasio-Temporal Traffic Pattern via Interactive Dashboard: Rancang Bangun Decision Support System demi Eskalasi Situational Awareness Keselamatan Maritim di VTS Merak Mohammad Darsoni; Deddy Pratama; Wandi Febriansyah
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.23-1.3478

Abstract

The Sunda Strait is a strategic shipping lane with a high density of vessel traffic, which increases the potential risk of maritime accidents. Therefore, analyzing traffic density patterns is essential to support safer and more efficient navigation management. This study aims to identify vessel traffic density patterns using machine learning-based clustering techniques, as well as to compare the performance of the K-Means and DBSCAN algorithms. The data used are secondary data obtained from VTS Merak, consisting of daily vessel counts categorized based on Traffic Separation Scheme (TSS) routes, namely Passing North, Passing South, Crossing West, Crossing East, and Vessel Not Using TSS. The data were processed through preprocessing stages, including data cleaning and normalization using the Min-Max Scaling method. The analysis was conducted by applying K-Means and DBSCAN algorithms and evaluated using the Silhouette Score and Davies-Bouldin Index. The results indicate that DBSCAN is more effective in identifying complex density patterns, forming density-based clusters, and detecting anomalies in vessel trajectories. In contrast, K-Means produces more structured clusters but is less flexible in handling irregular data patterns. Therefore, DBSCAN is considered superior for analyzing vessel traffic density in the Sunda Strait and has strong potential to support decision-making in improving maritime safety.

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