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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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Penerapan Logika Fuzzy Tsukamoto dalam Sistem Pendukung Keputusan untuk Prediksi Persediaan Barang Lismawati Gulo; 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.23-1.3067

Abstract

Pengelolaan persediaan yang tidak tepat dapat menyebabkan kekurangan atau kelebihan stok sehingga mengganggu efisiensi operasional dan menurunkan profit perusahaan. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pendukung keputusan berbasis Logika Fuzzy Tsukamoto untuk memprediksi kebutuhan persediaan barang secara lebih akurat. Data yang digunakan berupa histori penjualan dan persediaan selama 24 bulan untuk N item, yang kemudian dipetakan ke dalam himpunan fuzzy pada variabel permintaan dan stok. Sistem dibangun menggunakan 15 aturan IF–THEN yang diproses melalui tahapan inferensi dan defuzzifikasi untuk menghasilkan nilai prediksi kebutuhan barang yang bersifat crisp. Hasil pengujian menunjukkan bahwa sistem mampu menghasilkan prediksi dengan tingkat kesalahan yang relatif rendah, dengan nilai MAE sebesar 12,7 unit dan RMSE sebesar 15,3 unit. Selain itu, sistem dapat memberikan klasifikasi kondisi persediaan berupa status aman, waspada, dan kritis sesuai dengan skenario pengujian. Temuan ini menunjukkan bahwa pendekatan Fuzzy Tsukamoto efektif dalam mendukung pengambilan keputusan manajemen persediaan dan mampu mengurangi risiko terjadinya kekurangan maupun kelebihan stok. Kebaruan penelitian ini terletak pada penerapan aturan fuzzy adaptif yang terintegrasi dengan data historis jangka panjang, sehingga menghasilkan prediksi persediaan yang lebih stabil dan konsisten dibandingkan pendekatan fuzzy statis. Pendekatan ini diharapkan dapat menjadi alternatif solusi dalam pengelolaan persediaan pada berbagai sektor usaha.
Evaluasi Kematangan Manajemen Perubahan TI Menggunakan COBIT 2019 Domain BAI07 Mia Hamzani Dianasari; Sudin Saepudin; Hendri Ekasatria; Mupaat
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.23-1.3090

Abstract

The website sukabumiupdate.com, as a high-traffic online media platform, routinely performs system updates, server migrations, and feature enhancements to maintain service speed and stability. These activities may pose risks of service disruption if not managed through a structured IT change management mechanism, particularly in the digital media industry, which demands real-time system availability. This study aims to evaluate the maturity level of the information technology change management process at sukabumiupdate.com using the COBIT 2019 framework, focusing on the BAI07 domain (Managed IT Change Acceptance and Transitioning), which plays a crucial role in ensuring successful system change acceptance and transition. The research adopts a quantitative descriptive approach with a case study method, involving observation, interviews, and documentation with four respondents. The assessment was conducted using a questionnaire instrument based on BAI07 activities, measured through the COBIT 2019 capability level assessment. The results indicate that the change management process has reached Capability Level 5 (Optimizing) with an achievement score of 88.46%, demonstrating that the process is consistently implemented, well-documented, and oriented toward continuous improvement. These findings address the research gap regarding the application of the BAI07 domain in the online media industry and provide both theoretical contributions to IT governance studies and practical guidance for digital media organizations in enhancing the effectiveness and efficiency of IT change management.
A Prediksi Prestasi Siswa Menggunakan Algoritma Decision Tree C4.5 Umi Halimatussa'diyah; 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.23-1.3111

Abstract

This study aims to predict student achievement levels at MA Darul Muna using the C4.5 Decision Tree algorithm as a data mining method to support academic decision making. The data used consists of scores for several core subjects, including Al-Qur'an Hadith, Aqidah, Fiqh, PPKN, and Indonesian Language. The research stages included data collection and pre-processing, C4.5 model formation, and evaluation using accuracy, precision, and recall. The resulting model achieved 89.7% accuracy for the student dataset for one academic year, indicating that subject grades were the most influential attribute in achievement classification. These findings indicate that C4.5 can help schools identify students who are potentially underperforming so that learning interventions can be implemented more quickly. The research is still limited in terms of the number of academic variables and the scope of a single institution, so further research opportunities exist in developing non-academic variables and a broader dataset.
Deteksi Potensi Putus Sekolah Menggunakan Algoritma C4.5 Studi Kasus SMP di Giligenting Fauzi Helmi; Iddrus; Miftahul Arifin
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.23-1.3114

Abstract

Dropout in island regions such as Giligenting District is a crucial issue influenced by geographical and academic constraints. This study aims to predict the potential dropout risk among junior high school students using data mining techniques with the C4.5 algorithm. The dataset used consists of 358 student records covering demographic, academic, social, and economic attributes. The research stages include preprocessing, attribute weighting, and classification using RapidMiner with an 80:20 split data validation scheme. The testing results show that the model achieved an accuracy of 62.5 percent, precision of 68.42 percent, and recall of 76.47 percent. Based on attribute weight analysis, the most dominant factors influencing dropout risk are Average Grade and Distance from Home to School, followed by Attendance and Family Dependents. This study contributes as a foundation for an early warning system, enabling schools to carry out priority interventions for students with low academic indicators and long travel distances to school.
Deteksi Anomali Peminjaman Buku Menggunakan Algoritma Isolation Forest untuk Meningkatkan Efisiensi Layanan Miftahul Arifin; Rizal Sapta Dwi Harjo; Ilman Firmansa; Muhammad Faizal Ilham
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.23-1.3117

Abstract

University libraries require systems capable of detecting abnormal book borrowing behavior to maintain service effectiveness. Without an anomaly detection mechanism, libraries are prone to various operational issues, such as increased manual verification workload for librarians, delays in identifying problematic borrowings, and discrepancies in book inventory records. Based on preliminary data prior to this study, an average of 8–12% of transactions exhibited irregularities, such as extreme delays, borrowings outside operational hours, or unusually large numbers of books borrowed in a single transaction. This condition highlights the need for an analytical approach that can provide faster and more consistent detection than manual inspection. This study applies the Isolation Forest algorithm to detect anomalies in the book borrowing dataset of the Wiraraja University Library. The borrowing data are processed through data cleaning, feature extraction, and standardization before being used for model training. The results show that the model achieves an accuracy of 90% in identifying normal borrowing patterns and successfully detects transactions with extreme characteristics as anomalies. These findings confirm that a machine learning–based approach can improve library operational efficiency while minimizing the risk of abnormal transactions that were previously difficult to detect through manual processes.
Analisis Keamanan Sistem Keuangan Daerah Menggunakan Black Box Penetration Testing Muhammad Addissahhilna; Chaerul Umam
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.23-1.3120

Abstract

Information system security is a crucial aspect in supporting digital-based regional financial services. This study offers novelty by implementing penetration testing on the financial service system of Pati Regency, which previously had not been comprehensively evaluated. The black-box testing method is used to simulate external attacks, while the Penetration Testing Execution Standard (PTES) is selected because it provides structured and replicable work stages, thereby improving the accuracy of vulnerability identification. The testing results reveal serious vulnerabilities, such as exposure of the phpinfo() page and the use of default credentials in the login process, which are classified as high risk based on CVSS 3.1. These vulnerabilities had not been detected by the system administrators prior to testing. The implementation of security recommendations, including brute-force protection, removal of sensitive pages, and strengthened authentication mechanisms, is able to significantly reduce the level of risk. The main contribution of this research lies in providing a structured and applicable security evaluation model for local governments to enhance the resilience of digital financial services against cyberattacks.
Evaluasi Celah Keamanan Cross-Site Scripting (XSS) pada Website Menggunakan Black-box Penetration Testing Muhammad Faiz Fadllan; Khairunnisak Nur Isnaini; Ali Nur Ikhsan
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.23-1.3122

Abstract

The xyz.or.id website is a research institution that manages data and information. Given the importance of the data it manages, this website is vulnerable to cyber attacks, especially Cross-Site Scripting (XSS), which can pose serious risks such as data theft and user session hijacking. This study focuses on investigating the security of the input validation mechanism in the registration system. The study aims to identify and analyze security vulnerabilities on the xyz.or.id website using the black-box penetration testing method. The research method includes the stages of information gathering, penetration testing analysis, and reporting. The test results identified a total of 6 security vulnerabilities, classified into 2 high, 1 medium, and 3 low levels. The penetration test analysis found an XSS vulnerability in the “Full Name” input form on the registration page, where the injected payload was successfully executed on the client side. This finding provides empirical evidence that the input validation mechanism and website security policy are not yet optimal. This research resulted in technical recommendations for improvement, including the implementation of input validation, output encoding, and Content Security Policy (CSP) configuration to prevent exploitation by external parties.
Perbandingan IBAC dan ABAC Sebagai Strategi Keamanan Siber Berbasis Zero Trust Architecture (ZTA) Erwin Maulana; Herdi Ashaury; Melina
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.23-1.3127

Abstract

The rapid development of web technologies has increased exposure to various types of cyberattacks, making traditional perimeter-based security approaches less effective. Zero Trust Architecture (ZTA), with its principle of “never trust, always verify,” offers a more adaptive approach to addressing modern threats. This study aims to analyze and compare the effectiveness of two access control models within the ZTA framework—Identity-Based Access Control (IBAC) and Attribute-Based Access Control (ABAC)—in enhancing web application security. The research employs a quantitative experimental approach by developing two ZTA-based web application prototypes implementing IBAC and ABAC. Three common cyberattacks—SQL Injection, Cross-Site Scripting (XSS), and Brute Force—are tested using Detection Rate as the evaluation parameter. The results show that both models achieve a 100% Detection Rate against all simulated attacks, indicating that they provide an equivalent level of protection within the context of this experiment. Nevertheless, conceptual analysis reveals that ABAC offers advantages in terms of granularity and contextual awareness, as it is capable of dynamically evaluating user attributes, object attributes, and environmental conditions. These characteristics make ABAC more aligned with the principle of continuous verification in Zero Trust, particularly for long-term implementation in modern web applications.
Integrasi Artificial Intelligence dalam Pembelajaran Sains di Pendidikan Menengah: Sebuah Tinjauan Literatur Nissa Ul Awal; Nurmalahayati Nurdin
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.23-1.3140

Abstract

The rapid advancement of digital technology has opened opportunities for the use of artificial intelligence (AI) in learning, particularly in science education, which requires an understanding of complex concepts. This study aims to examine the development of research and the implementation of AI in science learning at the secondary education level (junior and senior high schools or equivalent). The method employed is a Systematic Literature Review (SLR) using the PRISMA approach on 41 scientific articles published between 2020 and 2025. The results indicate a significant increase in the number of research publications related to AI in science education, with physics as the most dominant field, followed by general science, chemistry, and biology. The most widely studied types of AI include educational chatbots, interactive simulations and videos, AI-based learning analytics, adaptive learning systems, as well as virtual reality and augmented reality (VR/AR) technologies. Overall, the findings show that the application of AI has a positive impact on student learning outcomes, learning motivation, learning personalization, and assessment effectiveness. However, this review also identifies several major challenges, including infrastructure limitations, low digital literacy among educators, and ethical and data protection issues. This study provides an overview of research trends and implications for educational policy and future research related to the integration of AI in science learning at the secondary education level.
Penilaian Otomatis Jawaban Esai SMA Menggunakan Sentence-BERT dan Hybrid Levenshtein-Jaccard dengan Akurasi Hybrid Timoti Michael Sitorus; Edvin Ramadhan; Fatan Kasyidi
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.23-1.3141

Abstract

Essay assessment remains a persistent challenge in many schools due to the time-consuming nature of manual grading and the variability that arises from assessor subjectivity. This situation highlights the need for an automated scoring system capable of producing fast, consistent, and teacher-comparable evaluations. This study proposes a hybrid approach for automatic essay scoring by combining semantic similarity from Sentence-BERT with lexical features derived from Jaccard Similarity, Levenshtein Similarity, Keyword Coverage, and Length Penalty. The five similarity components are integrated using a weighted aggregation scheme and calibrated to the 0–100 scoring scale through linear regression. The model was tested on a dataset of high-school essay responses accompanied by manual teacher scores. Experimental results indicate that the proposed system performs reliably, achieving a Mean Absolute Error (MAE) of 3.58 and a Root Mean Square Error (RMSE) of 4.48 on the test set. The model also demonstrates strong practical alignment with teacher scoring, reaching an agreement rate of 87.30% within a tolerance of ±7 points. These findings suggest that the hybrid method can approximate human scoring patterns with a high degree of consistency, providing a promising tool to support objective and efficient assessment processes in educational settings.

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