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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 1,145 Documents
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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.
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

Website xyz.or.id merupakan sebuah lembaga penelitian yang mengelola data dan informasi. Mengingat pentingnya data yang dikelola, website ini rentan menjadi sasaran serangan siber, terutama Cross-Site Scripting (XSS) yang dapat menimbulkan risiko serius seperti pencurian data dan pembajakan sesi pengguna. Penelitian ini berfokus pada investigasi keamanan terhadap mekanisme validasi input pada sistem pendaftaran. Penelitian bertujuan untuk mengidentifikasi dan menganalisis kerentanan keamanan pada website xyz.or.id menggunakan metode black-box penetration testing. Metode penelitian mencakup tahapan information gathering, penetration testing analysis, dan report. Hasil pengujian mengidentifikasi total 6 kerentanan keamanan, terklasifikasi menjadi 2 tingkat tinggi (high), 1 menengah (medium), dan 3 rendah (low). Analisis uji penetrasi menemukan adanya kerentanan XSS pada form input "Nama Lengkap" di halaman pendaftaran, di mana payload yang diinjeksikan berhasil dieksekusi di sisi klien. Temuan ini memberikan bukti empiris bahwa mekanisme validasi input dan kebijakan keamanan website belum optimal. Penelitian ini menghasilkan rekomendasi teknis perbaikan, meliputi implementasi input validation, output encoding, dan konfigurasi Content Security Policy (CSP) untuk mencegah terjadinya eksploitasi dari pihak luar.
Prediksi Kesiapan Membaca Anak Taman Kanak-Kanak Menggunakan Algoritma Backpropagation Neural Network Siti Khoirotul Maftukhah; Iwan Setiawan Wibisono
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.23-1.3129

Abstract

Reading ability is an important indicator of cognitive development in early childhood, particularly in kindergarten. This study aims to predict children’s reading readiness levels using the Backpropagation Neural Network (BPNN) algorithm. The data were obtained through observations and tests conducted on kindergarten children, with variables including age, letter recognition ability, phonemic ability, and concentration level. The BPNN model was trained by dividing the data into training and testing sets, using a single hidden layer and a sigmoid activation function. Model evaluation shows good predictive performance, with a Root Mean Squared Error (RMSE) of 0.527, indicating an average prediction error of less than 1% relative to the target values. These results confirm the ability of BPNN to recognize nonlinear patterns and accurately predict children’s reading readiness. Therefore, the application of BPNN can assist teachers and parents in designing appropriate learning interventions tailored to children’s developmental needs.
Pengembangan Sistem Analisis Defect Proses Jahit Berbasis Fishbone Diagram dan FMEA Menggunakan Aplikasi Web Widia Jelita 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.23-1.3135

Abstract

The sewing process is one of the important stages in clothing production, but defects often occur that can reduce product quality and increase production costs. This study aims to develop a web-based sewing process defect analysis system that utilizes the Fishbone Diagram and Failure Mode and Effects Analysis (FMEA) methods. This system is designed to identify the most common types of defects, classify their causes, including human factors, machines, work methods, materials, and the environment using Fishbone Diagrams, and evaluate the risk level of each factor through FMEA. The analysis results show that human factors, such as operator skills and accuracy, as well as machine conditions, such as needles and components that are subject to wear and tear, are significant causes of defects. Through FMEA, the system provides a risk assessment so that repair priorities can be determined more objectively. The recommendations generated include operator training, periodic machine maintenance, and the implementation of standard operating procedures. The development of this web application contributes to providing a systematic, easily accessible defect analysis tool that can be applied in the garment industry to improve the quality of the sewing process.
Sistem Pendukung Keputusan Untuk Memilih Pemasok Terbaik Menggunakan Metode Simple Additive Weighting (SAW) Farid Achmad; 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.23-1.3139

Abstract

Choosing the right supplier is an important part of maintaining an effective and efficient supply chain. In reality, this decision can be very difficult because it involves many different factors, such as price, quality, delivery time, and supplier reliability. This study presents a Decision Support System (DSS) that uses the Simple Additive Weighting (SAW) method to help select the best supplier based on these criteria. The SAW method is known for its straightforward approach to multi-criteria decision making, making it an ideal choice for this type of task. A prototype of the system was developed and tested using sample supplier data to assess its performance. Based on the results, the system was able to provide accurate and consistent recommendations, helping users identify the most suitable suppliers for their needs. This research offers a practical tool that can support businesses in making better supplier decisions and demonstrates how the SAW method can be effectively applied in everyday business scenarios.
Literatur Review Integrasi Artificial Intelligence dalam Pembelajaran Sains di Pendidikan Menengah 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.
Peramalan Penjualan Mitra Konsinyasi Menggunakan SARIMA Berbasis Grid Search dan Evaluasi Akurasi Muhamad Hasanudin; 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.23-1.3147

Abstract

This study aims to forecast the sales of four consignment partner products using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method based on monthly historical sales data from January 2023 to August 2025. The consignment system faces a high risk of product returns due to demand uncertainty, making accurate and reliable forecasting methods essential. The novelty of this research lies in the application of automatic parameter optimization using Grid Search to reduce subjectivity in selecting SARIMA models. The results indicate that the optimized SARIMA models provide good predictive performance and satisfy residual diagnostic tests. The KSP product shows the highest accuracy with a MAPE value of 7.69%, followed by KSO at 17.28%, while MO and MSM yield MAPE values of 25.54% and 27.55%, respectively, which are still acceptable for short-term operational planning. These findings confirm that a Grid Search–based SARIMA approach can serve as a reliable basis for decision-making in inventory control and in mitigating the risks of overstock and stockout in consignment schemes.
Prediksi Popularitas Lagu Berdasarkan Data Multi-Platform Streaming Menggunakan Metode Decision Tree Ichsan Zaki Heryadi; Yulison Herry Christanto; Gunawan Abdillah
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.23-1.3149

Abstract

Penelitian ini bertujuan untuk memprediksi popularitas lagu menggunakan metode Decision Tree berdasarkan integrasi data multi-platform streaming. Dataset yang digunakan mencakup indikator performa lagu dari berbagai platform digital, antara lain Spotify, Youtube, Tiktok, dan platform streaming lainnya, yang meliputi jumlah streams, views, likes, serta jangkauan playlist. Pendekatan Decision Tree dipilih karena kemampuannya dalam melakukan klasifikasi sekaligus memberikan interpretasi yang jelas terhadap faktor-faktor yang memengaruhi Tingkat popularitas lagu. Metodologi Penelitian meliputi tahapan preprocessing data, pemilihan fitur numerik lintas platform, konstruksi label popularitas global berbasis agregasi indikator streaming, pembagian data latih dan data uji, serta pelatihan model Decision Tree CART. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan f1-score untuk mengukur performa klasifikasi. Hasil penelitian ini menunjukkan bahwa model mampu mengklasifikasikan lagu populer dan tidak populer dengan Tingkat akurasi yang tinggi. Analisis feature importance mengungkapkan bahwa jumlah streams di Spotify, views di Tiktok, serta jangkauan playlist di Youtube merupakan faktor yang paling berpengaruh terhadap popularitas lagu secara global. Kontribusi utama penelitian ini terletak pada pemodelan prediksi popularitas lagu berbasis data multi-platform yang bersifat interpretable, serta pada analisis kontribusi relatif masing-masing platform streaming, yang belum banyak dibahas secara eksplisit dalam penelitian sebelumnya. Temuan ini diharapkan dapat dimanfaatkan oleh industry musik digital dalam mendukung pengambilan Keputusan strategis terkait promosi dan pemasaran lagu.
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.

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