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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 1,026 Documents
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Penggunaan Delone And Mclean Dalam Mengevaluasi Kesuksesan Sistem Informasi Terhadap E-Ppt Fakultas Ilmu Komputer Universitas Sriwijaya Salsabila, Adella; Yunika Hardiyanti, Dinna
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2955

Abstract

The e-PPT system of the Faculty of Computer Science Universitas Sriwijaya was developed to support online academic administrative services. However, based on observations, users sometimes experience problems such as connection interruptions, a user interface that is not user friendly, and delays in document processing. This study aims to evaluate the success of the e-PPT system implementation using the DeLone and McLean 2003 model with a quantitative approach based on Partial Least Squares Structural Equation Modeling PLS SEM. Data were collected from 353 active students through an online questionnaire, measuring six dimensions namely system quality, information quality, service quality, use, user satisfaction, and net benefits. The results show that information quality and service quality have a significant influence on use and user satisfaction, with service quality being the main driver of system use path coefficient 0.416. Service quality is significant for user satisfaction but not for use p value 0.081. Both use and user satisfaction contribute significantly to net benefits such as time and cost savings. Although the validity and reliability of the instruments were confirmed AVE greater than 0.5 and Cronbachs Alpha greater than 0.7, weaknesses in service quality such as connection errors and a less user friendly interface still need improvement. This study recommends improving administrative responsiveness, simplifying the interface, and adding qualitative analysis for deeper contextual understanding. These findings are relevant for developing more effective academic information systems in higher education institutions.
Implementasi Algoritma Apriori Dalam Menentukan Penyusunan Buku di Perpustakaan Peter Gelu, Leonard; Chrisinta, Debora; Eduardo Simarmata, Justin
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2956

Abstract

University libraries play an important role in supporting the learning and research processes. However, book borrowing transaction data is often not optimally utilized to understand user needs. This study aims to identify book borrowing patterns at the University of Timor Library in order to find associations between book types that can be used for reading recommendations and collection management. The method used is association rule mining with the Apriori algorithm on book borrowing transaction data for the 2023–2024 period. The analysis results show strong association patterns between several book titles, particularly in the fields of finance and taxation, such as “Financial Statement Analysis and Its Application,” “Value Added Tax,” and "Techniques for Drafting Local Regulations: Regarding Local Taxes and Local Levies," which had a support value of 0.011494, confidence of 100%, and lift of 87. Another strong pattern was found in the field of information technology, namely between the books “Data Mining: [For Data Classification and Clustering]” and “A Brief Introduction to Python 3 Programming” with a lift of 58, indicating a correlation between the topics of programming and data analytics. These findings confirm that library users tend to borrow books with related themes, so the results of this study can be used for collection planning, shelf arrangement, and the development of data-based recommendation systems in libraries.
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lapatta, Nouval; Wiria Nugraha, Deny; Wirdayanti; Ar Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Implementasi Metode Exponential Smoothing Pada Sistem Informasi Peramalan Penjualan Produk Olahan Daun Kelor Untuk Meningkatkan Efisiensi Produksi Alsyah Harahap, Dymas Fatthur Rohim; Putri, Raissa Amanda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2959

Abstract

PT. Keloria Moringa Jaya, based in Medan, North Sumatra, is engaged in the processing and distribution of products made from moringa leaves (Moringa oleifera). The company is still constrained by the lack of an organized sales recording system, making it difficult to monitor performance, analyze trends, evaluate marketing strategies, and make the right business decisions. This study aims to apply the exponential smoothing method in the sales forecasting information system at PT. Keloria Moringa Jaya. This method is used to forecast product sales based on the company's historical data. The sales data used covers the period from May 2024 to April 2025 with a total of 12 monthly data. The system was developed using the Laravel framework and is equipped with features for inputting product data, sales data, and alpha parameter data, as well as displaying the forecast results and their accuracy values. The test results show that the MAD, MSE, and MAPE values for all products are in the accurate category. This research provides practical contributions to the company in supporting more efficient production decision-making and sales strategies.
Klasifikasi Indeks Standar Pencemaran Udara Menggunakan Algoritma Catboost Dengan Teknik Balancing Data Random UnderSampling Aditya, Aldy; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2971

Abstract

Air quality is an important factor that affects public health and the environment. The Air Pollution Index is used as an indicator to measure the level of air pollution in a region. The main challenge in the air quality classification process is the imbalance of data that can affect the modeling results. This study aims to analyze the performance of the Categorical Boosting (CatBoost) algorithm in ISPU classification by applying the Random Under sampling technique to overcome class imbalance. The dataset used was obtained from air quality monitoring in DKI Jakarta for the period 2020–2024 with a total of 5,386 records and 12 attributes. The research stages included data collection, data cleaning, data transformation, data balancing, feature selection using Recursive Feature Elimination (RFE), modeling with CatBoost, and model evaluation using a confusion matrix. The feature selection results showed five main features that had the most influence, namely PM10, PM2.5, SO2, NO2, and max. The CatBoost model built with the best parameters produced an accuracy of 98 percent, precision of 100 percent, recall of 98.91 percent, and an F1-score of 99.44 percent. Thus, the application of CatBoost and Random Under sampling techniques proved to be effective in improving ISPU classification performance. The results of this study are expected to be used as a decision support system in efforts to mitigate the impact of air pollution in DKI Jakarta.
Penerapan Supply Chain Management untuk Efisiensi Rantai Pasok Produk Cokelat Milandari, Novi; Irawati, Novica; Rahayu, Elly
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2974

Abstract

Business competition in the era of globalization demands that companies operate quickly, efficiently, and adaptively, including in the snack food industry. PT Halven Jaya, as a distributor of chocolate products, continues to face distribution challenges, such as high levels of leftover inventory, frequent product damage, and sales revenue that has yet to meet targets. These conditions indicate inefficiencies in the supply chain, which directly impact company performance and customer satisfaction. This study aims to design and implement a web-based Supply Chain Management (SCM) system to improve product distribution efficiency. The research employed a descriptive qualitative method through interviews, observations, and documentation at PT Halven Jaya. Analysis was conducted to identify weaknesses in distribution, design a system solution, and test the implementation of the SCM software. The results indicate that the developed SCM system can record incoming and outgoing stock, detect damaged products, and provide real-time distribution reports. Its implementation reduces the risk of overstock and product damage, enhances transparency in the distribution flow, and provides strategic information for management in decision-making. Thus, the implementation of a web-based SCM system has been proven to increase operational effectiveness, improve cost efficiency, and support the achievement of company sales targets. These findings offer important managerial implications for enhancing strategic decision-making in supply chain management.
Implementasi Metode WP Dan TOPSIS Dalam Menentukan Perbaikan Alat Berat : Studi kasus: PT.Pilaren Ariyanto, Arya; Samsudin; Dedi Irawan, Muhammad
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2976

Abstract

This study aims to implement the WP and TOPSIS methods in the decision-making process for heavy equipment repairs at PT. Pilaren. The WP method is used to assign weights to various criteria such as damage condition, operational impact, spare parts availability, repair costs, and other factors to determine the best alternative in a non-linear manner. Meanwhile, the TOPSIS method helps to assess the alternatives that are closest to the ideal solution and furthest from the worst solution objectively. A web-based decision support system was developed using the Laravel framework and MySQL database to facilitate data management and calculation processes. The results of this system are expected to improve efficiency, transparency, and objectivity in determining the priorities for heavy equipment repairs in the company.
Klasterisasi Gaya Belajar Mahasiswa Berbasis VARK dengan Algoritma DBSCAN untuk Personalisasi E-Learning Maulana, Iqbal; Witanti, Wina; Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2980

Abstract

The incompatibility between e-learning systems and students' learning styles remains a major challenge in improving the effectiveness of learning in Indonesian universities. This study aims to classify the learning styles of students at Jenderal Achmad Yani University using the VARK (Visual, Auditory, Read/Write, Kinesthetic) model, enriched with the Kano method. Data were collected from 1,000 students through the VARK-Kano questionnaire and analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. The clustering process was carried out by determining the optimal parameters using the k-distance plot, and the validity of the clusters was assessed using the Silhouette Score. The results showed that DBSCAN could form representative clusters of student learning styles and effectively detect data noise. This study contributed to the development of a cluster-based adaptive e-learning framework that could be implemented in Indonesian universities. These findings could serve as a basis for designing adaptive learning strategies that are more suited to student characteristics, thereby increasing the effectiveness of e-learning and learning motivation.
Analisis Tripartit Keamanan Docker: Evaluasi Metode Deteksi Kerentanan, Registry, dan Layanan Widyanto Utomo, Arya; Ghozi, Wildanil; Umam, Chaerul
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2983

Abstract

The adoption of Docker as the standard container platform poses new security challenges, particularly regarding vulnerabilities in public images. This study evaluates the effectiveness of three vulnerability scanning methods for Docker images: direct scanning, vendor-integrated SBOM scanning, and cross-vendor SBOM scanning, using Trivy and Grype on 36 images from three major registries (Docker Official, Bitnami, Chainguard). The results show that direct scanning and vendor-integrated SBOM scanning produce identical detections (12,023 vulnerabilities with Trivy; 8,950 with Grype), while cross-vendor SBOM scanning decreases dramatically by more than 90% (only 800–790 findings). Chainguard proved to be the most secure, while Docker Official was the most vulnerable (e.g., python:latest had 2,053 vulnerabilities). Programming language-based images (Rust: 3,825; Node.js: 3,816) were also riskier than specialized services (Redis: 341; MongoDB: 351). This research developed a framework for evaluating the effectiveness of cross-approach vulnerability scanning and strengthened the theory of software supply chain security through the concept of SBOM provenance dependency, which became the basis for the development of a multi-phase vulnerability scanning framework and recommendations for secure container implementation.
Pengembangan Sistem Pemetaan Titik Bencana Longsor di Desa Cikondang Hartono, Rudi; Rahmat Hidayat, Cepi; Ikhlas Ramadhan, Teguh; Nur Izza, Azmi; Silpia Arum, De Sipa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2994

Abstract

Desa Cikondang, Kecamatan Cineam, Kabupaten Tasikmalaya, merupakan wilayah dengan kerentanan tinggi terhadap bencana tanah longsor akibat kondisi topografi dan curah hujan yang tinggi. Kejadian longsor pada awal tahun 2025 telah menyebabkan dampak signifikan terhadap perekonomian dan infrastruktur warga. Untuk mendukung upaya mitigasi bencana, penelitian ini bertujuan mengembangkan sistem pemetaan titik bencana longsor berbasis Sistem Informasi Geografis (SIG) yang akurat dan mudah diakses. Penelitian ini menggunakan metode rekayasa perangkat lunak Extreme Programming (XP) yang mencakup tahapan perencanaan, desain, pengkodean, dan pengujian. Hasil dari penelitian ini adalah sebuah sistem aplikasi berbasis web yang mampu menyajikan peta interaktif titik rawan longsor di Desa Cikondang, beserta informasi detail dan visualisasi kondisi lapangan. Pengujian usabilitas sistem menggunakan metode System Usability Scale (SUS) yang melibatkan 20 partisipan menghasilkan skor rata-rata 81.25, yang masuk dalam kategori Excellent dan menunjukkan bahwa sistem sangat mudah digunakan serta diterima dengan baik oleh pengguna.

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