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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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Perbandingan Apriori dan FP-Growth dalam Association Rule Pola Pembelian Sparepart Preventive Maintenance Anita; Arief 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.23-1.3427

Abstract

Spare part inventory management is an important aspect of preventive maintenance activities. This study aims to analyze the performance comparison between the Apriori and FP-Growth algorithms in identifying spare part purchasing patterns for preventive maintenance activities. The main problem in spare part management is the lack of optimal inventory planning, which can lead to overstock or stock shortages. The method used in this study is Association Rule Mining with two algorithms, namely Apriori and FP-Growth, applied to spare part purchasing transaction data. The analysis process was conducted through data preprocessing, frequent itemset generation, and association rule formation using minimum support and confidence parameters. The results indicate that the FP-Growth algorithm performs more efficiently than Apriori in terms of computation time and the ability to handle large datasets. Meanwhile, the Apriori algorithm is easier to implement and understand. The resulting association patterns can be used as a basis for decision-making in more effective and efficient spare part inventory management. Therefore, this study is expected to contribute to improving data-driven preventive maintenance strategies.
Analisis Keamanan Website UPT RSUD RAA Soewondo Pati Berdasarkan Hasil Penetration Testing Menggunakan Owasp Dani Yudanta Prapaskia; 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.23-1.3429

Abstract

The development of technology in the healthcare sector has encouraged the utilization of web-based platforms to comprehensively support hospital service operations. This requires the implementation of strict security standards to protect the privacy of patients’ medical data. This study focuses on evaluating the security level of the official website of UPT RSUD RAA Soewondo Pati through penetration testing based on the OWASP framework. The evaluation stages included web infrastructure identification using Wappalyzer and vulnerability scanning using OWASP ZAP. Based on the testing results, several security vulnerabilities with varying levels of risk were identified, including SQL Injection, Cross-Site Scripting (XSS) threats, and vulnerabilities related to session management and authentication systems. In general, the system’s security profile falls into the medium-risk category, indicating that further improvements are required to reduce cyber threats. The use of OWASP guidelines in this study proved effective in identifying system weaknesses while also formulating mitigation strategies, such as optimizing server configuration, implementing secure coding practices, and improving authentication workflows.
Transfer Learning VGG16 untuk Deteksi Kanker Otak MRI: Analisis Komparatif CNN, FNN, LSTM Nesa Puspitasari; Imam Tahyudin
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.23-1.3431

Abstract

Kanker otak memiliki tingkat mortalitas tinggi akibat keterlambatan diagnosis, sehingga sistem deteksi dini yang akurat menjadi kebutuhan mendesak. Penelitian ini mengusulkan pendekatan transfer learning dua tahap (initial training dan fine-tuning) menggunakan VGG16 sebagai ekstraktor fitur, dikombinasikan dengan tiga arsitektur klasifikasi CNN, FNN, dan LSTM untuk deteksi kanker otak pada citra MRI. Kebaruan penelitian terletak pada perbandingan sistematis ketiga arsitektur tersebut dalam kerangka transfer learning pada dataset MRI berskala kecil (818 citra, rasio 80:20) dengan augmentasi data. VGG16+LSTM mencapai akurasi tertinggi (96,38%), diikuti VGG16+FNN (96,21%) dan VGG16+CNN (94,74%). Model terbaik diintegrasikan ke dalam aplikasi web sebagai sistem pendukung keputusan klinis untuk skrining awal. Hasil ini mengonfirmasi efektivitas transfer learning dua tahap dalam mengatasi keterbatasan data sekaligus meningkatkan performa klasifikasi berbasis MRI.
Pengembangan Sistem Manajemen Stok dan Penjualan Berbasis Web Dalam Mendukung Transformasi Digital Menggunakan Metode Prototype Pada Butik Merry’s Fashion Lampung Dhella Samputri; Halimah
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.23-1.3432

Abstract

Penelitian ini bertujuan mengembangkan sistem informasi manajemen stok dan penjualan berbasis web untuk mengatasi permasalahan pencatatan manual yang menyebabkan ketidaksesuaian data dan rendahnya efisiensi operasional. Metode yang digunakan adalah Prototype untuk memungkinkan pengembangan sistem secara iteratif berdasarkan kebutuhan pengguna. Sistem yang dikembangkan mencakup fitur pengelolaan stok, transaksi penjualan, purchase order, serta laporan terintegrasi dengan dukungan teknologi barcode dan data real-time. Hasil pengujian menunjukkan bahwa sistem mampu meningkatkan efisiensi proses operasional, mempercepat transaksi, serta mengurangi kesalahan pencatatan (error) dibandingkan metode manual. Selain itu, sistem juga meningkatkan kemudahan monitoring stok dan pengambilan keputusan. Dengan demikian, sistem yang dikembangkan efektif dalam mendukung transformasi digital pada Butik Merry’s Fashion Lampung.
Analisis Kesenjangan Pendidikan dan Usia Kerja Masyarakat Kabupaten Garut dengan K-Means Clustering Hamzah Nurrifqi Fakhri Fikrillah; Fahmi Fadillah 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.23-1.3433

Abstract

Penelitian ini bertujuan menganalisis kesenjangan pendidikan dan usia kerja masyarakat di Kabupaten Garut dengan menggunakan algoritma K-Means Clustering. Data penelitian berasal dari hasil verifikasi Dinas Sosial Kabupaten Garut tahun 2024 dengan jumlah 912.419 individu berusia 15 hingga 60 tahun. Atribut utama yang digunakan meliputi usia, pendidikan terakhir, dan pekerjaan, dengan penentuan jumlah cluster optimal melalui Elbow Method  K=3. Hasil analisis menunjukkan tiga cluster utama, yaitu Cluster 0 (32,4%) individu usia produktif akhir dengan pendidikan rendah dan dominasi pekerjaan informal, Cluster 1 (43,6%) individu muda dengan pendidikan menengah dan mayoritas belum bekerja, dan Cluster 2 (24,0%) individu produktif menengah dengan pendidikan menengah dan pekerjaan beragam. Validasi model menghasilkan nilai Silhouette Score sebesar 0,5855 dan Davies-Bouldin Index sebesar 0,5188, yang menunjukkan kualitas cluster cukup baik. Temuan ini menegaskan bahwa pendidikan merupakan faktor kunci dalam mobilitas sosial dan akses pekerjaan, serta dapat menjadi dasar kebijakan strategis untuk mengurangi ketimpangan sosial-ekonomi di Kabupaten Garut.
Perencanaan Sistem Informasi Pelaporan Kasus Kekerasan Berbasis Constraint: Studi pada Yayasan Sanggar Suara Perempuan Jeny Maryana Nuban; Eko Sediyono; Evi Maria
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.23-1.3434

Abstract

Violence case reporting systems in community-based organizations are often framed as technical solutions, overlooking the social and operational conditions that shape their use. This study examines how reporting system requirements are formed in such contexts and develops an information system design that responds to these conditions. The study draws on a qualitative, design-oriented case study conducted at Yayasan Sanggar Suara Perempuan (SSP). Data were collected through interviews and observations with key informants, analyzed thematically, and mapped onto the Ward and Peppard framework to inform system design. The findings show that system requirements are not purely functional but emerge as a configuration of interacting constraints, process fragmentation, data security risks, and social barriers that simultaneously bound integration, visibility, and user access. Fragmented reporting practices across unintegrated channels lead to data inconsistencies and delays, while concerns about identity exposure constrain user participation. In response, the study proposes an integrated reporting system featuring centralized data management, anonymous reporting, and role-based access control. These findings demonstrate that system design in high-risk, socially sensitive contexts cannot be derived from functional requirements alone but must be configured within a constraint-bounded design space that directly shapes and limits design decisions.
Klasifikasi Kultivar Jambu Semarang Menggunakan MobileNetV2 dengan Pendekatan Transfer Learning Ndaru Febrian Pujo Leksono; Bagus Adhi Kusuma; Andi Dwi Riyanto
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.23-1.3437

Abstract

Identifikasi kultivar jambu Semarang secara manual rentan subjektif. Penelitian ini mengusulkan model MobileNetV2 berbasis transfer learning untuk mengklasifikasikan 12 kultivar. Sebagai studi awal, keterbatasan utama penelitian ini adalah ukuran dataset yang sangat kecil, yakni 192 citra dengan distribusi seimbang 16 citra per kelas. Data diakuisisi pada kondisi bervariasi (in-the-wild) mencakup perbedaan latar belakang, pencahayaan, dan sudut pengambilan. Dataset dibagi dengan rasio 70% pelatihan dan 30% validasi. Hasil pengujian menunjukkan akurasi validasi mencapai 94% (F1-score rata-rata 0,94). Namun, analisis menggunakan confusion matrix dan evaluasi per kelas menunjukkan model masih kesulitan pada misklasifikasi fine-grained antar buah yang mirip. Mengingat ukuran data yang kecil serta ketiadaan pengujian menggunakan test set independen maupun cross-validation, performa model ini baru sebatas indikasi awal yang memiliki batasan generalisasi. Selain itu, tahap fine-tuning juga terbukti kurang signifikan. Sebagai rekomendasi, diperlukan peningkatan volume data, pengujian cross-validation, dan eksplorasi arsitektur bermekanisme attention pada penelitian selanjutnya.
Analisis Komparatif Image-to-Video Artificial Intelligence Pada Animasi 2D Menggunakan PSNR Dan SSIM Rizki Pamuji; Imam Tahyudin
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.23-1.3438

Abstract

The development of generative Artificial Intelligence (AI) technology has had a significant impact on the multimedia sector, particularly in image-to-video techniques that are capable of automatically transforming static images into videos. This study aims to analyze and compare the video quality produced by four AI platforms, namely Kling, Runway, PixVerse, and Pika, in the context of 2D animation. The method used is a comparative experimental approach combining quantitative and qualitative methods. The data consist of three rendered 2D animation images from Blender that were converted into 5-second videos using identical prompts on each platform. Quantitative evaluation was conducted through measurements of processing time, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Meanwhile, qualitative evaluation involved panelists using a Likert scale to assess nine visual aspects. The results indicate that Pika and Runway excelled in processing time efficiency, with average times of 34.4 seconds and 36.3 seconds, respectively. Kling achieved the highest PSNR and SSIM values, with an average PSNR of 14.62 dB and an SSIM of 0.41, indicating the best technical quality. On the other hand, Runway received the highest ratings in terms of visual and aesthetic aspects based on respondent evaluations. Overall, no single platform outperformed the others across all aspects of the study. Therefore, the selection of a platform should be adjusted according to user needs, whether in terms of efficiency, technical quality, or visual aesthetics. This study highlights the importance of an integrated evaluation approach to produce a more comprehensive assessment of video quality.
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

Pengelolaan inventori gudang perkebunan kelapa sawit membutuhkan sistem yang cepat, akurat, dan terintegrasi. Pencatatan manual sering menimbulkan ketidaksesuaian stok, keterlambatan pembaruan data, dan lemahnya monitoring persediaan. Penelitian ini bertujuan mengembangkan Web-Based Inventory Information System untuk mendukung monitoring stok secara real-time. Kebaruan penelitian terletak pada pembaruan stok otomatis berbasis transaksi barang masuk dan keluar yang terintegrasi dengan pelaporan terpusat. Sistem dikembangkan menggunakan metode Waterfall, perancangan UML, implementasi berbasis web, dan pengujian Black Box Testing. Fitur utama meliputi pengelolaan data barang, transaksi inventori, pembaruan stok otomatis, serta laporan inventori. Hasil pengujian terhadap 25 skenario menunjukkan seluruh fungsi berjalan sesuai kebutuhan dengan tingkat keberhasilan 100%. Sistem ini mampu meningkatkan efisiensi pengelolaan inventori, mempercepat monitoring stok, mengurangi kesalahan pencatatan, dan mendukung pengambilan keputusan berbasis data.
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.

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