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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 1,026 Documents
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Rancang Bangun Aplikasi Pemetaan Tempat Pemakaman di Kecamatan Karangpawitan Garut Berbasis Web GIS Septiana, Yosep; Imanuloh, Agil Nazhar
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.22-2.2611

Abstract

Pengelolaan data pemakaman di Kecamatan Karangpawitan masih dilakukan secara manual, sehingga menyulitkan proses pencatatan, pemantauan, dan penyampaian informasi kepada masyarakat. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi geografis berbasis web (Web GIS) guna memetakan lokasi serta ketersediaan tempat pemakaman secara digital. Metode yang digunakan dalam pengembangan sistem adalah Rational Unified Process (RUP) yang terdiri dari tahapan inception, elaboration, construction, dan transition. Sistem ini dirancang dan dibangun menggunakan framework CodeIgniter, database MySQL, dan library Leaflet untuk menampilkan peta interaktif. Aplikasi dibagi ke dalam tiga hak akses yaitu admin, pengelola, dan masyarakat. Hasil pengujian black box menunjukkan semua fungsi berjalan dengan baik, sedangkan hasil uji beta terhadap 32 responden menghasilkan tingkat kepuasan sebesar 83,54%. Aplikasi ini diharapkan dapat membantu pemerintah kecamatan dalam mengelola data pemakaman secara efisien dan transparan.
Prediksi Pendapatan Film Menggunakan Gradient Boosting Rahmah, Revina Nur; Sabrina, Puspita Nurul; Ramadhan, Edvin
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.22-2.2613

Abstract

Industri film memiliki tingkat persaingan dan risiko tinggi, sehingga kemampuan memprediksi pendapatan sebelum rilis menjadi krusial bagi produser, distributor, dan investor. Penelitian ini mengembangkan model prediksi pendapatan film menggunakan algoritma Gradient Boosting dan metode diskretisasi Equal Frequency Binning (EFB) pada atribut Earnings. Dataset mencakup film dari tahun 1930–2016 dengan berbagai fitur seperti genre, anggaran, box office, aktor, dan sutradara. Proses meliputi pre-processing data, diskretisasi Earnings menjadi tiga kelas (Low, Medium, High), pembagian data dengan Holdout Method (80% latih, 20% uji), serta pelatihan dan evaluasi model. Hasil menunjukkan akurasi 96.51% dengan precision, recall, dan F1-score tinggi di semua kelas, berkat efektivitas EFB dalam menyeimbangkan distribusi dan keunggulan Gradient Boosting dalam menangkap interaksi fitur. Model ini terbukti akurat dan dapat dijadikan referensi dalam pengambilan keputusan investasi pra-produksi. Penelitian lanjutan disarankan untuk memperluas cakupan data dan mempertimbangkan fitur tambahan seperti sentimen media sosial dan strategi promosi guna meningkatkan generalisasi model.
Pengembangan Virtual Tour 360 Derajat Sebagai Media Informasi Wisata Curug Sanghyang Taraje Berbasis Web Algifari, M.; Deddy Supriatna, Asep
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.22-2.2616

Abstract

Curug Sanghyang Taraje is a natural tourist attraction in Garut Regency that holds significant aesthetic potential but remains relatively unknown due to limited access to engaging promotional media. This study aims to develop an information platform based on interactive 360-degree virtual tour technology, accessible via a website, to provide users with an engaging and informative visual exploration experience. The application development follows the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, material collection, production, testing, and distribution. The 3DVista software is used to construct the virtual tour, while integration into the website is carried out using Visual Studio Code with HTML, CSS, and JavaScript. Key features of the application include 360-degree panoramic views, informational text, narrated audio, interactive navigation via hotspots, background music, and a digital map. Testing is conducted using a black-box approach with decision table techniques to ensure all functions meet user requirements. The result of this research is an interactive virtual tour application that comprehensively presents tourism information and serves as a digital promotional tool for tourism managers. The application facilitates tourists in exploring the destination remotely and demonstrates that virtual tour technology can be an innovative solution to support digital tourism promotion.
Clustering Minat Pembeli Produk Furniture Menggunakan Algoritma K-Means untuk Strategi Penjualan Indriyani; Handayani, Masitah; Sumantri
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.22-2.2625

Abstract

Minat beli konsumen merupakan faktor penting dalam menentukan keberhasilan penjualan produk, termasuk industri furniture. Toko Deyumi Jati Sei Piring menghadapi permasalahan dalam memprediksi minat pelanggan terhadap berbagai jenis furniture yang dijual. Penelitian ini bertujuan mengelompokkan minat beli pelanggan berdasarkan data penjualan menggunakan algoritma K-Means Clustering. Data penelitian mencakup penjualan lima jenis furniture utama selama 12 bulan terakhir. Proses klasterisasi dilakukan untuk membagi produk ke dalam kategori berdasarkan pola penjualan. Hasil penelitian menunjukkan bahwa lemari, tempat tidur, dan kursi termasuk dalam kategori produk dengan minat beli tinggi, sedangkan meja, rak TV, dan beberapa produk lainnya tergolong pada kategori dengan minat rendah. Informasi ini dapat membantu perusahaan menentukan strategi produksi, pengelolaan stok, serta penempatan barang secara lebih efisien. Selain itu, penelitian ini menghasilkan sistem berbasis web yang terintegrasi dengan metode K-Means, sehingga pemilik usaha dapat secara real-time memantau kategori produk dan menyesuaikan strategi penjualan. Kontribusi utama penelitian ini adalah menyediakan sistem pendukung keputusan berbasis data mining yang membantu perusahaan meminimalkan risiko kelebihan atau kekurangan stok. Dengan demikian, penerapan algoritma K-Means terbukti efektif dalam mengidentifikasi pola minat beli konsumen dan mendukung perencanaan bisnis yang lebih baik.
Analisis Keamanan Sistem Kepegawaian dan Pengembangan Sumber Daya Manusia di Sektor Pemerintahan Dengan Metode OWASP Nur Hariyanto, Mohammad Malik; 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.22-2.2631

Abstract

Information system security plays an important role in supporting public services, particularly in the management of civil servant data and human resources. This research aims to evaluate the security of the personnel information system in the government sector in Pati Regency. This research approach uses OWASP ZAP. The findings show that the system still has a high potential for exploitation through various types of cyber attacks, such as XSS, clickjacking, CSRF, and data theft. This condition makes it very important to conduct regular IT security audits to maintain the reliability and integrity of government information systems.
Rancang Bangun Aplikasi E-learning Berbasis Web Untuk SMK Menggunakan Metode Extreme Programming Tresnawati, Dewi; Ardiansyah, Ryan
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.22-2.2647

Abstract

The development of information and communication technology has brought significant changes in the field of education, where learning is no longer confined to conventional classrooms. Global trends indicate that e-learning has become one of the main solutions to address the challenges of modern education, particularly with the growing demand for flexible, interactive, and accessible distance learning for both students and teachers. In Indonesia, the implementation of e-learning is increasingly encouraged by government policies aimed at school digitalization. However, in practice, many educational institutions still face obstacles such as limited infrastructure, lack of system integration, and low utilization of technology in teaching and learning activities. Based on these conditions, this research focuses on the development of a web-based e-learning system specifically designed for SMK Setia Bhakti Garut. The application was developed using the Extreme Programming methodology to ensure flexibility and efficiency throughout the development process. The system includes key features such as material management, assignments, online examinations, interactive learning videos through the WordPress H5P plugin, student group management, and activity notifications. The development process began with user requirements analysis using a user story approach, followed by system design, program implementation, and testing to ensure functionality. The results of black-box testing indicate that all functional components of the system operate as expected according to the defined functional requirements. Moreover, the integration of H5P content within WordPress provides teachers with the flexibility to design interactive multimedia-based learning materials.
Penerapan E-CRM Dalam Strategi Pemasaran Online Barang Retail Rumah Tangga Manurung, Silpany; Sembiring, Muhammad Ardiansyah; Azmi, Sri Rezki Maulina
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.22-2.2651

Abstract

This study explores the implementation of Electronic Customer Relationship Management (E-CRM) as a strategic approach to enhance the effectiveness of online marketing in the household goods retail business, Adele Grosir Kisaran, located in Asahan Regency, North Sumatra. The primary challenges identified include the absence of digital promotional tools, the lack of a computerized transaction system, and limited customer communication. To address these issues, an integrated web-based E-CRM system was developed using the PHP CodeIgniter framework and MariaDB database. Key features include customer registration, product catalog, digital ordering and payment, feedback system, real-time chat, and automated notifications for promotions and loyalty programs. A qualitative approach was employed, involving observation, interviews, and literature review, while system evaluation was conducted through black-box testing to ensure functional compliance. Customer data analysis revealed that the E-CRM implementation not only improved transaction efficiency and service communication but also generated behavioral patterns that could be explored through association rules. For instance, an association rule with 35% support and 78% confidence indicated that customers who purchased kitchenware also tended to buy household cleaning supplies. This pattern formed the basis for targeted promotional strategies, which proved to increase customer response rates by 25% compared to previous promotional methods. This research contributes to the digital transformation literature of MSMEs by demonstrating how the integration of E-CRM and customer data analysis can strengthen data-driven decision-making. The study offers a replicable system model for other MSMEs to enhance competitiveness, reinforce customer relationships, and achieve business sustainability in a highly competitive digital era.
Analisis Komparatif Model Pembelajaran Mesin untuk Klasifikasi Biner pada Data HVAC Gusti Ngurah Putra Arimbawa, I; Ayu Juli Astari, Ni Made; Crisnapati, Padma Nyoman; Devi Novayanti, Putu; Duika Adi Sucipta, I Kadek; Panji Anggara, Dicky; Yuda Danuarta, I Putu
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.22-2.2657

Abstract

The HVAC (Heating, Ventilation, and Air Conditioning) system plays a crucial role in maintaining thermal comfort and energy efficiency in commercial and industrial buildings. However, early detection of anomalies or failures in this system is often suboptimal, leading to increased energy consumption, reduced operational performance, and high maintenance costs. This study aims to develop and evaluate various machine learning models for real-time anomaly detection in HVAC systems, using a real-world dataset that includes 11 key operational variables. Several algorithms are used, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The dataset is labeled based on dynamic deviations between actual temperature and setpoint using the Exponential Moving Average (EMA) approach, which allows for adaptive anomaly labeling. The experimental results show that the XGBClassifier achieves an accuracy of 99.32%, with precision and recall of 0.98 each, and an F1-score of 0.98, making it the best model for detecting anomalies in a balanced manner. Logistic Regression (accuracy 99.54%, F1-score 0.99) and Random Forest (accuracy 98.70%, F1-score 0.96) also proved to be reliable and computationally efficient alternatives. Thus, this research not only provides a comprehensive comparison of models but also emphasizes the novelty of the adaptive labeling strategy to support real-time anomaly detection in HVAC systems, which can enhance energy efficiency while reducing maintenance costs.
Deteksi Emosi Teks X Berbahasa Indonesia Menggunakan Bi-LSTM dengan Seleksi Fitur Chi-Square Wangsajaya, Yosia Heartha Dhalasta; Setyowati, Erlin; Wibowo, Arief
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.22-2.2658

Abstract

Emotions are an important indicator in understanding public responses on social media, particularly X, which is the main medium for public expression. This study aims to develop a classification model for the emotions of Indonesian-speaking X users using a Bidirectional Long Short-Term Memory (Bi-LSTM) approach combined with data mining-based feature selection techniques. A dataset of approximately 6,000 tweets was collected through X scraping based on keywords and hashtags representing six main emotions: anger, sadness, fear, happiness, love, and surprise, from January 2024 to March 2025. The obtained data was processed through text normalization, stop word removal, and tokenization stages. Features were extracted using TF-IDF and selected using the Chi-Square method to improve classification performance. Tweets were labeled with emotions manually and semi-automatically. The Bi-LSTM model was trained and tested using accuracy, precision, recall, and F1-score metrics. Initial test results showed an accuracy of 86.3%, with the best performance on the emotions “happy” and “angry.” This study shows that the integration of deep learning and data mining can improve the accuracy of automatic emotion detection in Indonesian text. The main contribution of this study is the integration of Chi-Square feature selection with Bi-LSTM for Indonesian text, which has not been widely explored before.
Analisis Sentimen Opini Publik Menggunakan Algoritma Naive Bayes dan TF-IDF Agustin, Yoga Handoko; Cici Mulyani, Neng; Sindu Prasetya, Wahyu
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.22-2.2671

Abstract

Penelitian ini berfokus pada analisis sentimen masyarakat terhadap kebijakan larangan study tour yang dikeluarkan oleh Gubernur Jawa Barat dengan memanfaatkan data komentar dari media sosial Instagram. Data dikumpulkan melalui teknik web scraping menggunakan ekstensi Instant Data Scraper dengan kata kunci relevan, kemudian diberi label secara otomatis oleh ChatGPT. Untuk menjamin kualitas pelabelan, dilakukan validasi manual terhadap 10% data secara acak, yang menghasilkan tingkat akurasi sebesar 93%. Proses analisis dilakukan menggunakan algoritma Naïve Bayes dengan kerangka kerja SEMMA (Sample, Explore, Modify, Model, Assess). Tantangan distribusi kelas yang tidak seimbang diatasi melalui penerapan SMOTE (Synthetic Minority Over-sampling Technique). Evaluasi model dilakukan menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan akurasi model sebesar 80%, dengan F1-score tertinggi pada kategori sentimen positif (82%) dan negatif (81%). Temuan ini membuktikan bahwa kombinasi SEMMA dan algoritma Naïve Bayes efektif untuk memetakan opini publik berbasis data media sosial. Lebih jauh, penelitian ini memberikan kontribusi praktis bagi pemerintah dan pembuat kebijakan, khususnya dalam memonitor persepsi masyarakat secara real-time terhadap kebijakan yang diterapkan. Dengan pendekatan ini, pemerintah dapat lebih cepat mengidentifikasi respons publik, mengantisipasi potensi penolakan, serta menyusun strategi komunikasi yang lebih tepat sasaran. Selain itu, kerangka kerja yang digunakan dapat diadaptasi pada isu kebijakan lainnya, sehingga bermanfaat sebagai model analisis sentimen yang sistematis, terukur, dan mendukung pengambilan keputusan berbasis data.

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