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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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Rancang Bangun Aplikasi E-CRM di Toko UD. Maju Jaya Sari, Dira Purnama; Helmiah, Fauriatun; Syafnur, Afdhal
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2278

Abstract

Toko UD.Maju Jaya is a building shop that sells all kinds of building materials. Sales at UD Store. Maju Jaya is still experiencing problems, ranging from unstable sales to still using a manual system for recording and searching for products by customers is also inefficient because customers must come directly to the store. The purpose of this study is to create a web-based E-CRM system to make it easier for customers to place orders for building materials and increase operational efficiency. The research method used is the research and development method which consists of 6 stages starting from problem identification, data collection, analysis, system design, system testing to system implementation. The results of this study resulted in a web-based E-CRM system with features such as product details, shopping cart, online payment, chat so that it can maintain the store's relationship with customers and can also increase customer loyalty.
Sistem Pakar E-Rapor untuk Prediksi Minat Bakat dan Roadmap Pendidikan Siswa dalam Pemilihan Sekolah Nurohmah, Siti; Wibisono, Iwan Setiawan
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2280

Abstract

Choosing a school and university that does not match students' interests and talents often leads to regret later on. According to Irene Guntur, an Educational Psychologist from Integrity Development Flexibility (IDF), 87% of students in Indonesia feel they are in the wrong major. In addition, the Minister of Education, Culture, Research, and Technology (Mendikbudristek) Nadiem Makarim stated that 80% of students in Indonesia do not work in accordance with the major they took. This is due to students' lack of understanding of their interests and talents, as well as the influence of friends, family, or people closest to them in the decision-making process. This study aims to develop an integrated e-report system that is able to identify students' interests and talents based on academic data from elementary, junior high, to high school levels. This system provides recommendations for relevant schools and universities, and functions as a promotional platform for educational institutions through profile information, vision, mission, and blogs. The development of the system follows the Waterfall method which consists of the stages of needs analysis, system design, implementation, testing, and maintenance. . Student academic data in grades 6, 9, and 12 is the basis for system analysis. The results of the study show that the system is able to increase the accuracy of recommendations by up to 95%, while providing an effective promotional medium for schools and universities. This system is expected to help students make better educational decisions, minimize external influences, and encourage educational institutions to improve their competitiveness and service quality. These findings contribute to the development of innovative, effective and predictive technology-based educational information systems.
Pengembangan Sistem Informasi Manajemen Biopori Berbasis Web Menggunakan Metodologi RAD untuk Meningkatkan Efisiensi Pengelolaan Sampah Organik Putra, Made Wahyu Purnama; Paramitha, A.A. Istri Ita; Putri, I Gst. Agung Pramesti Dwi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2282

Abstract

This research develops a Website-Based Biopori Management Information System as an innovative solution to overcome organic waste management problems in Peliatan Village, Bali. The village faced challenges in monitoring the One Million Biopores program due to the absence of an effective monitoring system, low community participation, and the absence of a centralized recording mechanism. Using Rapid Application Development (RAD) methodology, the system was developed through four stages: requirements planning, user design, construction, and implementation. The system has key features such as biopori data recording, harvest schedule notification, educational blog, and waste bank management. Testing using the User Experience Questionnaire (UEQ) with 34 respondents showed positive results on the aspects of attractiveness, efficiency, and accuracy of the system. Although there were variations in the assessment of clarity and novelty, overall the system succeeded in providing a satisfactory user experience. The system is expected to increase the effectiveness of biopore management in Peliatan Village and become a model for the development of similar systems in other areas.
Analisis Sentimen Ulasan Aplikasi Pembelajaran Bahasa Menggunakan Metode VADER Leonardi, Veronica Hertensia; Ibrahim, Ali; Kurnia, Rizka Dhini; Afrina, Mira
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2285

Abstract

Perkembangan teknologi saat ini mempermudah proses belajar bahasa melalui aplikasi seperti Duolingo. Penelitian ini bertujuan untuk memahami persepsi pengguna terhadap Duolingo dengan menggunakan analisis sentimen berbasis VADER (Valence Aware Dictionary and Sentiment Reasoner). Ulasan pengguna dari Google Play Store diproses menggunakan Google Collaboratory, menghasilkan 1.831 data yang dikelompokkan sebagai netral, negatif, dan positif. Hasil analisis menunjukkan akurasi keseluruhan sebesar 98 persen. Model ini efektif dalam mengidentifikasi sentimen netral (presisi 100 persen, recall 97 persen, F1-score 99 persen) dan positif (presisi 99 persen, recall 82 persen, F1-score 99 persen). Namun, model kurang efektif dalam mendeteksi emosi negatif, dengan F1-score 74 persen, recall 82 persen, dan presisi 67 persen, yang menunjukkan adanya kesalahan klasifikasi pada beberapa emosi negatif. Awan kata menunjukkan kata-kata positif seperti "good," "helpful", dan "fun," serta kata-kata negatif seperti "technical problems" dan "learning limitations." Tantangan dalam penggunaan VADER termasuk ketidakmampuan menangani konteks bahasa yang kompleks dan nuansa emosional yang mendalam. Untuk meningkatkan klasifikasi sentimen, penelitian ini merekomendasikan penggunaan VADER bersama Deep-Translator. Kombinasi ini dapat membantu mengidentifikasi sentimen negatif dengan lebih baik dan menangani data dengan berbagai bahasa secara lebih efisien. Tujuan penelitian ini adalah untuk memahami sudut pandang pengguna dan meningkatkan akurasi analisis sentimen, sehingga berkontribusi pada pengembangan aplikasi pembelajaran bahasa yang lebih baik.
Implementasi Aplikasi Smart Farm Berbasis Android Menggunakan Metode Waterfall Muzakki, Achmad; Amri, Arni Muarifah; Alhari, Muhammad Ilham; Sadam, Firlana
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2286

Abstract

This study aims to develop an Android-based Smart Farm application. The main objective of this application is to improve operational efficiency and data recording accuracy. This application has livestock recording features, feeding schedules, and production report generation. With these features, it is expected that the application can help farm owners and officers in monitoring and managing farm activities efficiently and systematically. The methodology used in the development of this application is the Waterfall model, to provide more structured development and in accordance with the needs of farmers. Data for needs analysis were collected through field observations of farm owners and farm officers. The results show that this application facilitates monitoring of livestock conditions and feed management in real time. Based on these findings, this study recommends the addition of an automatic notification feature to improve farmers' responsiveness to farm needs.
Model Prediksi Stunting Anak di Indonesia Menggunakan Extreme Gradient Boosting Aziz, Halim Al; Santoso, Heru Agus
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2289

Abstract

Stunting is a significant nutritional problem that negatively impacts children’s physical and cognitive development, especially in poor countries like Indonesia. This study used the XGBoost algorithm to examine stunting data in children under the age of five. The analysis results showed that XGBoost processed complex datasets quickly and produced accurate predictions, achieving a model performance evaluation with 86 percent accuracy, 89 percent precision, 95 percent recall, and 92 percent F1 score. This approach effectively found significant trends for early stunting identification through the utilization of body mass index (BMI) and other anthropometric data, which conventional methods failed to reveal. This study also presents opportunities for advancement in the Internet of Things (IoT) framework to improve the efficacy of real-time stunting detection systems. IoT devices provide more precise and reliable anthropometric data collection, thereby improving the efficacy of the XGBoost model in estimating stunting risk. Although IoT applications were not the primary focus of this study, its findings provide substantial contributions to the advancement of data science and technology in the healthcare sector, particularly in initiatives aimed at preventing stunting. This research offers theoretical contributions to the development of data science and health technology, as well as practical benefits in the form of data-based solutions that can be integrated into national programs to reduce the prevalence of stunting, to support more targeted nutritional interventions and improve the quality of life of children in Indonesia.
Transformasi Perawatan Kesehatan Ibu Hamil dengan IoT: Solusi Cerdas untuk Pemantauan Real-Time di Daerah Terpencil Oktarina, Eka Sari; Alamsyah, Gempar; Nurhalissa, Rahmalia; Satria, Rahul Fahmi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2290

Abstract

The high maternal and infant mortality rates in developing countries remain a major challenge in public health. Contributing factors include poverty, limited healthcare infrastructure, and cultural norms that restrict access to medical services. One of the main obstacles is the lack of continuous and real-time monitoring of pregnant women's health, which leads to delays in detecting pregnancy complications. Although various efforts have been made, there is still a gap in the use of technology capable of practically and accurately monitoring maternal health conditions in the field. This study aims to develop an Internet of Things (IoT) device to measure maternal health parameters such as height, weight, blood pressure, and fetal heart rate, with real-time data transmission to the cloud using Firebase. Testing was conducted on 15 pregnant women in Malang Regency, comparing the IoT device's measurements with standard measuring instruments. The results showed high accuracy, with an average error of 0.45% for height and 0.29% for weight. Systolic blood pressure measurements showed greater error variation (7.71%–21.45%), while diastolic pressure was more stable (1.81%–8.95%). Data transmission to Firebase showed an average delay between 1.75 and 2.69 seconds without data loss, indicating that the communication system operated optimally and maintained information integrity. This IoT device has the potential to support real-time monitoring of maternal health, thereby facilitating early medical intervention and contributing to reducing maternal and infant mortality in developing regions.
CRM Terhadap Loyalitas Pelanggan Berbasis Web Putri, Sugini; Putra, Guntur Maha; Apridonal M, Yori
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2297

Abstract

E-commerce is the sale or purchase of services or goods between companies, households, individuals, governments, communities, or other private organizations, which are carried out through computers on network media. Customer Relationship Management is a corporate-level strategy that focuses on building and maintaining relationships with customers and in essence the company wants to build stronger bonds with customers that aim to maximize customer satisfaction. This research aims to develop a web-based CRM system to increase customer loyalty at Jaya Plastik store. The research method involves the use of use case diagrams for system design, implementation with Visual Studio Code, and MySQL database. Test results show that the system can increase product information transparency by 80% and retain 90% of active customers.
Sistem Pendukung Keputusan Pemantauan Stok dan Restok Otomatis Berbasis Web Wibisono, Bagas; Sanjaya, Ucta Pradema
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2301

Abstract

Problems in stock management are often caused by delays in monitoring and the absence of a system that provides accurate restock recommendations. This study designs and implements a web-based decision support system to monitor stock and recommend restocks automatically, using the Laravel Framework. This system applies rule-based logic by considering remaining stock, average daily sales, and delivery time. The calculation process compares available stock with minimum requirements predicted from historical sales patterns. The development methodology follows the Waterfall model. The implementation results show that the system is able to accelerate the monitoring process and provide accurate restock notifications, as well as support managerial decision making in product distribution. This system also succeeded in reducing the number of expired goods to only 2%, which shows an increase in distribution efficiency. An informative dashboard interface also makes it easier to monitor stock conditions in real time. This system makes a real contribution to improving the efficiency and accuracy of inventory management.
Market Basket Analysis untuk Penjualan Retail: Perbandingan Akurasi Algoritma Apriori dan FP-Growth Berbasis CRISP-DM Rahman, Irfan Fadholur; Riana, Dwiza
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2303

Abstract

Increasing the efficiency of sales strategies and product stock management is a major requirement in the retail business, including in the sale of school uniforms. This research aims to identify consumer purchasing patterns through the application of the Market Basket Analysis method using two data mining algorithms, namely Apriori and Frequent Pattern Growth (FP-Growth). The approach used is CRISP-DM, consisting of six main stages, with a dataset of 365 sales transactions and minimum support parameters of 2% and confidence of 60%. The results showed that the Apriori algorithm generated association rules with an accuracy rate of 63.19%, average confidence of 75%, and support of 4.5%, while FP-Growth only achieved an accuracy of 2.92%. This finding shows that in the context of school uniform sales transaction data, Apriori is superior in exploring consumer purchasing patterns. The practical contribution of this research is the recommendation of product bundling and stock optimization strategies based on actual association patterns, which can be applied by educational retail businesses to improve business efficiency and effectiveness.

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