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All Journal Jurnal Ilmiah Ecosystem Jurnal Teknologi Elekterika Journal of Informatics and Computer Engineering Research
Rini Nur, Rini
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Gamifikasi Aplikasi Kuliner Berbasis Web Sebagai Strategi E-Marketing Produk Bahan Pangan Nur, Rini; Utomo, Muhammad Nur Yasir; Fansab, Nur Ayu Farahgta
Jurnal Ilmiah Ecosystem Vol. 23 No. 1 (2023): ECOSYSTEM Vol. 23 No 1, Januari - April Tahun 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35965/eco.v23i1.2132

Abstract

Aplikasi kuliner yang berisi ribuan resep masakan memiliki peluang untuk pengembangan stategi e-marketing. Tidak sedikit penyaji resep masakan secara eksplisit menuliskan bahan yang digunakan dengan merek produk yang hanya menguntungkan perusahaan terkait. Penyebutan ini, secara tidak langsung membuat penyaji resep merekomendasikan produk tersebut kepada para pembaca resep. Akan tetapi, penyaji resep tidak mendapatkan feedback apapun dari perusahaan. Penelitian ini bertujuan untuk mengimplementasikan aplikasi kuliner berbasis web yang di dalamnya menerapkan konsep gamifikasi guna memaksimalkan e-marketing dan sosial bisnis. Jenis penelitian yang digunakan adalah penelitian pengembangan, sedangkan data dianalisis secara deskriptif kuantitatif. Alur gamifikasi dimodelkan dalam Graph Database karena unsur-unsur terkait memiliki keterhubungan data satu sama lain yang sangat kuat. Selain memaksimalkan stategi e-marketing melalui aplikasi kuliner, user yang terlibat di dalamnya pun akan mendapatkan benefit dari penyebutan merek bahan pangan tersebut melalui konsep bisnis sosial. Berdasarkan hasil pengujian menggunakan metode blackbox testing, disimpulkan bahwa aplikasi ini berjalan sesuai fungsi dari setiap fitur berdasarkan kondisi masukan. Hasil uji coba melalui kuesioner dengan total 45 responden dimana kinerja sistem menunjukkan nilai 99,98%, pengaruh gamifikasi dalam sistem untuk user menunjukkan nilai 97.7% dan kepuasan responden terhadap user interface menunjukan  nilai 91.1%. Culinary applications that contain thousands of recipes have opportunities for the development of e-marketing strategies. Not a few recipe presenters explicitly write down the ingredients used with product brands that only benefit the related company. This mention indirectly makes the recipe presenter recommend the product to the recipe readers. However, the recipe presenter did not receive any feedback from the company. This study aims to implement web-based culinary applications in which the concept of gamification is applied to maximize e-marketing and social business. The type of research used is development research, while the data is analyzed descriptively quantitatively. The gamification flow is modeled in the Graph Database because related elements have very strong data connectivity with each other. In addition to maximizing the e-marketing strategy through culinary applications, users who are involved in it will also benefit from mentioning the food brand through the concept of social business. Based on the test results using the blackbox testing method, it is concluded that this application runs according to the function of each feature based on input conditions. The test results through a questionnaire with a total of 45 respondents where the performance of the system shows a value of 99.98%, the effect of gamification in the system for users shows a value of 97.7% and respondents' satisfaction with the user interface shows a value of 91.1%.
Development of an Agricultural Department Application to Predict Small Chili Prices Maemunah, Maemunah; Utomo, Muhammad Nur Yasir; Nur, Rini
Jurnal Teknologi Elekterika Vol. 21 No. 2 (2024)
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v21i2.5104

Abstract

Small chili farmers in Soppeng Regency face challenges in planning production and marketing due to fluctuating prices. To assist various stakeholders, an accurate price prediction system is needed. This research proposes a system solution to predict small chili prices using the Gradient Boosting Algorithm. The Gradient Boosting Algorithm is employed to predict prices based on historical data using tools such as Visual Studio Code, Microsoft Excel, Python, PHP, CSS, and SQL. The dataset used consists of chili price data provided by the Department of Agriculture of Soppeng Regency, covering prices from 2018 to April 2024. The developed prediction system is capable of providing accurate price predictions, aiding farmers, traders, and consumers in production and purchasing planning. This algorithm is highly effective in predicting small chili prices and offers significant benefits to farmers, traders, and consumers in the region. The system was tested using Mean Absolute Error (MAE) and User Acceptance Testing (UAT). The results showed that the prediction error, measured using MAE, was 2730.83. Meanwhile, the User Acceptance Testing yielded a questionnaire score of 86.27%, indicating that the system has excellent performance and is highly suitable for use. It is hoped that this system will provide benefits to the entire community, especially small chili farmers, in production planning.
KINERJA HYBRID MONGODB-ELASTICSEARCH PADA APLIKASI SOCIAL NETWORK ANALYSIS Jaury, Muhammad; Olivya, Meylanie; Nur, Rini
Journal of Informatics and Computer Engineering Research Vol. 1 No. 1 (2024)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v1i1.4916

Abstract

Social media data is growing rapidly in size, variety and complexity. Social media data stores various potential information such as sentiment analysis, trend predictions etc. Potential information can be extracted through Social Network Analysis. SNA has a major challenge which is to process very large datasets in a reasonable time. One of the efforts that can be done is create hybrid system of MongoDB and Elasticsearch using social media datasets from Twitter. The results of this study that the highest response time in insert process starting from 26.2s on 1K data to 19520.45s on 1M data. The replication process with 1K tweet data is 6.25s to 1M tweets is 2817,146s. The select process has under 0.1s and relatively constant due to the Inverted Index on Elasticsearch. Highest CPU performance in process of selecting data from Elasticsearch. Highest RAM performance in the insert process to MongoDB and data replication to Elasticsearch.
Co-Authors Fansab, Nur Ayu Farahgta Jaury, Muhammad Meylanie Olivya Mochammad Imron Awalludin Utomo, Muhammad Nur Yasir Zawiyah Saharuna, Zawiyah