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All Journal Building of Informatics, Technology and Science JIKA (Jurnal Informatika) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmiah Computer Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Ariany, Fenty
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Decision Support System for Tourist Attraction Recommendations Using Reciprocal Rank and Multi-Objective Optimization on the basis of Ratio Analysis Ariany, Fenty; Suryono, Ryan Randy; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i3.4663

Abstract

A tourist attraction is a destination or place visited by tourists to enjoy a variety of attractions, natural beauty, culture, history, or recreation. Attractions can be beaches, mountains, lakes, national parks, historical buildings, museums, amusement parks, and much more. One common problem is confusion in choosing the right attraction, where the information available is incomplete or inaccurate, causing tourists difficulty in making the right decision. Therefore, there needs to be a holistic and integrated approach in choosing tourist attractions, taking into account these aspects so that the tourist experience becomes more meaningful and meaningful for all parties involved. The research objective of the Attraction Recommendation Decision Support System Using Reciprocal Rank and MOORA is to develop a system that can provide optimal attraction recommendations to users based on their preferences against diverse criteria, such as distance, cost, travel time, and cleanliness level. By using the Reciprocal Rank approach to take into account the user's subjective preferences towards each criterion. Meanwhile, by applying MOORA, this study aims to optimize the relative performance of alternative attractions based on the relationship between criteria. Thus, this research is to provide useful tools for users to make better and more informed decisions. The ranking results provide recommendations for alternative krui beach with a final value of 0.3752 to rank 1, alternative tanjung setia beach with a final value of 0.3558 to rank 2, alternative klara beach with a final value of 0.3512 to rank 3
PERBANDINGAN KINERJA DEEP LEARNING LENET DAN ALEXNET DENGAN AUGMENTASI DATA PADA IDENTIFIKASI ANGGREK tarisa, ekanofi; Ariany, Fenty
Jurnal Informatika Vol 8, No 1 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i1.9923

Abstract

Anggrek adalah tanaman florikultura yang sangat digemari oleh masyarakat karena menarik perhatian dari segi bentuk dan warna bunga yang unik serta masa berbunganya yang relatif panjang. Meskipun banyak diminati keanekaragaman anggrek masih sangat sulit untuk diidentifikasi hanya berdasarkan bentuk dan warna. Deep learning berfokus pada penggunaan arsitektur jaringan syaraf tiruan yang mempunyai kemampuan dalam pengenalan citra. Sehingga deep learning dipilih sebagai metode utama untuk mengatasi permasalahan identifikasi citra anggrek. Penerapan metode deep learning pada penelitian ini untuk membandingkan hasil akurasi kinerja arsitektur LeNet dan AlexNet pada identifikasi citra anggrek dan menggunakan skenario pengujian K-Fold Cross Validation. Dataset anggrek memiliki 1000 gambar, lalu dataset di augmentasi menjadi 2000 gambar. Google Colab digunakan sebagai alat untuk melakukan proses pelatihan model deep learning. Hasil penelitian menunjukkan AlexNet menggunakan augmentasi rotate memiliki nilai akurasi 79.50% dan LeNet memiliki nilai akurasi 62,50%. Sehingga dapat disimpulkan bahwa identifikasi spesies anggrek dengan menggunakan arsitektur AlexNet lebih akurat dibandingkan dengan arsitektur LeNet.
Evaluasi Kinerja Divisi Logistik Berbasis Sistem Pendukung Keputusan dengan Pendekatan OWH-TOPSIS Ariany, Fenty
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.49

Abstract

The performance of the Logistics Division is one of the important indicators in ensuring the smooth flow of goods, information, and resources in an organization. The optimal performance of the logistics division can be seen from the ability to meet delivery time targets, operational cost efficiency, order fulfillment accuracy, and adaptability to changes in market demand. One of the main problems is that the assessment criteria are not clear or relevant, so the assessment results do not reflect the actual abilities and contributions of employees. In addition, there is a lack of measurable quantitative data to identify operational performance. The solution to this problem involves the application of structured, objective, and data-driven evaluation methods, as well as the development of systems that support transparency in the assessment process. This study aims to evaluate the performance of the Logistics Division objectively and comprehensively using the decision support system approach based on OWH-TOPSIS, so as to provide a transparent, accurate, and relevant performance evaluation system to support strategic decision-making related to improving the performance of the Logistics Division. The results of the ranking of the performance evaluation of the logistics division, Team D showed the best performance with the highest score, which was 0.882. In second place, Team A has a score of 0.8341, followed by Team B with a score of 0.8255. Meanwhile, Team C occupies the last position with the lowest score of 0.6831. This difference in scores indicates that there is a variation in performance between teams, with Team D significantly superior to other teams.
Sistem Pendukung Keputusan Pemilihan Objek Wisata di Lampung Selatan Menggunakan Metode Analitical Hierarchy Process Khusairi, Ahmad Andi; Ariany, Fenty
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.844

Abstract

Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) yang membantu wisatawan memilih objek wisata terbaik di Kabupaten Lampung Selatan menggunakan metode Analytical Hierarchy Process (AHP). Kabupaten ini memiliki beragam destinasi wisata alam, seperti pantai eksotis, pulau-pulau kecil, air terjun, dan pemandian air panas yang berpotensi dikembangkan. Namun, kurangnya informasi komprehensif terkait fasilitas, aksesibilitas, dan biaya sering menjadi kendala bagi wisatawan dalam menentukan destinasi sesuai preferensi mereka. Metode AHP digunakan untuk menganalisis sembilan kriteria utama, yaitu tiket masuk, fasilitas, aksesibilitas, kebersihan, keindahan alam, keamanan, aktivitas wisata, kenyamanan, dan waktu operasional. Hasil penelitian menunjukkan bahwa Grand Elty Krakatoa Resort (A1) memiliki nilai prioritas tertinggi sebesar 36,22%, diikuti oleh Krakatau Park (A2) dengan 22,32%, dan Pantai Marina (A3) sebesar 11,17%. Destinasi lain, seperti Pantai Pasir Putih, Slanik Waterpark, Menara Siger, dan Pemandian Air Panas Way Belerang, menempati posisi berikutnya dengan nilai prioritas yang bervariasi. Sistem ini terbukti efektif dalam memberikan rekomendasi destinasi wisata berdasarkan kriteria yang relevan, membantu wisatawan dalam membuat keputusan yang lebih terinformasi. Selain itu, SPK berbasis AHP ini juga berkontribusi bagi pemerintah daerah dan pengelola wisata dalam meningkatkan kualitas layanan serta daya tarik destinasi wisata. Penelitian ini tidak hanya memperkuat penerapan AHP di sektor pariwisata, tetapi juga memberikan solusi nyata untuk meningkatkan kepuasan wisatawan dan mendukung pengembangan pariwisata berkelanjutan di Kabupaten Lampung Selatan. Dengan adanya sistem ini, ilmu pengetahuan dalam bidang pengambilan keputusan semakin berkembang, sekaligus memberikan manfaat praktis bagi industri pariwisata dalam meningkatkan daya saing destinasi wisata dan menarik lebih banyak wisatawan.
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Wang, Junhai; Darwis, Dedi; Gunawan, Rakhmat Dedi; Ariany, Fenty
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.6

Abstract

The number of users of e-commerce platforms has increased significantly in recent years, and consumers are now more likely to shop online due to ease of access, diverse product choices, and flexibility in transaction times. The difficulty in determining the best e-commerce platform is often caused by subjectivity in the weighting of the criteria used for evaluation. The weighting process is carried out based on the preferences of certain individuals or groups, without considering objective data. This research aims to apply an objective, structured, and accurate approach in evaluating and ranking e-commerce platforms based on relevant multi-dimensional criteria. By using the root assessment method, the evaluation process can be carried out systematically through hierarchical analysis, while the MEREC weighting ensures that the weight of each criterion reflects its real impact on the outcome of the decision. Through the combination of these two methods, this research is expected to make a significant contribution to improving the quality of decision-making, especially in helping users or business people choose the e-commerce platform that best suits their needs. The results of the final score calculation Platform E was ranked first with the highest score of 4.87083, Platform A was ranked second with a score of 4.85162, and Platform B was ranked third with a score of 4.83842. Future research should address the identified limitations by exploring the integration of advanced predictive analytics and artificial intelligence techniques to improve the adaptability and resilience of models. In addition, sensitivity analysis of the MEREC Root Assessment and Weighting Methods should be performed to understand its performance under various data conditions.
Co-Authors Dedi Darwis Gunawan, Rakhmat Dedi Khusairi, Ahmad Andi Ryan Randy Suryono Setiawansyah Setiawansyah tarisa, ekanofi Wang, Junhai