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Sistem Rekomendasi Tempat Wisata di Provinsi Riau dengan Metode Simple Additive Weighting (SAW) Risky Silitonga; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.929

Riau is a province that has quite good potential for new tourist attractions and tourist destinations that can be developed further. Because there are quite a lot of tourist attractions in Riau, it is difficult for the community to choose recommendations for tourist attractions that suit the wishes of the user. Therefore, the researcher created a recommendation system to assist the community in choosing the tourist attractions they want to visit. In developing this recommendation system, the Simple Additive Weighting method is used, where this method ranks alternative tourist destinations according to predetermined criteria values. The criteria offered by the system are distance, cost, facilities, time and accessibility. The system created can help people to choose the right tourist destination. Web-based built system. The recommendation system for tourist attractions in Riau Province with the Simple Additive Weighting method has succeeded in assisting users in selecting tourist attractions according to predetermined criteria values. Then the best recommendation for tourist attractions on behalf of Ulo Kasok tourist attractions is obtained with the final result value ( 88.80), Based on the UAT test, the results obtained were 83%, which means that the user really agreed

Sistem Rekomendasi Hotel Di Provinsi Riau Dengan Metode AHP dan SAW Nurul Ramadiah Madjid; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.931

Riau Province is one of the provinces that has been equipped with various recreational facilities, sports facilities and tours that are very interesting to visit. With the increasing number of facilities and tourist attractions that can be visited, the hotel is one place that is needed as a lodging facility. Hotels in Riau Province are also growing very rapidly. Riau provides so many choices of hotels scattered in various locations with various hotel classes, rental prices, facilities and services. The system is a set of components and combined elements, organized and collaborative components to achieve a certain goal, namely providing information. AHP is a decision support model that will describe complex multi-factor or multi-criteria problems into a hierarchy. The SAW method is known as the term weighted sum. The basic concept of the SAW method is to find the weighted sum of the performance ratings for each alternative on all attributes. The results obtained from this system are systems that are made into the good category by applying the AHP and SAW methods. The Hotel Recommendation System in Riau Province with the AHP and SAW Methods can help the Tourism Office and the community in selecting hotels. The Hotel Recommendation System using the AHP and SAW methods has been able to produce hotel recommendations in Riau Province according to needs and based on criteria determined by the user. Based on the Black Box testing, it shows that the system can run well with its functions, and with the UAT testing that has been carried out, a result of 82% is obtained where the results fall into the strongly agree category

Sistem Pendukung Keputusan untuk Rekomendasi Pemilihan Guru Terbaik Menggunakan Metode Simple Additive Weighting Janaria Sapitri; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
Indonesian Journal of Innovation Multidisipliner Research Vol. 2 No. 2 (2024): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v2i2.139

Guru yang profesional dibutuhkan di sekolah-sekolah, seperti SMKN Kehutanan Pekanbaru, untuk memberikan pengalaman belajar mengajar yang unggul. Oleh karena itu, sekolah terus berupaya untuk meningkatkan kualitas guru dengan menilai bagaimana para pengajar menjalankan tugasnya untuk memastikan mereka memenuhi kriteria kompetensi. Sistem pendukung keputusan adalah sistem yang dapat memecahkan masalah dan menanganinya tujuannya bukan untuk menggantikan pengambil dilanjutkan dengan prosedur perangkingan untuk menemukan alternatif terbaik dari daftar pilihan keputusan, melainkan untuk membantu merekomendasikan pengambil keputusan. Simple Additive Weighting (SAW) adalah metode yang populer dalam sistem pendukung keputusan karena dapat menetapkan nilai pembobotan untuk setiap fitur dan kemudian yang tersedia. Pada contoh kasus ini, metode SAW (simple additive weighting) yang digunakan untuk memilih guru terbaik di SMKN Kehutanan Pekanbaru berhasil membantu pengguna, dan diperoleh rekomendasi guru terbaik yaitu A12 dengan nilai akhir 0,95. Berdasarkan pengujian UAT, diperoleh hasil 85% yang menandakan bahwa aplikasi ini dapat diterima dengan baik oleh pengguna.

Sistem Pendukung Keputusan untuk Rekomendasi Pemilihan Guru Terbaik Menggunakan Metode Simple Additive Weighting Janaria Sapitri; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
Indonesian Journal of Innovation Multidisipliner Research Vol. 2 No. 2 (2024): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v2i2.139

Guru yang profesional dibutuhkan di sekolah-sekolah, seperti SMKN Kehutanan Pekanbaru, untuk memberikan pengalaman belajar mengajar yang unggul. Oleh karena itu, sekolah terus berupaya untuk meningkatkan kualitas guru dengan menilai bagaimana para pengajar menjalankan tugasnya untuk memastikan mereka memenuhi kriteria kompetensi. Sistem pendukung keputusan adalah sistem yang dapat memecahkan masalah dan menanganinya tujuannya bukan untuk menggantikan pengambil dilanjutkan dengan prosedur perangkingan untuk menemukan alternatif terbaik dari daftar pilihan keputusan, melainkan untuk membantu merekomendasikan pengambil keputusan. Simple Additive Weighting (SAW) adalah metode yang populer dalam sistem pendukung keputusan karena dapat menetapkan nilai pembobotan untuk setiap fitur dan kemudian yang tersedia. Pada contoh kasus ini, metode SAW (simple additive weighting) yang digunakan untuk memilih guru terbaik di SMKN Kehutanan Pekanbaru berhasil membantu pengguna, dan diperoleh rekomendasi guru terbaik yaitu A12 dengan nilai akhir 0,95. Berdasarkan pengujian UAT, diperoleh hasil 85% yang menandakan bahwa aplikasi ini dapat diterima dengan baik oleh pengguna.

Sistem Pendukung Keputusan Pemilihan Sepeda Motor Listrik Menggunakan Fuzzy AHP-TOPSIS dengan Pendekatan User-Driven Muhammad Habib; Yelfi Vitriani; Reski Mai Candra; Surya Agustian; Iwan Iskandar
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10260

The growth of electric motorcycles in Indonesia, which is projected to reach more than 196,000 units by mid-2025, has created complexity in consumers’ purchasing decision-making processes due to the wide variety of technical specifications across brands. This study designs and develops a user-driven Android-based Decision Support System by integrating the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate personalized recommendations for selecting electric motorcycles. The system evaluates 23 alternatives from seven brands with official dealers in Pekanbaru based on seven technical criteria: price, range, charging time, maximum speed, motor power, load capacity, and battery capacity. Criterion weights are determined dynamically through a 1–5 scale slider interface mapped to Triangular Fuzzy Numbers (TFN) and processed using Chang’s Extent Analysis Method (1996), while ranking is performed using TOPSIS. This study applies the Fuzzy AHP method to address the ambiguity in users’ subjective assessments, which are often not well accommodated by single crisp values in conventional AHP. The main contribution of this study lies in the simplification of the weight elicitation mechanism, which reduces 21 conventional pairwise comparisons to just seven direct slider inputs mapped into TFN form. Furthermore, this study successfully implemented this user-driven, slider-based mechanism into an Android-based decision support system (DSS) for selecting electric motorcycles that is directly accessible to end consumers. For system testing, all scenarios in the Black Box Testing (33 scenarios) were successfully executed without errors. Furthermore, an evaluation via User Acceptance Testing (UAT) using the USE Questionnaire framework on 10 respondents yielded an acceptability score of 83.2%, which falls into the “Highly Acceptable” category. Based on the Performance preference profile, the United RX6000 was determined to be the best alternative with a Closeness Coefficient value of 0.9377.

Analisis Perbandingan Metode DBSCAN dan Meanshift dalam Klasterisasi Data Gempa Bumi di Indonesia MHD Ade Setiawan; Fitri Insani; Yelfi Vitriani; Yusra Yusra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.605

Indonesia is one of the countries with a high vulnerability to earthquakes due to its location at the convergence of three major tectonic plates: the Indo-Australian, Eurasian, and Pacific plates. As a result of this interaction, seismic activity is highly frequent across various regions. Understanding the distribution patterns of earthquakes is essential for disaster risk mitigation. One approach used to analyze these patterns is clustering, particularly using the DBSCAN and Meanshift algorithms, which can group spatial data without predefining the number of clusters. This study aims to compare the effectiveness of both algorithms in clustering earthquake data based on spatial parameters, namely latitude and longitude. Evaluation was conducted using cluster visualization and the Silhouette Score as the clustering validity metric. The results show that DBSCAN produces more optimal clustering with a Silhouette Score of 0.930028, higher than Meanshift's score of 0.90103. DBSCAN is also capable of detecting relevant outliers in earthquake analysis, while Meanshift generates more clusters but with less separation. Using spatial parameters such as latitude and longitude, DBSCAN is considered more effective in identifying the spatial distribution patterns of seismic activity in Indonesia based on earthquake data. This research supports the development of decision support systems for earthquake disaster mitigation and serves as a reference for selecting appropriate clustering methods for spatial data analysis.

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