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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Pendidikan Sains Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Pendidikan (Teori dan Praktik) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Ilmiah Flash JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) ILKOM Jurnal Ilmiah SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal ilmiah teknologi informasi Asia
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : edumatic jurnal pendidikan informatika

 1 
Analisis Perbandingan Pearson Correlation dan Cosine Similarity pada Rekomendasi Musik berbasis Collaborative Filtering Yuniardini, Fatma; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27781

Abstract

Advances in digital technology have revolutionized the world of music, making access to various genres and musicians easier and unlimited, but users still have difficulty finding music that suits their tastes. This research aims to analyze and compare the performance of the pearson correlation and cosine similarity methods on personal music recommendations based on Collaborative Filtering, with a focus on Item-Based Filtering, measured using Mean Absolute Error (MAE) and Root Mean Squared Error  (RMSE). The dataset utilized comprises public metal music ratings from Amazon, sourced from Kaggle, totaling 19,065 samples. The k-Nearest Neighbors (KNN) algorithm was employed for recommendation prediction. The research steps included data collection, pre-processing to address missing values, duplicates, normalization, and outlier detection, followed by prediction using the KNN algorithm, and accuracy measurement using MAE and RMSE. Evaluation results indicated that Pearson Correlation produced an MAE of 0.066538 and an RMSE of 0.086698, while cosine similarity yielded an MAE of 0.066559 and an RMSE of 0.086709. These findings suggest that pearson correlation is more effective in capturing linear relationships within the rating data, leading to recommendations that are more relevant and aligned with user preferences. Pearson correlation considers the variability in each user's ratings, resulting in more accurate recommendations that align with individual rating patterns.
Perbandingan Cosine Similarity dan Mean Squared Difference dalam Rekomendasi Buku Fiksi berbasis Item Rosydah, Lucyta Qutsyaning; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27783

Abstract

The need for recommendations is increasingly crucial in the digital era, especially with the abundance of fiction book data from e-book platforms and digital libraries. This study aims to evaluate the effectiveness of item-based collaborative filtering using cosine similarity and Mean Squared Difference (MSD) metrics for book recommendations. The knowledge discovery in databases method was applied, encompassing data selection, pre-processing, transformation, data mining, and evaluation. The dataset includes 100,000 user ratings obtained from Kaggle's "Book Recommendation Dataset." Our findings show that the Mean Absolute Error for MSD is 0.152307, slightly better than cosine similarity at 0.152406. The Root Mean Squared Error for MSD is lower at 0.185551, compared to cosine similarity's 0.185636. However, Cosine Similarity is more efficient in processing time, with 0.50 seconds compared to 0.59 seconds for MSD. Understanding these metrics is crucial, as they reveal differences in accuracy and efficiency in book recommendation. The results indicate that MSD performs better in the accuracy of fiction book recommendations compared to cosine similarity, making it more suitable for applications prioritizing recommendation precision, while Cosine is more efficient for large data processing.
Analisis Metode Collaborative Filtering menggunakan KNN dan SVD++ untuk Rekomendasi Produk E-commerce Tokopedia Hazizah, Chalista Yulia; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27793

Abstract

The rapid development of internet technology has driven increased adoption of e-commerce, yet companies face challenges in enhancing users' shopping experiences. To assist users in finding products that match their preferences, relevant recommendation analysis is crucial. This research compares the effectiveness of K-Nearest Neighbors (KNN) and Singular Value Decomposition Plus Plus (SVD++) algorithms for e-commerce product recommendations using the Tokopedia Product Reviews dataset from Kaggle, which contains 40,893 reviews. The study includes data collection and preprocessing steps such as removing duplicates, replacing missing values with the average, and normalizing ratings. KNN and SVD++ are then applied to predict ratings using cosine similarity and factor matrices. Evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) shows that SVD++ outperforms KNN, achieving a lower MAE of 0.161176 and RMSE of 0.185252, compared to KNN's MAE of 0.163964 and RMSE of 0.197045. This indicates that SVD++ is more effective in delivering accuracy and capturing data complexity. The findings highlight the potential to enhance recommendation effectiveness in e-commerce, improving user satisfaction by efficiently matching products to preferences.
Si Pelabuhanna: Game Edukasi Pengenalan Buah-Buahan Mengandung Vitamin A menggunakan Metode Forward Chaining Kurniawan, Rizky Rizaldi; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27837

Abstract

Providing education in a fun way about fruits that contain vitamin A is important because one of the important benefits of vitamin A is for human vision. The purpose of this study to develop the game Si Pelabuhanna and apply the method of forward chaining to determine the eligibility of players to rise to level 2. Using the Game Development Life Cycle (GDLC) development method with stages used initiation, pre-production, production, testing, and release. Our findings are in the form of Si Pelabuhanna games that have a play menu, material, and information and apply the forward chaining method. The Si Pelabuhanna Game can be used in elementary school children's subjects where the material is about fruits containing vitamin A. Application of forward chaining method by specifying variables to be used to create rules. Rules are used to determine if a player is eligible to advance to level 2. Testing using simulation by testing one by one rule after being applied in the game. Based on the test obtained an accuracy value of 100%. This means that the forward chaining method is successfully applied to determine whether a player is eligible to rise to level 2 in a Si Pelabuhanna game.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed Annas Gading Pertiwi Arif Mudi Priyatno Aya Shofia Mufti Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Darwis, Herdianti Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Fadli Hidayat, M. Noer Falah, Moh Zainul Fitriyah Fitriyah Fitriyah Fitriyah Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Hakkun Elmunsyah Haviluddin Haviluddin Hazizah, Chalista Yulia Heru Wahyu Herwanto Hirashima, Tsukasa I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Habibullah Arief M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nafalski, Andrew Natasya Titania Ramadhanti Nazhiroh Tahta Arsyillah Nurhidayati Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Ramdhani, Muhammad Ilham Rendy Yani Susanto Rhomdani, Rohmad Wahid Rismayanti, Nurul Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Salsabila, Nabila Sari, Heni Vidia Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Farida Utami Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma