Article Per Year (5 Year)
p-Index From 2021 - 2026
1.937
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Prosiding SNATIF International Journal of Artificial Intelligence Research AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT Rang Teknik Journal INTEK: Jurnal Penelitian Value: Jurnal Manajemen dan Akuntansi PERSPEKTIF Jurnal Teknologi Elekterika International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Nusantara Hasana Journal Jurnal Litigasi Amsir Prosiding Seminar Riset Mahasiswa
Asriyadi Asriyadi
Fakultas Teknik, Universitas Andalas
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Nursing Social Sciences

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
PENERAPAN METODE K-NEAREST NEIGHBOAR (KNN) DALAM KLASIFIKASI KELULUSAN SISWA Samsuriah, Samsuriah; Asriyadi, Asriyadi
Nusantara Hasana Journal Vol. 5 No. 8 (2026): Nusantara Hasana Journal, January 2026
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i8.1898

Abstract

Student graduation determination is an important aspect of the educational process that requires objective and accurate decision-making. Along with the development of information technology, the utilization of academic student data can be conducted through data mining approaches to support decision-making processes. This study aims to apply the K-Nearest Neighbor (KNN) method to classify student graduation status based on attendance data and final scores. The dataset consists of five student records as training data and two student records as testing data. The research stages include data preprocessing, distance calculation using Euclidean Distance, and class determination based on the majority of the nearest neighbors with a K value of 3. The results show that student F is classified as graduating because most of its nearest neighbors belong to the graduating class, while student G is classified as not graduating due to a greater number of nearest neighbors from the non-graduating class. Therefore, it can be concluded that the K-Nearest Neighbor (KNN) method is able to provide fairly accurate student graduation classification results and can be used as a decision support tool in the education sector.
Co-Authors AA Sudharmawan, AA Ade Silvia Handayani Afifah, Luthfia Agung, Muhammad Zakuan Ahamad Rizal Sultan Ahmad Taqwa Ahyar, Muh. Akhriana, Asmah Alwali, Muhammad Ambarwati, Nur Masyita Ayu Andi Wawan Indrawan Aryanti Aryanti Arzuna, Pit BASTIAN, ALVIAN Ciksadan, Ciksadan Ciksadan, Ciksadan Dharma Aryani Dzulqarnain, Andi Emilia Hesti Faris, Fakhri Al Fradesa, Fiza Indah Ayu Lestari, Indah Ayu Irma Salamah Kaila, Afifah Syifah Karović, Sadmir Latif, Asriadi Leni Novianti Lindawati Lindawati M. Ardiansyah Mohd Arifullah Muharram, Syair Muhira Dzar Faraby, Muhira Dzar Mukhlisin Mukhlisin Mustika Mustika Nofriyanti, Duwi Nur Hidayah Nur, Emil Fitra Nur, Fajria Nurhayati Nurhayati Nyayu Latifah Husni, Nyayu Latifah Parenreng, Mardawia Mabe Pranoto, Sarwo Rachmat Ramadhan, Rachmat Rahman, M. Arief Rakhman, M Arief Ramadan, Syahrul Mifta RD Kusumanto Rosita, Ella Samsuriah, Samsuriah Saputra, Defri Sawitry, Maryanti Setawati, Elok Suci, Yuni Selvita Suroso Suroso suzan zefi Wahyu Caesarendra Yuniarti Yuniarti