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All Journal Jurnal Teknologi dan Manajemen Informatika Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Infotek : Jurnal Informatika dan Teknologi Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat
Ahmad Hudawi As
Universitas Nurul Jadid
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Health Professions Social Sciences Other

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Penentuan Metode Terbaik Dalam Menentukan Jenis Pohon Pisang Menurut Tekstur Daun (Metode K-NN dan SVM) Wali Ja'far Shudiq; Ahmad Hudawi As; M Fadhilur Rahman
Jurnal Teknologi dan Manajemen Informatika Vol 6, No 2 (2020): Desember 2020
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v6i2.5156

Abstract

Di sejumlah masyarakat banyak ditemui berbagai jenis pohon pisang. Tidak hanya buahnya yang rasanya manis, tapi juga pohonnya bisa membantu penghijauan alam. Seringkali masyarakat kecewa saat pohon pisang yang ditanamnya tidak sesuai dengan yang diharapkan saat menanamnya. Hal ini bisa dimaklumi mengingat pohon pisang membutuhkan waktu yang lama untuk tumbuh sebelum berbuah. Maka akan lebih baik jika dapat diketahui sejak awal jenis pohon pisang tersebut berdasarkan komponen pohon yang mudah diamati yaitu tekstur daun. Metode yang digunakan adalah dua metode data mining yang klasifikasi yaitu K-Nearest Neighbor (K-NN) dan Support Vector Machine (SVM), yang akan mencari model terbaik dari kedua metode tersebut, dalam mencari tingkat keakurasian yang paling tinggi. Hasil dari penelitian ini menunjukan kinerja metode K-Nearest Neighbor (K-NN) dengan nilai akurasi mencapai 74,00% lebih baik dari hasil kinerja metode Support Vector Machine (SVM) dengan nilai akurasi mencapai 67,89%. DOI: https://doi.org/10.26905/jtmi.v6i2.5156
Pemanfaatan E-Commerce Untuk Memperluas Jaringan Pemasaran Produk Olahan Hasil Laut di Desa Randutatah Ahmad Hudawi AS; Said Agil Asy’ari; Muhammad Naufal
Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat Vol. 1 No. 4 (2023): Desember : Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/kegiatanpositif.v1i4.466

Abstract

The implementation of this community service involves this Partner Village located in Randutatah Village, Paiton District, Probolinggo Regency. This group has various businesses, processed seafood. Technology development in the digital era has brought significant changes in various aspects of life, including in business and marketing. E-commerce is one form of technology utilization that is growing rapidly, providing new opportunities for business actors, including farmers and fishermen in Randutatah Paiton Village. However, obstacles such as limited internet access, lack of e-commerce knowledge, low marketing networks, and intense competition on e-commerce platforms are the main challenges.To overcome these obstacles, a training program on e-commerce utilization was conducted. This training program involves business owners and workers, with an implementation method that includes planning, implementing activities, and monitoring and evaluation. The purpose of the implementation of this community service is to apply e-commerce to the sale of various MSME products in Randutatah Village with the intention of introducing and increasing their sales turnover, while the method used is to use participatory methods, counseling, mentoring and training. The results obtained from the implementation of this community service are that with the help of training and e-commerce applications, it can reduce the sales turnover of MSME products in Randutatah Village.
Implementasi GridSearch dalam Meningkatkan Kinerja Model Support Vector Regresion (SVR) utuk Prediksi Penjualan Produk (Studi kasus : Meubel Rohman Jaya) Ahmad Baidowi Eko Fitra Firmanda; Ahmad Hudawi AS; Abu Tholib; Juvinal Ximenes Guterres

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.5042

Abstract

In the era of digitalization, product sales forecasting plays a crucial role for companies in estimating future demand. Meubel Rohman Jaya, a furniture business established since 2010, requires accurate predictions to optimize stock availability with the variety of products they produce. This research aims to forecast furniture product sales using the Support Vector Regression (SVR) algorithm with GridSearch optimization. Sales data of 11 furniture products over 30 months (January 2021 - June 2023) were processed through data collection and preprocessing. Modeling was performed using SVR without optimization and SVR with GridSearch optimization to obtain the best parameters. Predictions were generated and then evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that SVR without optimization achieved a MAPE of 40.39%, while SVR with GridSearch achieved a MAPE of 0.45%, indicating a significant increase in accuracy. GridSearch optimization has proven effective in improving prediction performance and is highly recommended for implementation in forecasting product sales at Meubel Rohman Jaya.
Perbandingan Penggunaan Arsitektur Graph GCN Dan LightGCN Pada Sistem Rekomendasi Hotel Berbasis Rating Pengguna Dengan Dataset Terbatas Rhodil Fauzi; Abu Tholib; Ahmad Hudawi AS
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35058

Abstract

Hotel recommendation systems often fail to recommend new hotels due to extreme data sparsity problems (item cold-start) and are vulnerable to the computational over-smoothing phenomenon. This study aims to comprehensively evaluate and compare the ranking architectures of Graph Convolutional Network (GCN) and LightGCN. The method used is a computational experiment using an Ablation Study approach to dissect the effect of propagation depth (1-Layer vs. Multi-Layer) and the injection of external features. The evaluation was conducted on a small-sized dataset from Mendeley Data containing review histories for 16 hotel entities. The main results show that the 1-Layer LightGCN with features is the most superior model for active users (warm-start), achieving an NDCG@10 score of 0.8702. However, in the extreme new hotel scenario (0-shot cold-start), this shallow architecture failed, and the best solution was actually won by the pure Multi-Layer model without features (No-Feature), which achieved a Hit Ratio (HR@10) of 71.43%. In conclusion, there is no single perfect model for all conditions; system implementation is recommended to adopt a dual-framework that integrates the speed of 1-Layer LightGCN and the propagation robustness of the Multi-Layer model.
Co-Authors Abu Tholib Ahmad Baidowi Eko Fitra Firmanda Juvinal Ximenes Guterres M Fadhilurrahman Muhammad Naufal Rhodil Fauzi Said Agil Asy’ari Wali Ja’far Shudiq