Article Per Year (5 Year)
p-Index From 2021 - 2026
2.392
P-Index
This Author published in this journals
All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial MULTINETICS Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) Ladang Artikel Ilmu Komputer Journal of Entrepreneurship and Community Innovations Journal of Information Technology
Dede Wintana
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : J-Intech (Journal of Information and Technology)

 1 
Sistem Rekomendasi Produk Menggunakan Metode User-Based Collaborative Filtering Pada Digital Marketing Suhada, Satia; Bahri, Saeful; Nugraha, Setyo Bagus; Hidayatulloh, Taufik; Wintana, Dede
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.866

Abstract

The recommendation system has been implemented in digital marketing used in marketing products and services. The recommendation system is used to provide offers of goods and services in accordance with customer habits and interests in the proposed products and services, but in practice the right product offering for customers leads to the idea of developing a product recommendation system. Purchase data obtained from customers can be used to analyze customer needs and product preferences. In the recommendation system, Collaborative Filtering is one of the most commonly used algorithms. The purpose of this study is to find out how accurate the recommendation system is based on the purchase of similar goods between consumers using User-based Collaborative Filtering. Based on the results of the study, User-based Collaborative Filtering using Cosine Similarity calculations can be applied and produce 10 product recommendations with an RMSE value of 0.9.
Penerapan Multi-Layer Perceptron dan Diskrit pada Prediksi Cacat Software Wintana, Dede; Gunawan, Gunawan; Sulaeman, Hamdun; Bahri, Saeful
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1422

Abstract

Software defects are one of the main causes of information technology waste, posing a major challenge in software development as they can degrade the quality of the software itself. To reduce costs and efforts in software development and maintenance, predicting software defects is the best approach. Multi-Layer Perceptron (MLP) is a type of artificial neural network that can be used to learn complex and non-linear patterns in input data. It excels in modeling complex and non-linear relationships in data, as well as automatically extracting features and handling problems that cannot be solved by linear models. One of the preprocessing steps to optimize MLP is data discretization, which involves dividing the range of attributes into intervals to reduce the number of numerical attributes to categorical data. Testing results with five types of data from NASA MDP (CM1, JM1, KC1, KC2, and PC1) showed significant accuracy improvements. In the CM1 dataset, accuracy increased to 96.1% compared to using MLP alone, which achieved 91.1%. In the JM1 dataset, accuracy increased to 79.1% compared to MLP alone, which achieved 78.3%. In the KC1 data, accuracy increased to 88.5% compared to MLP alone, which achieved 85.9%. In the KC2 dataset, MLP with discretization achieved an accuracy of 89.8%, better than MLP alone at 84.8%. In the PC1 data, the highest accuracy obtained was 95.5% compared to MLP alone, which achieved 94.3%.
Analyzing Students' Interest in Mathematics Through the Implementation of the K-Means Clustering Algorithm Wintana, Dede; Sulaiman, Hamdun; Rohman, Ramdhan Saepul; Gunawan, Gunawan; Ghani, Muhammad Abdul
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1861

Abstract

This Research is motivated by the importance of understanding students' interest in mathematics, especially in State Junior High School 193 East Jakarta, considering that mathematics is often considered a difficult and frightening subject for some students. Learning interest, which is defined as the tendency of students to pay attention with a feeling of pleasure, has a significant influence on the process and results of student learning. This study aims to identify the level of student interest in mathematics using the K-Means algorithm. This method is used to group students into several clusters based on their level of interest. The results showed that students were divided into three clusters, namely the first cluster with very high interest totaling 193 students with an average Final Semester Exam score of 91.920, the second cluster with low interest totaling 18 students with an average score of 52.333, and the third cluster with high interest totaling 66 students with an average score of 87.606.
Co-Authors Ami Rahmawati Ami Rahmawati Ami Rahmawati Anak Agung Istri Sri Wiadnyani Apip Supiandi Daniel Ratag Diah Ayu Ambarsari Ghani, Muhammad Abdul Gunawan Gunawan Gunawan Gunawan Hamdun Sulaiman Hikmatulloh Hikmatulloh Hikmatulloh Hikmatulloh Jajang Jaya Purnama Jasisca Marleftan Moh Yusup Nurhadi Novita, Yati Nugraha, Setyo Bagus Nurul Ichsan Pribadi, Denny Rahmawati, Ami Ramdhan Saepul Rohman, Ramdhan Saepul Risma Nurfauzia Rusda Wajhillah Saeful Bahri Saeful Bahri Saeful Bahri Satia Suhada, Satia Setyo Bagus Nugraha Sulaeman, Hamdun surendi Taufik Hidayatulloh Taufik Hidayatulloh Veithzal Rivai Zainal yati novita, surendi Yusti Farlina