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All Journal Sinergi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pendidikan Matematika (Kudus) Journal of Soft Computing Exploration
Sam'an, Muhammad
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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Improved recommender system using Neural Network Collaborative Filtering (NNCF) for E-commerce cosmetic product Subhan, Subhan; Syarif, Deny Lukman; Widhihastuti, Endah; Rakainsa, Senda Kartika; Sam'an, Muhammad; Ifriza, Yahya Nur
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.014

Abstract

This study presents an enhanced recommender system tailored for e-commerce platforms specializing in cosmetic products. Traditional recommender systems often need help providing accurate and personalized recommendations due to the complexity and subjectivity inherent in cosmetic preferences. In e-commerce, personalized product recommendations are crucial to improving user engagement and driving sales. This paper presents an innovative approach to enhance recommendation systems by leveraging neural network collaborative filtering techniques for the cosmetic product domain. The proposed method integrates neural networks into collaborative filtering, namely neural network collaborative filtering with improved preprocessing step. To validate the effectiveness of our proposed system, extensive experiments were conducted using real-world e-commerce cosmetic datasets "eCommerce Event History in Cosmetics Shop".   Additionally, we evaluate the system's performance using historical e-commerce event data in cosmetics stores, demonstrating the system's effectiveness with mean reciprocal ratings (MRR) and normalized discount cumulative gain (NDCG). Evaluation Metrics of MRR and NDCG in this study got 0.56 and 0.60, respectively, with a split of the data: 70% train data, 15% validation data, and 15% test data. This study obtained better evaluation metrics than the previous study, which had an MRR of 0.31 and NDGC of 0.32. Furthermore, this model exhibits robustness against data sparsity and cold-start problems commonly encountered in e-commerce platforms. This research advances knowledge of recommendation systems and paves the way for more personalized and efficient online shopping experiences.
Irrigation management of agricultural reservoir with correlation feature selection based binary particle swarm optimization Ifriza, Yahya Nur; Sam'an, Muhammad
Journal of Soft Computing Exploration Vol. 2 No. 1 (2021): March 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i1.23

Abstract

The requirement for the applied innovation to farming water system is especially required for supplies, as rural water system focuses. Supplies as one of horticulture water system asset focus that are regularly constraints identified with the conveyance of repository water stream, this brought about lopsided dissemination of rural water system and the term of control of agrarian water system that streams from water system asset focuses. At the point when ranchers need to change the water system way, it will take a long effort to make another water system way. From these troubles to convey rural water systems simpler, it is important to plan a specialist framework to decide rural water system choices. A few researchers focused on improved quality of plant. There have been limited studies concerned with irrigation management Therefore, this research intends to design The objectives of this research are optimization irrigation management of agricultural reservoirs with CFS-BPSO. The consequences of this investigation demonstrate that the exactness of the utilization of the SVM calculation is 62.32%, while after utilizing the CFS calculation precision of 84.12% is acquired and exactness of ten SVM calculations by applying a blend of CFS highlight choice. also, BPSO 91.84%.
New fuzzy transportation algorithm without converting fuzzy numbers Sam'an, Muhammad; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.41

Abstract

The ranking function is widely used to convert fuzzy numbers to be crisp on solving fuzzy transportation problems. The converting process can indeed make it easier to play the fuzzy transportation method, but from the convenience, it causes failed in interpreting the results of converting fuzzy numbers. This is because the converting process of fuzzy numbers still has subjectivity values, so it cannot be eliminated, moreover, the ordering can cause incompatible input and output fuzzy numbers resulted. Therefore, the new fuzzy transportation method is proposed by fuzzy Analytical Hierarchy Process to order fuzzy parameters on fuzzy transportation problem without converting fuzzy numbers to crisp numbers, then Algorithm 2 until 6 is used to obtain a fuzzy optimal solution. The advantages of the new proposed method can improve the shortcomings of the existing methods, as well as relevant to solve fuzzy transportation problems in real life
Performance comparison of support vector machine and gaussian naive bayes classifier for youtube spam comment detection Ifriza, Yahya Nur; Sam'an, Muhammad
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.42

Abstract

Youtube is a video sharing site that was begun back in 2005. Youtube produces over 400 hours of substance each moment and more than 1 billion hours of substance are devoured by clients every day. In this work, we present a new approach by comparing the analysis results using a support vector machine and the Gaussian Naive Bayes classificatio. Our proposed methodology We used the dataset from UCI especially Youtube-Shakira for training and testing. The transformed dataset is split into training and testing subsets and fed into Naive Bayes and Support Vector Machin. In all cases, the F1 score was used to evaluate the classifier's performance. The results of the experiment are displayed in Gaussian Naive Bayes with an F1 score of 84.38% and a Support Vector Machine (SVM) with an F1 score of 88.00%. Naive Bayes is consistently the worst performer than SVM.
FORECASTING NICKEL PRICES WITH THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV APPROACH Haris, M. Al; Sari, Wulan; Fauzi, Fatkhurokhman; Sam'an, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1237-1250

Abstract

Nickel was a critical raw material used in a wide range of industries. The price movement of nickel tends to fluctuate and remain uncertain due to market conditions varying over time. Therefore, forecasting nickel prices was essential to understanding future price movements. In this study, we applied the automatic clustering fuzzy time series Markov chain method. The automatic clustering algorithm generates multiple intervals and fuzzy relations. Subsequently, forecasting was based on these fuzzy relations and a Markov chain transition probability matrix involving three stages to enhance forecast accuracy. We use monthly closing futures nickel price data from January 2009 to May 2024. The accuracy of the forecasting model was measured using the mean absolute percentage error (MAPE). The analysis showed that implementing the automatic clustering fuzzy time series Markov chain method results in excellent forecasting accuracy, with a MAPE value of 1.76% (equivalent to 98.24% accuracy). The predicted nickel price for June 2024 was US$ 19,608.5.
Solusi Optimal Masalah Transportasi Fuzzy Penuh Menggunakan Total Integral Ranking dan Ranking Score Method Sam'an, Muhammad
Jurnal Pendidikan Matematika Vol 1, No 2 (2018): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jmtk.v1i2.4144

Abstract

Abstract.Masalah transportasi fuzzy penuh merupakan masalah transportasi dimana biaya transportasi, jumlah persediaan, jumlah permintaan dan variabel keputusan dinyatakan dalam bentuk bilangan fuzzy. Untuk memecahkan masalah transportasi fuzzy tersebut, parameter bilangan fuzzy harus diubah ke bilangan crisp yang disebut metode perangkingan bilangan fuzzy. Pada tulisan ini diberikan masalah transportasi fuzzy yang diselesaikan menggunakan algoritma transportasi fuzzy dengan metode perangkingan yang berbeda yaitu total integral ranking  dan ranking score method. Algoritma Transportasi Fuzzy dengan perankingan Total integral Ranking menghasilkan solusi dan nilai optimal fuzzy yang lebih besar dibandingankan menggunakan Algoritma Transportasi Fuzzy dengan perankingan menggunakan Ranking Score Methode. Namun itersai yang diilakukan pada Algoritma Transportasi Fuzzy dengan perankingan Total integral Ranking lebih cepat dibandingkan Algoritma Transportasi Fuzzy dengan perankingan Ranking Score Method.
Co-Authors Fauzi, Fatkhurokhman M. Al Haris Rakainsa, Senda Kartika Subhan Subhan Syarif, Deny Lukman Widhihastuti, Endah Wulan Sari, Wulan Yahya Nur Ifriza