Garuda - Garba Rujukan Digital

Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Munsarif, Muhammad; Sam'an, Muhammad; Raharjo, Samsudi
Jurnal Surya Masyarakat Vol 6, No 2 (2024): Mei 2024
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.2.2024.269-275

The development of artificial intelligence (AI)--based learning models has made significant progress alongside the abundance of data. This enables the creation of complex deep-learning models to tackle increasingly intricate tasks. Evolving machine learning algorithms become a key factor in enhancing AI model capabilities. The demand for smart and efficient solutions from the business sector drives the adoption of AI technology, supported by advances in sensor technology, the Internet of Things (IoT), natural language processing (NLP), and image recognition. This article highlights the potential impact of AI model development on the learning experience, especially at the Elementary (SD), Junior High (SMP), and Senior High School (SMA) levels. Implementing AI models in elementary and secondary schools can support student progress assessment, provide material recommendations based on student understanding, and develop skills. The study discusses a teacher training initiative using ChatGPT to understand and utilize artificial intelligence in education. Training results show that teachers can effectively create varied and engaging learning materials using ChatGPT. Despite AI's benefits, cultural and social values remain irreplaceable, such as ethics towards teachers and social interactions among students. In conclusion, digital literacy training for teachers is essential to enhance their ability to develop modern and effective learning models, with AI as a valuable tool in creating dynamic and interactive learning environments.

Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Munsarif, Muhammad; Sam'an, Muhammad; Raharjo, Samsudi
Jurnal Surya Masyarakat Vol 6, No 2 (2024): Mei 2024
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.2.2024.269-275

The development of artificial intelligence (AI)--based learning models has made significant progress alongside the abundance of data. This enables the creation of complex deep-learning models to tackle increasingly intricate tasks. Evolving machine learning algorithms become a key factor in enhancing AI model capabilities. The demand for smart and efficient solutions from the business sector drives the adoption of AI technology, supported by advances in sensor technology, the Internet of Things (IoT), natural language processing (NLP), and image recognition. This article highlights the potential impact of AI model development on the learning experience, especially at the Elementary (SD), Junior High (SMP), and Senior High School (SMA) levels. Implementing AI models in elementary and secondary schools can support student progress assessment, provide material recommendations based on student understanding, and develop skills. The study discusses a teacher training initiative using ChatGPT to understand and utilize artificial intelligence in education. Training results show that teachers can effectively create varied and engaging learning materials using ChatGPT. Despite AI's benefits, cultural and social values remain irreplaceable, such as ethics towards teachers and social interactions among students. In conclusion, digital literacy training for teachers is essential to enhance their ability to develop modern and effective learning models, with AI as a valuable tool in creating dynamic and interactive learning environments.

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

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

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

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

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

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.

Study of data mining techniques to classify the life expectancy of patients with chronic hepatitis Sam'an, Muhammad
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.17519

This study examines a hepatitis patient dataset using eleven machine learning (ML) models, including LR, SVM, KNN, DT, RF, XGBoost, LightGBM, GBDT, Cat- Boost, AdaBoost, and Stacking. The dataset is subjected to various analyses, includ- ing correlation analysis, age distribution exploration, class imbalance resolution, and feature importance evaluation using eight methods: Chi-square, DT, RF, XGBoost, LightGBM, GBDT, CatBoost, and AdaBoost. The results of this study indicate that the implementation of the SMOTE method and feature importance analysis improves the performance of ML models. Among the eleven models used, the LR model achieved the highest accuracy, reaching 93.75% before applying SMOTE and increasing to 100% after its implementation. Furthermore, the SMOTE method suc- cessfully addressed the issue of class imbalance in the dataset, as evidenced by the improvement in accuracy of the RF model after applying SMOTE. Overall, this study demonstrates that the use of the SMOTE method and feature importance analysis, particularly with the Chi-square method, plays a crucial role in improving the performance of ML models. SMOTE helps address class imbalance issues, while feature importance analysis assists in selecting relevant features. By combining both approaches, ML models achieve higher and better accuracy in classifying samples from the minority class

Title

Found 8 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 8 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 8 Documents
Search