Article Per Year (5 Year)
p-Index From 2021 - 2026
3.487
P-Index
This Author published in this journals
All Journal Format : Jurnal Imiah Teknik Informatika IJISCS (International Journal Of Information System and Computer Science) Building of Informatics, Technology and Science Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Journal Social Science And Technology For Community Service ICTEE (Engineering Journals of Information, control, telecommunication and electrical) Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Paradigma Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer Jurnal Media Celebes Journal of Data Science and Information System Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Swarnadwipa Jurnal Informatika dan Rekayasa Perangkat Lunak
Gunawan, Rakhmat Dedi
Universitas Teknokrat Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search

 1   2   3 
Combination of SMART and CRITIC Methods in Decision Support Systems for Determination of Superior Products Gunawan, Rakhmat Dedi
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 2 No. 4 (2024): Volume 2 Number 4 October 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v2i4.158

Abstract

Superior products are goods or services that have a higher value compared to other similar products because of their quality, innovation, or uniqueness. These products are usually produced through a standardized production process, utilizing selected raw materials, and supported by modern technology and special skills. The determination of superior products is carried out through a comprehensive analysis process to identify goods or services that have great potential to provide added value, both economically and socially. The main problem in determining superior products often arises due to the lack of accurate and comprehensive data on market potential, product quality, and consumer needs. Inaccuracies in setting evaluation criteria or weights given to each criterion can also result in bias in the selection process. This research aims to implement a combination of SMART and CRITIC methods in a decision support system to determine superior products objectively and efficiently. This combination is designed to take advantage of the advantages of the SMART method in evaluating alternatives based on multi-criteria utility, as well as the CRITIC method in determining the weight of the criteria objectively based on data variation and correlation between criteria. The results of the ranking of seven products are based on the total value that has been calculated using a certain evaluation method. Product G (D700) ranks first with the highest score of 0.78961, showing the best performance compared to other alternatives. These results provide clear information about which products are superior and can be the basis for decision-making in choosing the best product.
Modification of Additive Ratio Assessment Method through Distance-Based Weighting Approach for Optimizing Assessment Accuracy Gunawan, Rakhmat Dedi; Arshad, Muhammad Waqas; Wahyudi, Agung Deni; Suryono, Ryan Randy; Widodo, Tri; Ulum, Faruk
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8810

Abstract

The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Wang, Junhai; Darwis, Dedi; Gunawan, Rakhmat Dedi; Ariany, Fenty
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.6

Abstract

The number of users of e-commerce platforms has increased significantly in recent years, and consumers are now more likely to shop online due to ease of access, diverse product choices, and flexibility in transaction times. The difficulty in determining the best e-commerce platform is often caused by subjectivity in the weighting of the criteria used for evaluation. The weighting process is carried out based on the preferences of certain individuals or groups, without considering objective data. This research aims to apply an objective, structured, and accurate approach in evaluating and ranking e-commerce platforms based on relevant multi-dimensional criteria. By using the root assessment method, the evaluation process can be carried out systematically through hierarchical analysis, while the MEREC weighting ensures that the weight of each criterion reflects its real impact on the outcome of the decision. Through the combination of these two methods, this research is expected to make a significant contribution to improving the quality of decision-making, especially in helping users or business people choose the e-commerce platform that best suits their needs. The results of the final score calculation Platform E was ranked first with the highest score of 4.87083, Platform A was ranked second with a score of 4.85162, and Platform B was ranked third with a score of 4.83842. Future research should address the identified limitations by exploring the integration of advanced predictive analytics and artificial intelligence techniques to improve the adaptability and resilience of models. In addition, sensitivity analysis of the MEREC Root Assessment and Weighting Methods should be performed to understand its performance under various data conditions.
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
IMPLEMENTATION OF SMARTER AND ORESTE METHODS FOR DETERMINING UNDERDEVELOPED VILLAGES Praptiwi, Riska Amalia; Suaidah, Suaidah; Gunawan, Rakhmat Dedi; Prakoso, Ryo Cahyo; Rudhistiar, Deddy; Mountaines, Patricia Evericho; Saputra, Thesa Adi
Jurnal Data Mining dan Sistem Informasi Vol 4, No 2 (2023): Agustus 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v4i2.3239

Abstract

Villages have less development than cities because villages have bigger problems such as higher poverty rates, lower health, lower human resources, facilities and infrastructure that are more difficult to reach than cities. Therefore we need the concept of sustainable village development. In sustainable development, the aspect of development is not only aimed at present society but also society in the future. Before making the concept of sustainable village development, so that village development in a city/regency/district area is conceptualized evenly, decision support is needed to identify underdeveloped villages. Some indicators villages or underdeveloped regions mostly related to the survey of Potensi Desa activities by BPS from 1980 to 2014 continually participated. Related to that conditions, criteria obtained underdeveloped villages by DPU and indicator data PODES by BPS, it can be applied on Decision Making System. In this research selected case studies are census data from Potensi Desa by BPS in Magetan. This system uses SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) methods as the calculation of the weights to the criteria and ORESTE methods used for the rankings of underdeveloped villages. In this system SMARTER methods using a weighting formula Rank Order Centroid (ROC) that is proportional weighting which reflects the distance and the priority of each criteria appropriately. Furthermore, the ranking process using Oreste methods by three main stages that is Projection Matrix position, Ranking of projections and Agegration of Global Ranking. Testing of this system, which one is changing the parameters of Oreste (α value) and obtained compatibility reach 91.06% of accuracy to the experts data of underdeveloped villages from the BPS Magetan by the number 100% alternative data with value 0:01 of alpha
Hybrid Music Recommendati1on System Using K-Means Clustering and Neural Collaborative Filtering for Spotify Playlist Personalization Pamungkas, Rastomi; Permata, Permata; Gunawan, Rakhmat Dedi; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9181

Abstract

Personalizing music recommendations has become a significant challenge on music streaming platforms such as Spotify due to the vast number of available songs and the limitations of conventional recommendation systems in accurately capturing user preferences. In addition, traditional single-method recommendation approaches often face the cold start problem, which reduces the effectiveness of generated recommendations. Therefore, this study aims to develop and evaluate a hybrid recommendation system that integrates the K-Means Clustering algorithm and Deep Collaborative Filtering based on Neural Matrix Factorization to improve the relevance of music playlist recommendations. The dataset used in this study consists of more than 15,151 Spotify songs obtained from the Spotify dataset available on Kaggle. The dataset was processed through several stages including data inspection, data cleaning, feature selection, and standardization. Audio features used in the analysis include danceability, energy, acousticness, instrumentalness, valence, tempo, and duration. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, resulting in five clusters with a relatively balanced data distribution. The clustering results were then used as the basis for Cluster-Based Filtering to narrow the search space of candidate songs before being processed by the Neural Matrix Factorization model. Performance evaluation was conducted using Hit Ratio at rank 10 and Normalized Discounted Cumulative Gain at rank 10. The proposed model achieved values of 0.1110 and 0.0507, respectively, indicating that the integration of clustering and deep collaborative filtering can improve the effectiveness and personalization of music recommendation systems. This study contributes by proposing a hybrid recommendation framework that integrates clustering-based item grouping with deep collaborative filtering to improve recommendation efficiency and playlist personalization in large-scale music streaming platforms.
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.
Co-Authors Abim Ari Bimantara Ade Surahman Agung Setiawan Aldi Nugraha Anggun Maylani Annisa Anggraini Ariany, Fenty Arshad, Muhammad Waqas Bagus Abadi Deddy Rudhistiar Dedi Darwis Dimas Rizki Mahendra Edwin Dwi Andriano Esy Ervina Yanti Fenty ariany Fuad Surya Mawinar Hayatunnisa, Destaria Heni Sulistiani Imam Ahmad Irma Hanifah Lathifah Lathifah Mico Fahrizal Muhamad Odhie Prasetio Muhtad Fadly Novriyadi Novriyadi Novriyadi Pamungkas, Rastomi Patricia Evericho Mountaines Permata Permata Permata, Permata Prakoso, Ryo Cahyo Praptiwi, Riska Amalia Prasetyawan, Purwono Priandika, Adhie Thyo Rido Febryansyah Riduwan Napianto Roby Satria Rohmat Indra Borman Ryan Andre Nugroho Ryan Randy Suryono Saputra, Thesa Adi Stevani Natasiyah suaidah suaidah SUAIDAH SUAIDAH, SUAIDAH Temi Ardiansah Tri Widodo Ulum, Faruk Wadiyan, Wadiyan Wahyudi, Agung Deni Wang, Junhai Wijayanto, Danur Yuri Rahmanto Zaenal Abidin