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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Quantitative Research and Modeling Formosa Journal of Applied Sciences (FJAS) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
R. Yadi Rakhman Alamsyah
Department Of Informatic, Faculty Of Technology And Informatic, Universitas Informatika Dan Bisnis Indonesia, Bandung, Indonesia.
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Physics Social Sciences Other

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Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Kolaborasi Kreatif Manusia dan Ai Untuk Generasi Masa Depan R. Yadi Rakhman Alamsyah; Reni Nursyanti; Anggi Dewi Nurcahyani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v5i1.181

Abstract

Digital transformation is currently dominated by developments in Artificial Intelligence (AI), which are bringing fundamental changes to the creative industry. For today’s youth, particularly vocational high school (SMK) students, mastering AI is no longer just an option but a necessity to maintain relevance and competence in the future.This Community Service Program (PkM) aims to equip students of SMK Bakti Nusantara 666 with a deep understanding of the creative collaboration between humans and AI. Through methods including counseling, practical mentoring in prompt engineering techniques, and evaluations via pre-tests and post-tests, this program targets an 80% increase in participants' digital literacy.The primary focus of this activity is to position AI as an 'intelligent assistant' that expands the imagination without eliminating the originality of human ideas, while consistently upholding ethics and copyright. The results of this program are expected to produce digital talents who are innovative, responsible, and ready to contribute to the creative economy.
A stacking ensemble model with SMOTE for improved imbalanced classification on credit data Nur Alamsyah; Budiman Budiman; Titan Parama Yoga; R. Yadi Rakhman Alamsyah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25921

Abstract

This research is based on a significant problem in credit risk analysis in the banking sector caused by class imbalance. We face the problem of the model’s inability to accurately identify risks in the ‘‘Charged Off’’ class. As a solution, we propose a stacked ensemble approach that utilizes synthetic minority over-sampling technique (SMOTE) to balance the class distribution. Experiments were conducted by applying SMOTE to the training data before training the credit model using gradient boosting (XGBoost) and random forest (RF) algorithms in a single ensemble. The results show significant improvements in precision, recall, and F1-score after applying SMOTE on the unbalanced classes. The updated model achieved a striking accuracy rate of 0,97 on resampled training data. This re-search clearly identifies the problem of class imbalance as a major challenge in credit risk analysis. The application of SMOTE in a stacked ensemble was found to be effective in improving model performance, making a valuable contribution to the development of more reliable credit models for better risk management and revenue generation in financial institutions.
Rancang Bangun Media Informasi Berbasis Multimedia Untuk Mencegah Risiko Stunting Pada Anak Balita R. Yadi Rakhman Alamsyah; Reni Nursyanti; Resvina Alya Putri
INTERNAL (Information System Journal) Vol. 6 No. 2 (2023)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v6i2.869

Abstract

Stunting is a growth disorder that describes the failure to growth potential as a result of inadequate health status or nutrition. Child stunting data in 2022 has fallen to 21.6%, but according to the World Health Organization (WHO) criteria, the percentage is still high (20%). One prevention in reducing the risk of stunting is to increase the knowledge of parents about stunting and the intake of good food to be consumed by children. The dissemination of information about stunting and nutritional food intake is often found in the media of information such as text, video, and images but the distribution of information is not organized in one medium. The development phase uses the Multimedia Development Life Cycle (MDLC) methodology with five (five) stages: concept, design, material collection, assembly, and testing. The result of this study is a multimedia-based information media in dasawisma RT 05 Bumi Orange, Cimekar Village, Bandung Regency, with a selection of stunting material menus including complementary foods for breast milk (MPASI), children's weight and height standards, child nutrition, and food intake.
Co-Authors Akbar, Imannudin Aliman Fauzi Anggi Dewi Nurcahyani Budiman Iqbal Maulana Ismi Nur Muhamad Luthfi Fadle Rynaldy Marwondo Nur Alamsyah Nur Alamsyah Nursyanti, Reni Resvina Alya Putri Sardjono Sardjono Setiana, Elia Silca Silkillah Adwa Titan Parama Yoga Venia Restreva Danestiara Wasnadi .