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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan Society
Antoni Wibowo
Faculty of Computing, Universiti Teknologi Malaysia, Johor
Humanities Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Stock Price Movement Classification Using Ensembled Model of Long Short-Term Memory (LSTM) and Random Forest (RF) Gunawan, Albertus Emilio Kurniajaya; Wibowo, Antoni
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1640

Abstract

Stock investing is known worldwide as a passive income available for everyone. To increase the profit possibly gained, many researchers and investors brainstorm to gain a strategy with the most profit. Machine learning and deep learning are two of these approaches to predicting the stock's movement and deciding the strategy to gain as much as possible. To reach this goal, the researcher experiments with Random Forest (RF) and Long Short-Term Memory (LSTM) by trying them individually and merging them into an ensembled model. The researcher used RF to classify the results from LSTM models obtained throughout the Hyperparameter Optimization (HPO) process. This idea is implemented to lessen the time needed to train and optimize each LSTM model inside the ensembled model. Another anticipation done in this research to overcome the time needed to train the model is classifying the return for longer periods. The dataset used in this model is 45 stocks listed in LQ45 as of August 2021 This research results in showing that LSTM gives better results than RF model especially when using Bayesian Optimization as the HPO method, and that the ensembled model can return better precision in predicting stocks in comparison to the LSTM model itself. Future improvement can focus on the model structure, additional model types as the ensemble model estimator, improvement on the model efficiency, and datasets research to be used in predicting the stock movement prediction
Analysis of the Implementation of ISO 27001: 2022 and KAMI Index in Enhancing the Information Security Management System in Consulting Firms Apriany, Allisha; Wibowo, Antoni
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.100385

Abstract

Keamanan Informasi Elektronik ini kini sudah menjadi hal yang perlu diperhatikan oleh seluruh perusahaan agar aset penting perusahaan tetap terjaga dan mendapatkan kepercayaan dari pelanggan atau klien. Dalam operasional sehari-hari, banyak aktivitas dan data pribadi yang dikirimkan ke perusahaan untuk melakukan transaksi. Akan tetapi, belum banyak perusahaan yang memiliki kesadaran akan keamanan informasi, yang apabila tidak dilakukan akan merugikan perusahaan. Selain itu, dapat menurunkan nilai kompetitif karena dinilai tidak mampu melindungi data pribadi pelanggan atau klien. Setiap kebocoran data dan pelanggaran keamanan informasi dapat merusak reputasi organisasi [1]. Oleh karena itu, penting untuk memiliki ISMS yang efektif sesuai dengan standar ISO 27001:2022 yang merupakan standar keamanan informasi internasional yang telah diterapkan pada banyak perusahaan di seluruh dunia. ISO 27001:2022, standar internasional untuk manajemen keamanan informasi, memberikan panduan dan persyaratan yang jelas untuk membangun, menerapkan, dan memelihara sistem keamanan informasi yang efektif. Dalam makalah ini, penulis akan menilai tingkat kematangan sistem manajemen keamanan informasi berdasarkan ISO 27001: 2022. Berdasarkan penilaian tersebut, perusahaan masih mampu mencapai standar ISO 27001:2022 dan Index KAMI. Beberapa perbaikan harus dilakukan untuk mencapai tingkat kematangan minimum III+ dari penilaian Index KAMI. Selain itu, berdasarkan ISO/IEC 27001:2022, skor hasil yang diperoleh adalah 39% yang dapat disimpulkan bahwa sebagian besar perusahaan belum menerapkan prosedur apa pun dan beberapa kontrol telah diterapkan. Oleh karena itu, rekomendasi perbaikan diperlukan bagi perusahaan, mulai dari penerapan kebijakan dan prosedur terkait manajemen keamanan informasi.
Elevating fraud detection: machine learning models with computational intelligence optimization Angelica, Cheryl; Charleen, Charleen; Wibowo, Antoni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4273-4280

Abstract

The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are particle swarm optimization (PSO) and genetic algorithm (GA). They are used to optimize the receiver operating characteristic (ROC) area under the curve (AUC) of the three machine learning algorithms, namely X-gradient boost, random forest classifier, and light gradient boost machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that random forest classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO. 
Combining XGBoost and hybrid filtering algorithm in e-commerce recommendation system Sinaga, Vincentius Loanka; Wibowo, Antoni
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp618-626

Abstract

This study proposes a hybrid filtering algorithm (HFA) that combines extreme gradient boosting (XGBoost), content-based filtering (CBF), and collaborative filtering (CF) to improve recommendation accuracy in electronic commerce (e-commerce). XGBoost first leverages demographic data (e.g., age, gender, and location) to address cold start conditions, producing an initial product prediction; CBF refines this prediction by measuring product similarities through term frequency-inverse document frequency (TF-IDF) and cosine similarity, while CF (implemented via singular value decomposition) further incorporates user interaction patterns to enhance recommendations. Experimental results across multiple datasets demonstrate that HFA consistently outperforms standalone XGBoost in key metrics, including precision, F1-score, and hit ratio (HR). HFA’s precision often exceeds 90%, indicating fewer irrelevant recommendations. Although recall levels remain modest, HFA exhibits stronger adaptability under cold start scenarios due to its reliance on demographic features and user-item interactions. These findings highlight the efficacy of combining advanced machine learning with hybrid filtering techniques, offering a more robust and context-aware solution for e-commerce recommendation systems.
Analisis Pengujian Otomatisasi pada Situs Web E-Commerce dan Tanggapan Pengguna Rizky, Mutiara Ayu; Wibowo, Antoni
Society Vol 13 No 1 (2025): Society
Publisher : Laboratorium Rekayasa Sosial, Jurusan Sosiologi, FISIP Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/society.v13i1.767

Abstract

This study compares two automation testing tools, Robot Framework and Cypress, applied to e-commerce websites. The testing procedure is based on predefined test case flows utilizing the Black Box Testing method, which focuses exclusively on functional validation according to requirement specifications, without reference to backend systems or source code. The research aims to evaluate the efficiency and effectiveness of both tools in executing identical test scenarios and to assess user responses concerning website access speed. Robot Framework, recognized for its keyword-driven testing approach, is compared with Cypress, a JavaScript-based end-to-end testing framework. The findings indicate that Cypress outperforms Robot Framework, particularly regarding execution speed and automated report generation. Cypress’s modern architecture and real-time interaction capabilities contribute to faster and more stable test execution. Conversely, while Robot Framework offers significant flexibility and extensibility, its performance is comparatively slower in this context. User feedback suggests that the Bhinneka, Gramedia, and Uniqlo websites are generally responsive and user-friendly; however, Uniqlo is notably preferred due to its accurate stock information and efficient delivery services.
BIBLIOMETRIC ANALYSIS OF NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES (N-BEATS) FOR RESEARCH TREND MAPPING Saputro, Dewi Retno Sari; Prasetyo, Heri; Wibowo, Antoni; Khairina, Fadiah; Sidiq, Krisna; Wibowo, Gusti Ngurah Adhi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1103-1112

Abstract

Bibliometrics is the statistical analysis of articles, books, and other forms of publication. The bibliometrics analysis is performed with data on the number and authorship of scientific publications and articles, and citations to measure the work of individuals or groups of researchers, organizations, and countries to identify national and international networks and map developments in new multidisciplinary fields of science and technology. In addition, bibliometrics assesses and maps the research, organization, and country of researchers at a given time period. The Bibliometric analysis also has advantages which include mapping relationships between concepts, mapping research directions or trends, mapping state of the art (the novelty of the results of research conducted), and providing insights related to fields, topics, and research problems for future works. This study aims to determine the growth and development of N-BEATS publications, their distribution, variable keywords, and author collaboration using a bibliometric network. The research method used in this paper, through screening of articles obtained from the Scopus database page in 2008-2022, is used for citations in the form of metrics. At the same time, they are visualizing the metadata with VOSviewer. Data was collected from the direct science database with the keyword N-BEATS. The results show that 2022 has the highest number of publications, reaching 310 publications (14.90%). The distribution of research publications on N-BEATS shows a perfect distribution. Terms in the N-BEATS variable that often appear and are associated with other variables.
SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS Putera Khano, Muhammad Nazhif Abda; Saputro, Dewi Retno Sari; Sutanto, Sutanto; Wibowo, Antoni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2235-2242

Abstract

Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
CLASSIFICATION OF SKELETAL MALOCCLUSION USING CONVENTIONAL NEURAL NETWORK (CNN) WITH VISION ATTENTION Ronny Eka Wicaksana, I Putu; Wibowo, Antoni; Rojali, Rojali; A Samah, Azurah; Alias, Aspalilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2709-2726

Abstract

Skeletal malocclusion, a common orthodontic condition, affects jaw function and dental health. It is often caused by genetic factors, abnormal growth, bad habits, or trauma. Conventional diagnostic models often fail to generalize across diverse datasets, leading to overfitting and poor test performance. This study aimed to improve diagnostic accuracy by incorporating Vision Attention mechanisms into a custom Convolutional Neural Network (CNN), enabling the model to focus on critical regions in X-ray images. A total of 491 radiographic images depicting facial skeletal structures with various malocclusion types (Classes 1, 2, and 3) were used in this study. A custom CNN was developed and evaluated both with and without attention mechanisms—specifically, Scaled Dot Product Attention and Multihead Attention—to assess their impact on classification performance. The baseline CNN without attention achieved an accuracy of 0.68. With Scaled Dot Product Attention, accuracy improved to 0.72, while Multihead Attention achieved the highest accuracy of 0.76. Evaluation using weighted average precision, recall, and F1-score showed that attention mechanisms significantly enhanced the model’s ability to differentiate between malocclusion classes. Notably, the Multihead Attention model yielded the best performance, reducing misclassification errors and improving generalization. Confusion matrix analysis revealed that it had the lowest classification errors, especially in distinguishing between Class 0 and Class 1. These results suggest that incorporating attention mechanisms, particularly Multihead Attention, enhances CNN performance by improving feature extraction and classification accuracy. Future research should explore more diverse datasets and implement advanced augmentation techniques to improve clinical reliability.
Co-Authors A Samah, Azurah Alias, Aspalilah Angelica, Cheryl Apriany, Allisha Charleen, Charleen Dewi Retno Sari Saputro, Dewi Retno Gunawan, Albertus Emilio Kurniajaya Khairina, Fadiah Prasetyo, Heri Putera Khano, Muhammad Nazhif Abda Rizky, Mutiara Ayu Rojali, Rojali Ronny Eka Wicaksana, I Putu Sidiq, Krisna Sinaga, Vincentius Loanka Sutanto Sutanto Wibowo, Gusti Ngurah Adhi