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All Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) International Journal of Engineering Continuity UPGRADE: Jurnal Pendidikan Teknologi Informasi
Juvinal Ximenes Guterres
Universidade Oriental Timur Lorosa’e
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Combination of Smote and Random Forest Methods for Lung Cancer Classification Christopher Michael Lauw; Hairani Hairani; Ilham Saifuddin; Juvinal Ximenes Guterres; Muhammad Maariful Huda; Mayadi Mayadi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3333

Abstract

Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.
Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data Hairani Hairani; Juvinal Ximenes Guterres
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3874

Abstract

The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.
Implementasi GridSearch dalam Meningkatkan Kinerja Model Support Vector Regresion (SVR) utuk Prediksi Penjualan Produk (Studi kasus : Meubel Rohman Jaya) Ahmad Baidowi Eko Fitra Firmanda; Ahmad Hudawi AS; Abu Tholib; Juvinal Ximenes Guterres

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.5042

Abstract

In the era of digitalization, product sales forecasting plays a crucial role for companies in estimating future demand. Meubel Rohman Jaya, a furniture business established since 2010, requires accurate predictions to optimize stock availability with the variety of products they produce. This research aims to forecast furniture product sales using the Support Vector Regression (SVR) algorithm with GridSearch optimization. Sales data of 11 furniture products over 30 months (January 2021 - June 2023) were processed through data collection and preprocessing. Modeling was performed using SVR without optimization and SVR with GridSearch optimization to obtain the best parameters. Predictions were generated and then evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that SVR without optimization achieved a MAPE of 40.39%, while SVR with GridSearch achieved a MAPE of 0.45%, indicating a significant increase in accuracy. GridSearch optimization has proven effective in improving prediction performance and is highly recommended for implementation in forecasting product sales at Meubel Rohman Jaya.
SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification Baiq Candra Herawati; Hairani Hairani; Juvinal Ximenes Guterres
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.147

Abstract

This research focused on using machine learning methods for breast cancer diagnosis, considering that breast cancer is the scariest disease for women because it can cause mortality. Not only that, but there is also an increase in breast cancer death rates in women yearly.  Early prediction is the right solution to increase life expectancy and reduce mortality rates caused by breast cancer. However, breast cancer data has a problem, namely that the data is imbalanced, which harms the performance of the machine learning method itself. In the data, breast cancer had a Benign class (357 instances) more than the Malignant class (212 instances). Therefore, this study aimed to solve the problem of imbalanced data using the Smote variants and Random Forest approaches in breast cancer classification. The results of this study showed that the Smote approach with Random Forest had the best performance compared to Borderline Smote and Random Forest in the case of breast cancer data classification, where Smote with Random Forest produced an accuracy of 97.3%, sensitivity of 96.9%, and specificity of 97.8%. In comparison, Borderline Smote with Random Forest produced an accuracy of 96.4%, sensitivity of 95.6%, and specificity of 96.9%. The results of this study can contribute to predicting breast cancer using the proposed method, because it has been proven to have high accuracy.
Enhancing Hotel Recommendation Using Multi-Criteria Neural CollaborativeFiltering Abu Tholib; Fathorazi Nur Fajri; Ilham Saifudin; Hairani; Juvinal Ximenes Guterres
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 4 No 1 (2026): Agustus 2026 In-Press
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v4i1.6253

Abstract

The increasing volume of hotel information on online travel platforms made hotel selection more difficult for users because decision making had to consider multiple aspects simultaneously, including value, accessibility, service, room quality, cleanliness, and sleep quality. Conventional recommendation methods often depended on overall ratings and therefore were not sufficiently capable of representing the multidimensional nature of hotel preferences. This study proposed an improved multi-criteria neural collaborative filtering (MCNCF) model for hotel recommendation using the Bali Hotel Review dataset. The proposed model integrated user identity, hotel identity, and six structured hotel evaluation criteria to learn user preferences in a more detailed and preference-sensitive manner. The experimental design was also strengthened through a more reliable preprocessing and evaluation pipeline, including data splitting before scaling, training-based imputation for missing values, and user ranking evaluation. The model was implemented using embedding-based neural interaction learning to capture nonlinear relationships between users, hotels, and multi-criteria features. The results showed that the proposed approach achieved stable and competitive performance across testing splits of 10%, 20%, 30%, and 40%. On the original rating scale, the model produced the best Root Mean Square Error of 0.416400 and the lowest Mean Absolute Error of 0.351719. In addition, the ranking performance remained consistently high, with Normalized Discounted Cumulative Gain values ranging from 0.976540 to 0.996243. These findings demonstrated that the proposed approach provided an effective and robust solution for hotel recommendation by leveraging structured multi-criteria preference information within a neural recommendation framework.
Co-Authors Abu Tholib Ahmad Baidowi Eko Fitra Firmanda Ahmad Hudawi As Christopher Michael Lauw Fathorazi Nur Fajri Hairani Hairani Hairani Herawati, Baiq Candra Ilham Saifuddin Mayadi Mayadi Muhammad Maariful Huda Saifudin, Ilham