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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL MANAJEMEN BISNIS Jurnal Literasiologi Jurnal Tekno Kompak Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Jurnal Abdidas Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Silva Samalas: Journal of Forestry and Plant Science Jurnal Elkasista ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Inovasi, Evaluasi dan Pengembangan Pembelajaran (JIEPP) Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Journal of Engineering, Technology and Computing (JETCom) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Insand Comtech : Information Science and Computer Technology Journal Valid Jurnal Pengabdian Journal of Economics and Management Scienties Prosiding Seminar Nasional CORISINDO Media Pendidikan Matematika
Hairani Hairani
Universitas Bumigora, Mataram, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies Hairani, Hairani; Widiyaningtyas, Triyanna; Dwi Prasetya, Didik
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2283

Abstract

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.
Prediction of Electricity Usage with Back-propagation Neural Network Anggrawan, Anthony; Hairani, Hairani; Candra, M. Ade
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1722

Abstract

The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.
Web-Based Application for Toddler Nutrition Classification Using C4.5 Algorithm Hairani, Hairani; Nurhayati, Lilik; Innuddin, Muhammad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i2.2387

Abstract

Health is something that is important for everyone, from year to year various efforts have been developed to get better and quality health. Good nutritional status for toddlers will contribute to their health and also the growth and development of toddlers. Fulfillment of nutrition in children under five years old (toddlers) is a factor that needs to be considered in maintaining health, because toddlerhood is a period of development that is vulnerable to nutritional problems. There are more than 100 toddler data registered at the Integrated Healthcare Center in Peresak Village, Narmada District, West Lombok Regency. The book contains data on toddlers along with the results of weighing which is carried out every month. However, to classify the nutritional status of toddlers, they are still going through the process of recording in a notebook by recording the measurement results and then looking at the reference table to determine their nutritional status. This method is still conventional or manual so it takes a long time to determine the nutritional status. Therefore, the solution in this study is to develop a web-based application for the classification of the nutritional status of children under five using the C4.5 method. The stages of this research consisted of problem analysis, collection of 197 instances of nutritional status datasets obtained from Integrated Healthcare Center Presak, analysis of system requirements, use case design, implementation using the C4.5 method, and performance testing based on accuracy, sensitivity, and specificity. The results of this study are a website-based application for the classification of the nutritional status of children under five using the C4.5 method. The performance of the C4.5 method in the classification of the nutritional status of toddlers using testing data as much as 20% gets an accuracy of 95%, sensitivity of 100%, and specificity of 66.6%. Thus, the C4.5 method can be used to classify the nutritional status of children under five, because it has a very good performance.
Combination of Smote and Random Forest Methods for Lung Cancer Classification Michael Lauw, Christopher; Hairani, Hairani; Saifuddin, Ilham; Ximenes Guterres, Juvinal; Maariful Huda, Muhammad; 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.
Electric Vehicle Sales-Prediction Application Using Backpropagation Algorithm Based on Web Ramadhanti, Ramadhanti; Hairani, Hairani; Innuddin, Muhammad
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.v2i2.3388

Abstract

The accuracy of predicting future product sales is needed to minimize losses and gain profits. Inventory of goods carried out manually or improper product inventory planning causes the number of goods to accumulate due to the small number of requests, so the goods are damaged. Therefore, a sales prediction system with high accuracy is needed to assist in stocking electric vehicles. This research aimed to predict electric vehicle sales using the web-based backpropagation method. This study uses the backpropagation method to predict electric vehicle sales data from 2015 to 2022. The data is divided into 84 instances as training data and 12 instances as testing data. The result of this study was that the backpropagation method obtained a MAPE error rate of 6.25%. Thus, the backpropagation method can be used for predicting electric vehicle sales because it has a very accurate performance level.
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods Switrayana, I Nyoman; Ashadi, Diki; Hairani, Hairani; Aminuddin, Afrig
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.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data Hairani, Hairani; Ximenes Guterres, Juvinal
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.
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach Hairani, Hairani; Janhasmadja, Mengas; Tholib, Abu; Ximenes Guterres, Juvinal; Ariyanto, Yuri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4375

Abstract

The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria, Riosatria; Hairani, Hairani; Anggrawan, Anthony; Syahrir, Moch.
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method Huda, Dias Nabila; Anggrawan, Anthony; Hairani, Hairani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4601

Abstract

The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a ”medoid,” which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Co-Authors Abdillah, Mokhammad Nurkholis Abdurraghib Segaf Suweleh Abdurraghib Segaf Suweleh Abu Tholib Adam, M. Awaludin Afrig Aminuddin Ahmad Ahmad Ahmad Fathoni Ahmad Zuli Amrullah Amelia, Bengi Amin, Farda Milanda Andi Sofyan Anas Andi, Moh syaiful Anggarawan, Anthony Anthony Anggrawan Arfa, Muhammad Ashadi, Diki Astuti, Ni Luh Budi Ayu Dasriani, Ni Gusti Candra, M. Ade Christine Eirene Christopher Michael Lauw Dadang Priyanto Dedi Aprianto Dedy Febry Rachman Dedy Febry Rahman Deny Jollyta Dian Syafitri Didik Dwi Prasetya Diki Ashadi Dirgantara, Bhintang Donny Kurniawan Dyah Susilowati Dyah Susilowaty Edddy, Syaiful Eka Setiawan, Rian Putra Fahry, Fahry Fatimatuzzahra Fatimatuzzahra Fitra Rizki Ramdhani Gibran Satya Nugraha Gibran Satya Nugraha Gumangsari, Ni Made Gita Gustiya, Sherly Dwi Guterres, Juvinal Ximenes Hadi, M Fawazi Hammad, Rifqi Hartono Wijaya Haryono Haryono Hasbullah Hasbullah Heru Kurnianto Tjahjono Hery Widijanto Hidayati, Diana Huda, Dias Nabila I Gusti Agung Ayu Hari Triandini I Nyoman Switrayana Ida Putu Andika Ifnaldi, Ifnaldi Ilham Saifuddin Indah Puji Lestari Indradewa, Rhian Isviyanti, Isviyanti Janhasmadja, Mengas Jauhari, M. Thonthowi Jupriadi, Jupriadi Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Kandisa, Amelia Kasiyanto Kasiyanto, Kasiyanto Khairan marzuki Khasnur Hidjah Khurniawan Eko Saputro Kurniadin Abd Latif Kurniawan Kurniawan Lalu Ganda Rady Putra Lilik Nurhayati lnnuddin, Muhammad M. Ade Candra M. Rasyid Ridho Maariful Huda, Muhammad Malika, Riwayati Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Mayasari, Astri Michael Lauw, Christopher Miftahul Madani Muhamad Azwar Muhamad Azwar, Muhamad Muhammad Arfa Muhammad Innuddin Muhammad Maariful Huda Muhammad Ridho Akbar Muhammad Ridho Hansyah muhammad Syahbudi, muhammad Muhammad Zulfikri Muhammad Zulfikri Muhammad Zulkarnaen Haris Mujahid Mujahid Neny Sulistianingsih Noor Akhmad Setiawan Nurhayati, Lilik Nurul Azmi Nurvianti, Nurvianti Nuzululnisa, Bq Nadila Pahrul Irfan Putu Tisna Putra Qososyi, Sayidina Ahmadal Rahmawati, Lela Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Rifqi Hammad Riosatria, Riosatria Riwayati Malika RR. Ella Evrita Hestiandari Saifuddin Zuhri Saifuddin, Ilham Samsul Hadi Santoso, Heroe Shudiq, Wali Ja'far Soepriyanto, Harry Sofiansyah Fadli Sri Winarni Sofya Sri Winarni Sofya Sudi Prayitno Sukron, Moh Sutarman Sutarman Syahrir, Moch. tadianta m., Winardi aries Teguh Bharata Adji Tri Widayatsih, Tri Triwijoyo, Bambang Krismono Triyanna Widiyaningtyas Umi Hanifah Vidiasari, Herlita Vidiasari, Viviana Herlita Wahyuningsih, Rr. Sri Handari Widiatmoko, Dekki Winarni Sofya, Sri Wira Hendri Wiyanto, Suko Ximenes Guterres, Juvinal Yuri Ariyanto Zilullah Nazir Hadi