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All Journal Jurnal technoscientia Jurnal Informatika dan Teknik Elektro Terapan Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL ILMIAH INFORMATIKA Pelita : Jurnal Penelitian dan Karya Ilmiah JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Komputer JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal Tekno Kompak Jurnal ICT : Information Communication & Technology Jurnal Manajemen Komunikasi Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) E-Link: Jurnal Teknik Elektro dan Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Informatika Terpadu Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi JURSIMA Jurnal Teknologi Ilmu Komputer AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Manajemen Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Ilmiah Sistem Informasi Informasi interaktif : jurnal informatika dan teknologi informasi
Gifthera Dwilestari
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5159

Abstract

The K-Means method is one of the Data Mining methods that is widely used in clustering research. Based on the results of research that has been conducted to build a process model for Poverty Data Clustering Analysis Using the K-Means Method Approach in Teluk Agung Village, Indramayu District, Indramayu Regency, can use RapidMiner tools by creating operators and processing parameters used for clustering the P3KE class category. The operators used in this study are Read Excel, Set Role, Select Attributes, Replace Missing Values, Nominal to Numerical, Multiply, Clustering (K-Means) and Performance operators. The operators used are 8 operators by applying the stages of Knowledge Discovery in Database (KDD). This research will apply the Davies Bouldin Index (DBI) as a way of optimising the number of clusters to group data, from the best cluster value experiment, the closest to 0 is K9 with a DBI value of -2.257, from this we can conclude that approximately 43 items from clusters 2 - 10 are included in the P3KE category, and other than the 43 items can be interpreted as still not included in the P3KE category.
Jurnal Klasifikasi KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5162

Abstract

Poverty is one of the fundamental problems that is of concern to governments in all countries.  An important aspect to support poverty alleviation strategies is the availability of accurate and targeted poverty data. Staple food is the nine basic needs of Indonesian people, including food or drinks used in daily life. On this basis, the government often organizes basic food assistance programs for those in need. classification is one of the most commonly used prediction techniques to predict new labels or categories based on experience gained from known data. The main purpose of classification is to understand patterns or relationships between input and output variables, so that you can take appropriate decisions or actions based on the available information. Based on the results of the analysis and implementation of the Decision Tree Algorithm for classification of eligibility for basic food aid recipients in the Teluk Agung Village area, Indramayu District, Indramayu Regency, it can be concluded that the model developed has a very high level of accuracy, namely 94.83%. This model has proven effective in classifying various categories that are worthy of receiving assistance, starting from class 1, 2, 3 and not worthy of receiving assistance. The factors used in this model, such as monthly income, have been processed well through stages in the Knowledge Discovery in Databases (KDD) framework, resulting in a reliable classification. With high accuracy and performance, it is hoped that this model can be implemented practically to support decision making in mitigating the risk of non-delivery of basic food aid in the Teluk Agung Village area, Indramayu District, Indramayu Regency.
Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial di Desa Cigayam Dede Hoeriah; Bani Nurhakim; Sandy Eka Permana; Willy Prihartono; Gifthera Dwilestari
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 1 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No1.pp52-58

Abstract

Social assistance is one of the government's programmes aimed at improving the lives of people especially for those who are economically disadvantaged. However, there are several reasons why some people are unable to access social assistance. In the case of this study, the authors used the Naïve Bayes algorithm with the KDD (Knowledge Discovery Database) method to predict the population in obtaining social assistance. The data was taken from the population data of Cigayam Village and the social welfare recipient data in the village ofCigayam with the results showing high accuracy in this study, for the true or false outcome of 1047 data and 53 data with the precision grade of 95.18%, 81.17%, for the real outcome, and 28.38% for the wrong outcome. So with the ROC curve shows the accuracy of the spinning visually, with an AUC of 0.868% for naïve bayes using the ROK curve of 0.90.1.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Anjar Ayuning Lestari; Ahmad Faqih; Gifthera Dwilestari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.743

Abstract

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
The Impact of Principal Component Analysis Dimensionality Reduction on Sentiment Classification Performance Using Support Vector Machine Azzahra Moudy Fajria; Ahmad Faqih; Gifthera Dwilestari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.744

Abstract

This study investigates the application of Principal Component Analysis (PCA) to enhance sentiment classification performance using the Support Vector Machine (SVM) algorithm. User reviews of the ChatGPT application from the Play Store were collected, preprocessed, and analyzed to identify the sentiment within the text (positive, negative, or neutral). The research follows the Knowledge Discovery in Databases (KDD) framework, starting with data selection, preprocessing, transformation, and applying PCA for dimensionality reduction. PCA was used to reduce the complexity of the high-dimensional text data, improving SVM's efficiency in sentiment classification. Evaluation results show that applying PCA led to an improvement in model performance, with accuracy increasing from 72.65% to 73.20%, precision from 71.58% to 72.24%, recall from 71.77% to 72.66%, and F1-score from 71.56% to 72.32%. Although the improvements were modest, the findings demonstrate that PCA effectively simplifies complex datasets and enhances SVM performance in sentiment classification, offering benefits in processing high-dimensional text data.
Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews Muhammad Enricco Rizqy; Ahmad Faqih; Gifthera Dwilestari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.750

Abstract

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Rauf Chaerudin Abdullah Syafii Abdullah Syafii Aby Febrian Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Agis Maulana Robani Agung Nugraha Agung Saeful agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Rifa'i Ahmad Zam Zami Aldiani, Dea Alia Cahyani, Cica Alibasyah, Aziz Ananda Rafly Andi Suandi Anita Nur Kirana Anjar Ayuning Lestari Anwar Musaddad Apriliyani, Ela Arif Rinaldi Dikananda Arifin, Bagas Adam Auliya Azzahra Moudy Fajria Bagas Al Haddad Bambang Siswoyo Bani Nurhakim Basysyar, Fadhil Muhammad Caswadi, Caswadi Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dea Miftahul Huda Dede Hoeriah Dessy Angelina Destriyanah, Riska Dian Ade Kurnia Dias Bayu Saputra Dienwati Nuris, Nisa Dienwati, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dzaffa 'Ulhaq Edi Tohidi Edi Tohidi Eka Permana, Sandy Fadhil Muhammad Basysyar Fadhil Muhammad Basysyar Fajar Fauzan, Muhammad Fasa, Saefullah Fathurrohman Fathurrohman Fatihanursari Dikananda Faujia, Agnes Fithrah Ali, Dini Salmiyah Fuadi Ahmad, Cecep Hamonangan, Ryan Haris Abdul Hadi Herdiana, Rulli Hermawan, Bagus Hermawan, Muhammad Andi Hilya Ashfia Nabila Himawan, Irvan Hira Wahyuni Azizah Hoeriah, Dede Hoerunnisa, Anis Iin Iin Solihin Iis Riyana Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Agustina Irma Purnamasari, Ade Irvan Himawan Jayawarsa, A.A. Ketut Karimah, Ayu Kaslani Kencana, Junaedi Surya Khaerul Anam Khoirul Huda, Muhammad Kokom Komariyah Martanto . Mar’atun Sholihah, Oliffia Maulana Sidiq, Cecep Mochamad Aditya Sunaryo Muhammad Abdurohman Muhammad Basysyar, Fadhil Muhammad Enricco Rizqy Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Nana Suarna Nana Suarna Nana Suarna Narasati, Riri Narasati Nining R Nining Rahaningsih Nisa Dieanwati Nuris Nur Amalia Nur Kirana, Anita Nuraini, Asyifa Nurhakim, Bani Nurul Aini, Yuli Nurwahidah, Dalilah Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Puji Pramudya Marta Purnamasari, Ade Irma Purnamasari, Adinda Puspita Maulana Arumsari R, Nining Raditya Danar Dana Raena Agustin Laeliyah Rahaditya Dasuki Ramdhan, Dadan Ramiro Firjatullah, Federicko Ranu Husna Rini Astuti Rizaldy, Farhan Rohmat, Cep Lukman Rosmeri Manurung, Agnes Rudi Kurniawan Saeful Anwar Saefullah Fasa Saepu Qirom, Dani Saepudin, Asep Saepul Hadi Sagita, Ayu Salsabila, Putri Sandy Eka Permana Sandy Eka Permana Septiana, Angga Setiawan, Riyan Sri Suwartini Suandi, Andi Suarna, Nana Subhiyanto, Fajar Sunana, Heliyanti Suryani Dewi, Ike Susana, Heliyanti Syafi'i Syafi'i Syafi'i, Syafi'i Tati Suprapti Tohidi, Edi Tuti Hartati Umi Hayati Vibrianti, Vera Wahyudin, Edi Willy Prihartono Yubi Aqsho Ramadhan Zacky Muhammad Dinata