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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 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 INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System 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 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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PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN DECISION TREE DALAM KLASIFIKASI KANKER PARU-PARU Dwilestari, Gifthera; Azmi Afifah, Turfa
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12463

Abstract

Kanker paru-paru merupakan salah satu penyebab utama kematian di dunia, menjadikan deteksi dini sebagai hal yang sangat penting. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Naive Bayes dan Decision Tree dalam klasifikasi kanker paru-paru. Metode penelitian melibatkan tahapan Knowledge Discovery in Databases (KDD), termasuk pemilihan data, praproses, transformasi, penambangan data, dan interpretasi hasil. Dataset yang digunakan berasal dari repository publik dengan berbagai fitur terkait kondisi pasien. Hasil menunjukkan bahwa Naive Bayes memiliki akurasi lebih tinggi sebesar 92,47% dan unggul dalam mendeteksi kelas positif (recall sebesar 98,77%), tetapi kurang optimal pada kelas negatif. Sementara itu, Decision Tree menunjukkan akurasi 88,17% dengan keseimbangan deteksi antar kelas yang lebih baik (recall kelas negatif sebesar 66,67%). Berdasarkan hasil tersebut, Naive Bayes lebih cocok untuk aplikasi yang membutuhkan deteksi cepat dan efisien, sedangkan Decision Tree lebih sesuai untuk skenario dengan kebutuhan keseimbangan deteksi antar kelas. Penelitian ini memberikan rekomendasi algoritma yang sesuai berdasarkan kebutuhan spesifik aplikasi dan menyarankan pengembangan lebih lanjut untuk optimasi algoritma.
PREDIKSI PERSETUJUAN PINJAMAN MENGGUNAKAN DATASET LOAN APPROVAL MENGGUNAKAN ALGORITMA KLASIFIKASI Amal Rois, Moh. Ichlasul; Dwilestari, Gifthera; Suarna, Nana
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12486

Abstract

Persetujuan pinjaman merupakan aspek kritis dalam industri keuangan yang mempengaruhi kelancaran operasional bank dan lembaga keuangan. Namun, proses evaluasi permohonan pinjaman seringkali memakan waktu dan rawan kesalahan manusia. Oleh karena itu, diperlukan sistem prediksi yang efektif untuk meningkatkan akurasi dan efisiensi persetujuan pinjaman. Penelitian ini bertujuan untuk mengembangkan model prediksi persetujuan pinjaman menggunakan dataset Loan Approval dengan menerapkan algoritma klasifikasi. Masalah yang dihadapi adalah bagaimana mengidentifikasi faktor-faktor penting yang mempengaruhi persetujuan pinjaman dan membangun model yang dapat memprediksi hasil dengan tingkat akurasi yang tinggi. Metode yang digunakan dalam penelitian ini melibatkan pengumpulan dan praproses data, eksplorasi data untuk memahami distribusi dan karakteristiknya, serta penerapan berbagai algoritma klasifikasi seperti Logistic Regression, Decision Tree, dan Random Forest. Model yang dibangun kemudian dievaluasi menggunakan metrik kinerja seperti akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan kinerja terbaik dengan akurasi mencapai 85%, precision 83%, recall 82%, dan F1-score 82%. Temuan ini menunjukkan bahwa penggunaan algoritma klasifikasi dapat membantu lembaga keuangan dalam membuat keputusan persetujuan pinjaman yang lebih tepat dan efisien, sehingga dapat mengurangi risiko kredit macet dan meningkatkan kepuasan pelanggan. Penelitian ini memberikan kontribusi penting dalam bidang teknologi keuangan dengan mengusulkan model prediksi yang dapat diimplementasikan dalam sistem penilaian kredit yang lebih cerdas dan responsif.
KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Rosid, Abdur; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.398

Abstract

The Covid 19 outbreak has an impact on the community so that there are family heads who cannot work in general. The policy pursued by the central government is to provide assistance to workers who have salaries below 5 million and other programs. The obstacles faced to the community are not exactly recipients of assistance in accordance with the criteria set by the government. The criteria set by the government are workers who have salaries below 5 million. The purpose of the study can model the recipients of social assistance that is on target, so that the assistance can be useful in the time of the Covid 19 pandemic. This method of approaching research uses knowladge data discovery with the first stage of data obtained by social services in 2020 the second stage of data classification based on the riteri that has been established. The third stage of preprocessing is used to clean up noise data, stage four of the random forest model by using rapid miner tool version 9.9. Stage six discussion of the results of the model produced from random forest. The results expected in the study get a good model so that it becomes a recommendation in determining the recipients of sosial assistance
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i, Syafi'i; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
RANCANG BANGUN APLIKASI SISTEM INFORMASI PENDATAAN PELAUT BERBASIS WEB Dikananda, Arif Rinaldi; Fasa, Saefullah; Ali, Irfan; Dwilestari, Gifthera
JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.473

Abstract

PT. Abdi Marine is one of the companies that has not used a web-based information system in the marine data collection section, where the data processing system is still manual. It often happens that seafarers' registration and flight date research takes up a lot of paper and seafarer data storage space, the calculation of the date is less accurate and making reports of incoming and outgoing seafarers' data takes a lot of time. To emphasize and learn in understanding the problems as described, the problem formulation that researchers can explain is to design a computerized marine crew data collection information system, create a database of data services for managers to carry out their work. The purpose of this research is to find out, develop and create an ongoing data collection application system into the PHP and HTML programming language using the MySQL database. So that researchers can draw conclusions in processing sailor crew data collection by implementing applications that have been designed and built in a systematic and structured manner, so that the level of damage in the process of implementing sailor crew data collection can be resolved.
Irvan Himawan PREDIKSI HARGA SAHAM DENGAN ALGORITMA REGRESI LINIER DENGAN RAPIDMINER Himawan, Irvan; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.475

Abstract

Stock investment in the capital market is very important for every company in the world. Stock prices in the capital market move very randomly, the highs and lows of stock prices are influenced by many factors. Therefore, it is necessary to predict the stock price so that it can help investors to see investment prospects in the future. In this study, the prediction of the stock price of BRI Bank with the BBRI stock code will be carried out, using an algorithm, namely Linear Regression on rapid miners. This Linear Regression Algorithm is the best algorithm to use because it is the most complex compared to other algorithms. Based on signaling theory, which are information signals needed by investors, the value of forecasting results that have been obtained can be used to consider investors' decisions that the stock has high or low risk in the future. Based on the theory of risk, this forecasting analysis helps investors to minimize losses. Stock prediction is one of the technical analysis. Stock buying and selling transactions without technicalities are gambling behavior and contain gharar or ambiguity. The impact of not using this technical analysis clearly resulted in transactions containing maisir and gharar which were clearly prohibited. The historical stock data used in the test was obtained from the finance.yahoo.com web page with the category PT. Bank Rakyat Indonesia Tbk, or with the issuer code BBRI shares. What will be used is annual data for the last 5 years in the form of time series accompanied by open, high, low and volume variables as independent variables and close as dependent variables. The algorithm used is multiple linear regression.
Implementasi Data Mining Pada Proses Seleksi Beasiswa Menggunakan Naive Bayes Dan Backward Elimination Agustina, Irma; Dwilestari, Gifthera; Rinaldi, Ade Rizki
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol. 10 No. 1 (2025): JII Volume 10, Number 1, Januari 2025
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Proses seleksi penerima beasiswa sering kali menghadapi tantangan dalam mengelola data yang kompleks dan memastikan keakuratan seleksi. Penelitian ini bertujuan mengoptimalkan algoritma Naive Bayes melalui teknik Backward Elimination untuk efisiensi proses seleksi. Dataset penelitian terdiri dari 1.042 data penerima beasiswa, mencakup variabel seperti Indeks Prestasi Kumulatif (IPK), penghasilan, jumlah tanggungan, dan status beasiswa. Penelitian dilakukan menggunakan platform RapidMiner versi 10.2 dengan tahapan meliputi preprocessing, transformasi data, pembagian data latih dan uji melalui Split Data. Teknik Backward Elimination diterapkan untuk menyederhanakan model dengan menghapus variabel yang kurang signifikan. Hasil penelitian menunjukkan bahwa penerapan Naive Bayes dengan teknik Backward Elimination menghasilkan tingkat akurasi sebesar 74,62%. Variabel utama yang paling berpengaruh adalah tanggungan orang tua dan penghasilan, yang secara signifikan memengaruhi keputusan seleksi. Selain itu, teknik ini juga berhasil mengurangi kompleksitas model, meningkatkan efisiensi proses analisis, dan meminimalkan waktu serta sumber daya yang dibutuhkan. Penelitian ini mendukung pengembangan sistem seleksi berbasis data yang lebih transparan dan efisien. Implementasi teknik Backward Elimination mempermudah interpretasi model. Dengan demikian, hasil ini diharapkan dapat menjadi landasan bagi pengembangan sistem seleksi beasiswa berbasis machine learning yang lebih efektif, serta membuka peluang untuk penelitian lanjutan yang berfokus pada optimalisasi algoritma dan seleksi fitur di berbagai sektor.
Application of Neural Network to Predict Rupiah Exchange Rate Against Korean Won Saeful, Agung; Dwilestari, Gifthera; Rinaldi, Ade Rizki
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.v4i2.734

Abstract

This study investigates the application of neural networks for predicting the exchange rate of the Indonesian Rupiah against the Korean Won, addressing the challenges posed by currency fluctuations in international trade and investment. The research employs a data mining approach utilizing historical exchange rate data, which allows the neural network to identify complex patterns that traditional forecasting methods may miss. The model is developed using RapidMiner software, facilitating data preprocessing, transformation, and evaluation. The outcomes show that the predictions were quite accurate, as indicated by a low prediction error rate. The findings suggest that the neural network model not only provides reliable forecasts but also maintains consistent performance over time. This research contributes to the growing field of artificial intelligence in finance, highlighting the potential of advanced predictive models to enhance decision-making processes in the context of global economic interactions. The study underscores the importance of integrating technology with economic analysis to better navigate the complexities of currency exchange and its implications for financial risk management.
Accuracy in Sentiment Analysis of the by.U Application Using Naïve Bayes and SMOTE Techniques Athhar Hafizha Luthfi; 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.v4i2.737

Abstract

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Lestari, Anjar Ayuning; 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.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Rauf Chaerudin Abdullah Syafii Abdullah Syafii Aby Febrian Ade Irma Purnamasari Ade Irma Purnamasari Ade Kurnia, Dian Ade Rizki Rinaldi Agis Maulana Robani Agung Nugraha agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Rifa'i Ahmad Zam Zami Aldiani, Dea Alia Cahyani, Cica Alibasyah, Aziz Amal Rois, Moh. Ichlasul Ananda Rafly Andi Suandi Anita Nur Kirana Anwar Musaddad Apriliyani, Ela Arif Rinaldi Dikananda Arifin, Bagas Adam Athhar Hafizha Luthfi Auliya Azmi Afifah, Turfa Bagas Al Haddad Bambang Siswoyo Basysyar, Fadhil Muhammad Caswadi, Caswadi Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dea Miftahul Huda 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 Fajar Maulana Adji, Moh Fajria, Azzahra Moudy 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 Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Agustina Irma Purnamasari, Ade Irvan Himawan Jayawarsa, A.A. Ketut Karimah, Ayu Kaslani Kencana, Junaedi Surya Khoirul Huda, Muhammad Kokom Komariyah Lestari, Anjar Ayuning Martanto . Mar’atun Sholihah, Oliffia Maulana Sidiq, Cecep Mochamad Aditya Sunaryo Muhammad Abdurohman Muhammad Basysyar, Fadhil Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Muzaki, Fazri 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 NURUL HIDAYAH Nurwahidah, Dalilah Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Puspita Maulana Arumsari R, Nining Raditya Danar Dana Raena Agustin Laeliyah Rahaditya Dasuki Ramdhan, Dadan Ramiro Firjatullah, Federicko Ranu Husna Riyana, Iis Rizki Fauzi, Ahmad Rizqy, Muhammad Enricco Rosmeri Manurung, Agnes Rudi Kurniawan Saeful Anwar Saeful, Agung Saefullah Fasa Saepu Qirom, Dani Saepudin, Asep Saepul Hadi Sagita, Ayu Salsabila, Putri Septiana, Angga 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 Wulan Suci, Salwa Yubi Aqsho Ramadhan