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All Journal Syntax Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Conference on Innovation and Application of Science and Technology (CIASTECH) Jambura Journal of Informatics CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Sisfotek Global JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Teknosains : Jurnal Sains,Teknologi dan Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jumat Informatika: Jurnal Pengabdian Masyarakat Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Sisfotek Global Jurnal Sosial dan Abdimas Scientific Student Journal for Information, Technology and Science Jurnal Algoritma Jurnal Buana Pengabdian Eduvest - Journal of Universal Studies Innovative: Journal Of Social Science Research Jurnal Informatika: Jurnal Pengembangan IT
Juwita, Ayu Ratna
Universitas Buana Perjuangan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS Fauzi, Farras Ahmad; Rohana, Tatang; Juwita, Ayu Ratna; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.3697

Abstract

Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values ​​of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.
Kompetensi Digital Guru-Guru Pesantren Al-Kautsar Melalui Pelatihan Teknologi Pendidikan Sukmawati, Cici Emilia; Juwita, Ayu Ratna; Latifah, Nurul; Khairani, Nova Pustita
Jumat Informatika: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): April
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasif.v6i1.5633

Abstract

The digital literacy skills of teachers in Islamic boarding schools are still low, especially in the use of technology to support learning activities and academic administration. One of the challenges faced is the lack of skills in using word processing software to systematically compile teaching materials and academic documents. This community service aims to improve the digital competence of Islamic boarding school teachers through training in the use of word processing software. The method used is a participatory approach with stages of planning, implementation of training based on direct practice, and evaluation through pre-tests and post-tests. This activity involves Islamic boarding school teachers as the main participants who receive intensive training in document creation and formatting, table management, and the use of automation features such as mass mailings and tables of contents. The results of the community service show an increase in participants' skills in operating word processing software, as indicated by an increase in post-test scores compared to the pre-test. In addition, this training also resulted in significant social changes, such as increased digital literacy of teachers, the emergence of individuals who act as mentors for colleagues, and the adoption of technology in academic administration. This program proves that a direct practice-based approach is effective in improving the digital skills of Islamic boarding school teachers. For the sustainability of the program, it is recommended that there be further training, a mentoring system between teachers, and technological infrastructure support so that the implementation of digital skills can take place optimally and sustainably.
Prediksi Penjualan Kendaraan Menggunakan Regresi Linear: Studi Kasus pada Industri Otomotif di Indonesia Amansyah, Ilham; Indra, Jamaludin; Nurlaelasari, Euis; Juwita, Ayu Ratna
Innovative: Journal Of Social Science Research Vol. 4 No. 4 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i4.12735

Abstract

Abstrak Industri otomotif Indonesia memiliki tingkat persaingan yang tinggi, sehingga perusahaan mobil seperti Toyota membutuhkan prediksi penjualan yang akurat untuk perencanaan bisnis yang efektif, dan prediksi penjualan yang akurat sangat penting untuk perencanaan bisnis yang efektif. Penelitian ini bertujuan untuk mengaplikasikan algoritma Regresi Linear dalam meramalkan penjualan mobil Toyota di Negara Indonesia. Data penjualan yang digunakan dalam penelitian ini diperoleh dari laporan penjualan mobil Toyota periode 2018 hingga 2023 yang diterbitkan oleh Gabungan Industri Kendaraan Bermotor Indonesia (GAIKINDO). Penelitian ini meliputi beberapa tahapan, mulai dari analisis masalah, pengumpulan data, preprocessing data, penerapan algoritma regresi linier, hingga evaluasi model menggunakan mean absolute error (MAE), mean square error (MSE), square error average (RMSE). dan rata-rata persentase kesalahan absolut (MAPE). Hasil penelitian menunjukkan bahwa model regresi linier dapat memprediksi penjualan mobil Toyota dengan akurasi yang cukup baik, dengan rata-rata kesalahan mutlak (MAE) sebesar 2.617 Unit penjualan dan rata-rata persentase kesalahan absolut (MAPE) sebesar 12,47% yang menunjukkan tingkat yang baik dalam akurasi ramalan. Nilai MAE, MSE, RMSE, Mape yang rendah menunjukkan bahwa model ini efektif dalam meramalkan penjualan di masa depan. Prediksi penjualan mobil Toyota untuk beberapa bulan ke depan menunjukkan hasil yang mendekati nilai aktual, sehingga model ini dapat diandalkan untuk perencanaan bisnis yang lebih baik. Kata Kunci: Algoritma Regresi Linear, Prediksi Penjualan, Industri Otomotif, Data Mining, Tren Penjualan
Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna Azzahra, Fathimah Noer; Rohana, Tatang; Rahmat, Rahmat; Juwita, Ayu Ratna
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5070

Abstract

One of the negative impacts of current digital advances is the increasing number of SMS spam. Spam SMS poses a security risk to users because they can contain malicious links or requests for personal information that are used for malware, smishing, or fraud attacks. However, with the various protection measures available, not all spam SMS can be classified and prevented effectively. However, this problem can be minimized by creating an anti-spam SMS model which aims to classify SMS types. So this research aims to classify types of SMS that contain spam and spam by applying the Naïve Bayes algorithm. In this study, the dataset consisted of 5572 records consisting of 2 categories, namely spam and ham. This algorithm is able to show satisfactory performance in differentiating spam and spam messages because, according to the diversity of literature, the Naïve Bayes algorithm is suitable for use in English language datasets. The evaluation model displays good results with accuracy reaching 93.2%, precision 93.7%, recall 93.2%, and F1-score 91.6%. In addition, analysis in the research using the Receiver Operating Characteristic (ROC) curve shows an accuracy rate of 97.3%, indicating that the model has very good performance in classifying spam in SMS messages. However, there is still room for improvement through the use of new methods and larger and more diverse data sets. This research has an important involvement in working on communication security and user experience in using short message services.
Analisis Sentimen Pemboikotan Produk dengan Pendekatan Algoritma Naïve Bayes Media Sosial X Rifaldi, Rizky; Indra, Jamaludin; Pratama, Adi Rizky; Juwita, Ayu Ratna
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5420

Abstract

This research aims to analyze sentiment regarding the problem of product boycotting by the public using the Naive Bayes algorithm. 1426 data were collected from social media x to study consumer behavior towards certain products. Through the application of the Naive Bayes algorithm, sentiment analysis was carried out to identify patterns in consumer opinions regarding boycotting the products studied. Experimental results show that the Naive Bayes algorithm succeeded in achieving 81% accuracy in classifying sentiment towards products. This shows the algorithm's ability to analyze consumer sentiment effectively, which can provide valuable insights for companies in understanding public perception and managing the reputation of their products. The practical implication of this research is the importance of utilizing sentiment analysis techniques in marketing strategy and brand management to increase product competitiveness in a competitive market.
Penerapan Metode Regresi Logistik Untuk Memprediksi Peristiwa Biner Pasien Pasca Operasi Kanker Payudara Sujana, Sylvia; Juwita, Ayu Ratna; Rahmat, Rahmat; Faisal, Sutan
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5521

Abstract

Breast cancer is the second leading cause of death in women worldwide. To overcome this growing problem, this study designed a model that can predict breast cancer by utilizing datasets and then processed using the Logistic Regression Prediction method. This method is appropriate for predicting the data used because of its ability to handle dependent variables that are categorical and provide outups in the form of probabilities. This study uses a dataset of 306 samples with 4 attributes. Data used Research steps include data collection, preprocessing, modeling with logistic regression and evaluating results using matrices such as confusion matrix, MAE, MSE, and R-Square. The results showed a prediction accuracy of 86%, with an MSE value of 0.137 and R-Square of 0.309. This study shows the effectiveness of logistic regression in predicting the survival of patients after breast cancer surgery. However, by applying different algorithms, this study can select the best set of significant attributes to increase the prediction accuracy value in postoperative breast cancer patients.
Co-Authors AA Sudharmawan, AA Adi Rizky Pratama Adi Rizky Pratama Adi Rizky Pratma Agustina Mardeka Raya Ahmad Fauzi Alganiu, Ajeng Shalwa Amansyah, Ilham Amril Mutoi Siregar Ardiansyah, Fikri ARIF, SITI NOVIANTI NURAINI Awal, Elsa Elvira Azzahra, Fathimah Noer Baharuddin Risyad Carudin, Carudin Cici Emilia Sukmawati Cici Emilia Sukmawati Dina Wulan Nurjanah Edo Ridho Lidinillah Faisal, Muhamad Agus Faisal, Sutan Fauzi, Farras Ahmad Fitri Nur Masruriyah, Anis Fransiskus Panca Juniawan Hanny Hikmayanti Handayani Heryana, Nono Indra, Jamaludin Irawan, Muhamad Anggi Khairani, Nova Pustita Kukuh Ardy Nugroho Lestari, Santi Arum Puspita Lusiana Rahmatiani Mayasari, Rini Mudzakir, Tohirin Al Mulyana, Assyifa Alif Rahayu Novalia, Elfina Nugraha, Bagja Nugraha, Najmi Cahaya Nurlaelasari, Euis Nurmayanti, Trisya nurul latifah Permana, Tedi Pratama, Adi Rizky Pratama, Adi Rizky Purwani Husodo Rahmat Rahmat Rahmat Sulaiman Rahmat Sulaiman Rifaldi, Rizky Riyandi Aditya Fitrah Rizki Mohamad Eka Marsa Sadjat rizky pratama, adi Rohana, Tatang Saefulloh, Nandang Siregar, Amril Mutoi Siti Silvia Arifin Sugihartono, Tri Sujana, Sylvia Sukmawati, Cici Emilia Tatang Rohana Tejayanda, Rigger Damaiarta Tohirin Al Mudzakir Tohirin Al Mudzakir Tohirn Al Mudzakir Triono Triono Triono Triono Wahiddin, Deden Wenda Adi Kusnaya Yaman, Nuurul Izzati Yana Cahyana Yudi Firmansyah