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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 JISA (Jurnal Informatika dan Sains) 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.
Air Quality Classification Using Naive Bayes Algorithm With SMOTE Technique Based on ISPU Data Fadhilah, Alya Febriyanti; Juwita, Ayu Ratna; Wicaksana, Yusuf Eka; Mudzakir, Tohirin Al
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2181

Abstract

Air pollution in DKI Jakarta is an important issue and has a negative impact on public health. This study applies the naive Bayes algorithm to classify air quality, Utilizing the SMOTE technique effectively addresses the issue of data imbalance. The data analyzed came from air pollution index data from 2022 to 2024, taken from five air monitoring stations in Jakarta. The analysis process was carried out following the CRISP-DM stages, starting from understanding the problem to evaluating the model. The results showed that SMOTE succeeded in increasing prediction accuracy in fewer classes. Without SMOTE, the model accuracy reached 90% but appeared biased towards fewer classes, with a recall value of only 0.75 and a precision of 0.62. While SMOTE, the model accuracy became 88%, with a precision value of 0.86, recall 0.87, and f1-score 0.87, which showed more balanced results across classes.
Klasifikasi Penerima Bantuan Rumah Tidak Layak Huni Desa Labansari Menggunakan Algoritma C4.5 Ahmad Zaelani; Ayu Ratna Juwita; Tohirin Al Mudzakir
Scientific Student Journal for Information, Technology and Science Vol. 7 No. 1 (2026): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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

Abstract

Program bantuan rumah tidak layak huni merupakan program bantuan sosial untuk meringankan keluarga berpenghasilan rendah dalam membangun rumah yang layak huni. Data calon penerima bantuan rumah tidak layak huni yang digunakan pada penelitian ini berjumlah 111 data. Ketidakakuratan penyaluran bantuan rumah tidak layak huni terjadi karena tidak adanya metode dalam menentukan penerima bantuan tersebut. Penyaluran bantuan yang tidak tepat sasaran akan berdampak pada pembangunan rumah yang tidak selesai. Oleh karena itu, untuk memperkecil kesalahan dalam pengambilan keputusan, data diklasifikasikan menggunakan algoritma C4.5. Perhitungan algoritma C4.5 menghasilkan nilai akurasi, presisi, recall, serta pohon keputusan dari data yang diolah. Pengujian yang dilakukan menggunakan Microsoft Excel menghasilkan akurasi 100%, presisi 100%, dan recall 100%. Sementara itu, pengujian menggunakan bahasa pemrograman Python juga memperoleh hasil akurasi 100%, presisi 100%, dan recall 100%.
Identifikasi Citra Penggunaan Masker Secara Real-Time dengan Arsitektur CNN pada Metode YOLO Aldo Zamaludin Fernando; Adi Rizky Pratama; Ayu Ratna Juwita
Scientific Student Journal for Information, Technology and Science Vol. 7 No. 1 (2026): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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

Abstract

Pada masa pandemi, masih banyak masyarakat yang kurang tertib dalam mematuhi protokol kesehatan, terutama dalam penggunaan masker, baik pada aktivitas di dalam ruangan maupun di luar ruangan. Oleh karena itu, diperlukan sebuah sistem yang dapat mendeteksi atau mengidentifikasi objek berupa penggunaan masker. Pada penelitian ini digunakan metode YOLO yang memiliki arsitektur dari algoritma Convolutional Neural Network (CNN), dengan memanfaatkan Darknet-53 untuk melatih (training) model pada metode YOLO agar dapat mendeteksi objek secara real-time. Dalam proses identifikasi objek penggunaan masker menggunakan metode YOLO, sistem dapat berjalan dengan baik dan mampu mendeteksi objek dengan tingkat akurasi yang cukup baik. Hasil pengujian menunjukkan nilai precision sebesar 74%, nilai recall sebesar 50%, dan tingkat akurasi sebesar 74%. Dengan demikian, sistem dapat dikatakan berhasil dalam mengidentifikasi penggunaan masker menggunakan metode YOLO dengan tingkat keakuratan yang cukup baik.
Perbandingan Algoritma K-Means dan DBSCAN Menggunakan Data Kunjungan Wisatawan Asing ke Indonesia di Masa Pandemi COVID-19 Muhamad Irfan Fadillah; Ayu Ratna Juwita; Cici Emilia Sukmawati
Scientific Student Journal for Information, Technology and Science Vol. 7 No. 1 (2026): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Pandemi Covid-19 berskala global yang telah terjadi selama beberapa tahun terakhir berpengaruh secara signifikan terhadap berbagai sektor. Pariwisata menjadi sektor yang paling terdampak oleh pandemi Covid-19 karena upaya menahan peningkatan jumlah orang yang terinfeksi dilakukan dengan membatasi pergerakan manusia, sehingga wisatawan asing tidak dapat leluasa bepergian ke luar negeri. Data yang tersedia di Badan Pusat Statistik (BPS) mengenai kunjungan wisatawan mancanegara ke Indonesia pada masa pandemi masih berupa data mentah dan belum diolah menggunakan bahasa pemrograman maupun melalui berbagai kajian ilmiah. Subjek penelitian ini berfokus pada permasalahan data pariwisata mancanegara tersebut. Berdasarkan rangkuman yang dilakukan dalam penelitian ini, penerapan algoritma K-Means dan DBSCAN memerlukan beberapa tahapan dalam pengolahan data pariwisata mancanegara, yaitu pengumpulan data, seleksi data, serta implementasi algoritma K-Means dan algoritma DBSCAN. Setelah hasil pengelompokan diperoleh, dilakukan evaluasi menggunakan metode Silhouette Coefficient. Hasil perhitungan evaluasi menunjukkan bahwa algoritma DBSCAN menghasilkan nilai Silhouette Coefficient sebesar 0,91962, yang lebih rendah dibandingkan dengan algoritma K-Means yang memiliki nilai sebesar 0,96234. Dengan demikian, dapat disimpulkan bahwa pada penelitian ini algoritma K-Means memiliki kinerja yang lebih baik dibandingkan algoritma DBSCAN dalam pengelompokan dataset kunjungan wisatawan mancanegara yang digunakan.
Co-Authors AA Sudharmawan, AA Adi Rizky Pratama Adi Rizky Pratama Adi Rizky Pratma Agustina Mardeka Raya Ahmad Fauzi ahmad zaelani Aldo Zamaludin Fernando 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 Diah Nurlaila Dina Wulan Nurjanah Edo Ridho Lidinillah Elsa Elvira Awal Fadhilah, Alya Febriyanti 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 Muhamad Irfan Fadillah Muhammad Arya Suhendi 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 Tatang Rohana teguh budianto Tejayanda, Rigger Damaiarta Tohirin Al Mudzakir Tohirin Al Mudzakir Tohirn Al Mudzakir Triono Triono Triono Triono Wahiddin, Deden Wenda Adi Kusnaya Wicaksana, Yusuf Eka Yaman, Nuurul Izzati Yana Cahyana Yudi Firmansyah