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Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data Ika Agustyaningrum, Cucu; Ramdhani, Yudi; Purnama Alamsyah, Doni; B. Hariyanto, Oda I.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3686-3694

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.

ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

Strengthening Digital and English Skills for Work Readiness in RW 08 Kalideres West Jakarta Rizky Mirani Desi Pratama; Dwi Puji Hastuti; Cucu Ika Agustyaningrum
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 1 (2026): Januari 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i1.4190

Abstract: This community service program was carried out by Universitas Bina Sarana Informatika (UBSI) with the aim of strengthening digital skills and English proficiency among high school/vocational school graduates in order to improve their job readiness. The activity took place at Balai RW 08, Kalideres, and West Jakarta. This program was motivated by the condition of high school/vocational graduates in RW 08, most of whom come from lower-middle-income families and are therefore more inclined to enter the workforce directly rather than pursue higher education. Thus, training is needed to enhance practical skills relevant to industry requirements.The training focused on three main aspects, developing a professional curriculum vitae (CV), strategies for facing job interviews in both Indonesian and English, and practicing digital skills through the use of Canva for CV creation. Participants were actively engaged through interactive sessions, simulations, and collaborative exercises. The results of the program indicated increased awareness, confidence, and skills among participants in preparing themselves to enter the workforce. The conclusion of this program is that integrating digital literacy and English proficiency in community-based training can enhance the competitiveness of high school/vocational school graduates and make a tangible contribution to supporting their readiness for the job market. Keywords: english proficiency, job readiness, digital skills, community service, vocational education Abstrak: Program pengabdian kepada masyarakat ini dilaksanakan oleh Universitas Bina Sarana Informatika (UBSI) dengan tujuan memperkuat keterampilan digital dan kemampuan berbahasa Inggris lulusan SMA/SMK untuk meningkatkan kesiapan kerja. Kegiatan ini berlangsung di Balai RW 08, Kalideres, Jakarta Barat. Program ini dilatarbelakangi oleh kondisi lulusan SMA/SMK di RW 08 yang sebagian besar berasal dari keluarga ekonomi menengah ke bawah, sehingga cenderung lebih memilih langsung bekerja daripada melanjutkan pendidikan ke jenjang yang lebih tinggi. Oleh karena itu, diperlukan pelatihan yang dapat meningkatkan keterampilan praktis sesuai kebutuhan industri.Pelatihan difokuskan pada tiga aspek utama: penyusunan curriculum vitae (CV) profesional, strategi menghadapi wawancara kerja dalam bahasa Indonesia maupun bahasa Inggris, serta keterampilan digital melalui penggunaan Canva untuk pembuatan CV. Peserta terlibat secara aktif melalui sesi interaktif, simulasi, dan latihan kolaboratif. Hasil kegiatan menunjukkan adanya peningkatan kesadaran, kepercayaan diri, dan keterampilan peserta dalam mempersiapkan diri menghadapi dunia kerja. Kesimpulannya, integrasi literasi digital dan kemampuan bahasa Inggris dalam pelatihan berbasis masyarakat dapat meningkatkan daya saing lulusan SMA/SMK serta memberikan kontribusi nyata dalam mendukung kesiapan mereka menghadapi pasar kerja. Kata kunci: kemampuan bahasa Inggris, kesiapan kerja, keterampilan digital, pengabdian masyarakat, pendidikan vokasi

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