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All Journal JUSIFO : Jurnal Sistem Informasi Briliant: Jurnal Riset dan Konseptual Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia CONSEN: Indonesian Journal of Community Services and Engagement
Muttakin, Fitriani
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Implementasi Metode WASPAS Pada Sistem Pendukung Keputusan Penilaian Kinerja Perawat Terbaik Pratama, Arya Yendri; Muttakin, Fitriani; Permana, Inggih; Zarnelly, Zarnelly; Marsal, Arif
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.v5i3.5068

Abstract

Hospitals are health service institutions that provide various services to the community, including inpatient, outpatient and emergency care. Hospitals as health service institutions require optimal nurse performance in providing quality services to patients. At XYZ Hospital, assessments and calculations are still carried out manually, often experiencing difficulties because in carrying out the assessment all the calculation data is carried out one by one, resulting in large errors and taking quite a long time to obtain the decision results and it is difficult to carry out rankings due to frequent assessment forms. scattered. There are 14 criteria for assessing nurse performance, namely loyalty/loyalty, work performance, responsibility, obedience/discipline, honesty, cooperation, communication, knowledge, competency I nurse (PK I), competency II nurse (PK II), competency III nurse (PK III), the presence of hand washing in the room, the quality of the work carried out by the person concerned, and the availability of ready-to-use facilities & infrastructure for the next shift. To obtain accurate performance assessment results, a decision support system was created using the WASPAS method. The WASPAS method is said to be appropriate for selecting the best nurses because it is ranked based on specified criteria values. It is hoped that the research carried out will help obtain effective results. In this research, the results obtained were that the best nurse at XYZ Hospital was the alternative with a score of 50,038 in the name of EET.
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Optimizing Qur'an Tahfiz Learning through Artificial Intelligence Training to Supporting the SDGs of Quality Education: Optimalisasi Pembelajaran Tahfiz Al-Qur’an melalui Pelatihan Artificial Intelligence dalam Mendukung SDGs Pendidikan Berkualitas Muttakin, Fitriani; Monalisa, Siti; Abdilllah, Abdilllah; Amillia, Fitri; Mulyanto, Mulyanto
CONSEN: Indonesian Journal of Community Services and Engagement Vol. 6 No. 1 (2026): Consen: Indonesian Journal of Community Services and Engagement
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/consen.v6i1.2740

Abstract

Tahfiz Al-Qur'an learning in nonformal institutions still faces obstacles in evaluating recitation and providing intensive memorization assistance. This community service activity aims to improve the understanding and skills of students and teachers in utilizing the Tarteel AI application as a supporting medium for tahfiz learning based on Artificial Intelligence (AI). The activity was carried out at PKBM Tahfiz Attamam Pekanbaru involving 95 participants consisting of students and teaching staff. The activity methods included socialization, training, hands-on practice, and evaluation using Likert scale-based pre-tests and post-tests. The evaluation results showed that 87% of participants understood the basic concepts of AI after the training, while 94% of participants demonstrated interest and readiness to utilize the Tarteel AI application as a supporting medium for tahfiz learning. In addition, teachers assessed that the application could help monitor students' memorization more systematically. This activity shows that the utilization of AI through the Tarteel AI application has the potential to support more adaptive tahfiz learning and contribute to the implementation of SDG 4 on Quality Education.
Co-Authors Abdilllah, Abdilllah Andrika, Nadila Angraini Angraini Anjani, Yulia Merry Ardiansah, Yoga Ariansah, Rizky Arif Marsal Auliadi, Maulana Dermawan, Tri Eki Saputra Febi Nur Salisah Fitri Amillia Hamzah, Muhammad Luthfi Inggih Permana Intan, Sofia Fulvi Jazman, Muhammad M. Afdal Mona Fronita, Mona Mulyanto Mulyanto Nabillah, Putri Nur Sarifudin, Muhammad Pratama, Arya Yendri Pristiawati, Andani Putri Putri, Amanda Iksanul Rachmawati, Dyana Rahmawati, Rahmawati Rahmawita Munzir, Medyantiwi Sabillah, Dian Ayu Safrudin, Muhammad Salsabila, Salsabila Sarbaini Sarbaini Siregar, Muslim Putra Perdana Siti Monalisa Syahri, Alfi Tasia, Ena Tengku Khairil Ahsyar Tshamaroh, Muthia Zarnelly Zarnelly