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All Journal SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Sistem Informasi Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta SPEED - Sentra Penelitian Engineering dan Edukasi Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pariwisata, Bisnis Digital dan Manajemen Journal Medical Informatics Technology International Journal of Computer Technology and Science
Daniati Uki Eka Saputri
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Jurnal Pilar Nusa Mandiri

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TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING Masturoh, Siti; Nugraha, Fitra Septia; Nurlela, Siti; Saelan, M. Rangga Ramadhan; Saputri, Daniati Uki Eka; Nurfalah, Ridan
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2168

Abstract

Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques. Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values.
APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT Saputri, Daniati Uki Eka; Aziz, Faruq; Khasanah, Nurul; Hidayat, Taopik; Septian, Rendi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5532

Abstract

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.
Co-Authors Abdul Latif Achmad Baroqah Pohan Ade Suryadi Agus Salim Annida Purnamawati Aziz, Faruq Cahyanti, F. Lia Dwi Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Faruq Aziz Faruq Aziz Firasari, Elly Fitra Septia Nugraha Gata, Windu Hasanah, Riyan Latifahul Hidayat, Taopik Juninisvianty, Tri Kekal, Harming Puja Khultsum, Umi Lili Novita Monikka Nur Winnarto Nugraha, Fitra Septia Nurfalah, Ridan Nurul Khasanah Nurul Khasanah Nurul Khasanah, Nurul Pratiwi, Risca Lusiana Rangga Pebrianto Risca Lusiana Pratiwi - Universitas Nusa Mandiri Rosiyadi, Didi Saelan, M. Rangga Ramadhan Salsabila, Unik Hanifah Saputri, Elly Sardiarinto Sardiarinto Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Solly Aryza Sri Hadianti Taopik Hidayat Umi Khultsum Veithzal Rivai Zainal YANTO YANTO