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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology
Widiastuti Widiastuti
Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
Computer Science & IT

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DECISION SUPPORT SYSTEM FOR HELP RECIPIENTS HOPE FAMILY PROGRAM ON VILLAGE WARU WITH SAW METHOD Siti Nurlela; Tuti Kurniawati; Siti Masturoh; Widiastuti Widiastuti; Ade Suryadi
Jurnal Techno Nusa Mandiri Vol 17 No 2 (2020): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v17i2.1678

Abstract

Waru Village is one of the villages in the Bogor Regency area. However, the selection of recipients of the Hope Family Program (PKH) in Waru Village is still subjective/qualitative so that the process of receiving the Hope Family Program (PKH) in Waru Village is not accurate and has not been on target. This makes the need for a method that can manage data on recipients of the Hope Family Program (PKH) and produce a ranking from the calculation of weight for the selection of recipients of the Hope Family Program (PKH). In making decisions about recipients of the Hope Family Program (PKH), there is a Simple Additive Weighting (SAW) method that can be used in quantitative problem-solving. With the SAW method, each criterion is compared with one another to provide results of recipients of the Hope Family Program (PKH) and provide an assessment of each recipient (alternative) of the Hope Family Program (PKH) in Waru Village. This study aims to determine the recipients of the PKH assistance program so that it can produce a decision on recipients of PKH assistance that the government distributes to the Waru village accurately on target.
PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM Nugroho Adi Putro; Rendi Septian; Widiastuti Widiastuti; Mawadatul Maulidah; Hilman Ferdinandus Pardede
Jurnal Techno Nusa Mandiri Vol 18 No 1 (2021): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v18i1.2056

Abstract

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes
Co-Authors Ade Suryadi Hilman Ferdinandus Pardede Mawadatul Maulidah Nugroho Adi Putro Rendi Septian Siti Masturoh Siti Nurlela Tuti Kurniawati