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All Journal International Journal of Artificial Intelligence Innovative: Journal Of Social Science Research
Susanto, Jessika Kindly
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice Public Health Other

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Selecting Favourite Majors at Sari Mulia University Using SAW Method Rusidah; Risdianti; Susanto, Jessika Kindly
International Journal of Artificial Intelligence Vol 10 No 1: June 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01001.482

Abstract

A decision support system, also known as a decision support system (DSS), is an interactive information system that offers data, models, and information. DSS is used as a decision aid in semi-structured and unstructured situations where there is no clear decision-making procedure. Determining the preferred major is one of the challenges in universities. The purpose of determining the most popular major is to improve the quality and services provided to students in each department, which is a crucial objective for universities. Currently, Universitas Sari Mulia determines the most popular majors based on qualitative data, which makes the determination of the most popular majors themselves inaccurate; therefore, a method capable of managing data on the selection of the most popular majors is necessary. In this study, the Simple Additive Weighting (SAW) technique will be utilized. This method is used to compare each criterion with one another in order to determine the most popular majors at Sari Mulia University and to evaluate each department.
Movie Genre Product Convolutional Neural Network Impact For Gen Z Susanto, Jessika Kindly; Nugraha, Bayu; Hidayat, Ahmad; Prastya, Septyan Eka
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (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.v4i6.16848

Abstract

Latar Belakang: Dalam beberapa tahun terakhir, perkembangan teknologi kecerdasan buatan dan pembelajaran mendalam telah memberikan dampak signifikan pada berbagai bidang, termasuk industri perfilman. Salah satu tantangan dalam industri ini adalah mengklasifikasikan genre film secara otomatis berdasarkan konten visualnya. Apalagi penonton film lebih banyak gen z yang menggunakannya. Tujuan: Penelitian ini bertujuan untuk mengetahui preferensi film kepada gen z dikota Banjarmasin Metode: Menggunakan metode convolutional neural network (CNN). Hasil: Hasil penelitian menunjukkan bahwa preferensi genre film gen z lebih banyak yang paling banyak minat yaitu genre comedy dengan 32,1% (18 responden gen z yang memilih dari 53 responden), kemudian disusul dengan genre action 32% (17 responden gen z yang memilih dari 53 responden), dan genre horor 18,9% (10 responden gen z yang memilih dari 53 responden). Dan yang terakhir genre romance 17% (8 responden gen z yang memilih dari 53 responden). Simpulan: Dengan sistem google form 53 data responden dari hasil mengambil sampel 12 poster film dari ratting film tertinggi, dan pada CNN dapat terpredict melalui salah satu poster film tersebut . Sistem preferensi film ini memudahkan gen z dalam memilih film dengan ratting usia dan ratting film tersebut. Penelitian ini memberikan kontribusi dalam memahami penerapan teknologi informasi untuk meningkatkan layanan industri perfilman.
Co-Authors Ahmad Hidayat Bayu Nugraha Prastya, Septyan Eka Risdianti Rusidah