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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Sewagati
Faradisa, Rosiyah
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Arts Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Peningkatan Kompetensi Pembuatan Animasi 2D pada Kru Studio Progresif TV untuk Iklan Pendek di PP Bumi Sholawat Sidoarjo Berbasis Project Based Learning Zikky, Moh; Rante, Hestiasari; Santoso, Tri Budi; Fathoni, Kholid; Sukaridhoto, Sritrusta; Zainuddin, Muhammad Agus; Susanto, Dwi; Sarinastiti, Widi; Anggraeni, Martianda Erste; Dianta, Ashafidz Fauzan; Darmawan, Zakha Maisat Eka; Faradisa, Rosiyah; Aji, Rendra Suprobo
Sewagati Vol 8 No 1 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i1.809

Abstract

Potensi industri kreatif di indonesia khususnya pada periklanan digital memiliki potensi yang sangat besar. Pada tahun 2023, belanja iklan mencapai $ 2,55 milliar. Saat ini industri periklanan lebih bervariasi, salah satunya dengan mengadopsi konsep animasi. Dengan menggunakan animasi, iklan lebih dinamis dan memiliki daya tarik visual. Namun ketersediaan SDM yang memiliki kompetensi di bidang animasi periklanan saat ini sangat terbatas. Politeknik Elektronika Negeri Surabaya, melalui Departemen Teknologi Multimedia Kreatif, Program Studi Teknologi Rekayasa Multimedia melakukan program pengabdian masyarakat berupa pelatihan pembuatan Iklan berbasis animasi 2D pada Progressive TV PP Bumi Sholawat Sidoarjo. Diharapkan dengan pelatihan ini mampu meningkatkan kompetensi SDM talenta digital di Indonesia Khususnya di Sidoarjo.
Evaluasi Faktor-Faktor Pembelajaran Online pada Perguruan Tinggi Menggunakan Analytic Hierarchy Process (AHP): Studi Kasus Politeknik Elektronika Negeri Surabaya (PENS) Faradisa, Rosiyah; Assidiqi, Mohammad Hasbi; Badriah, Tessy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106860

Abstract

Pandemi Covid-19 dan lockdown telah memaksa dunia pendidikan untuk bergerak secara online. Perubahan yang telah terjadi akibat pandemi covid-19 yang telah terjadi sementara sekitar dua tahun di perguruan tinggi, diperkirakan tidak bersifat sementara dan bahkan akan berlanjut. Standar evaluasi pembelajaran tatap muka tradisional tidak dapat diterapkan begitu saja pada pembelajaran online, di perlukan penyesuaian khusus untuk kondisi kelas online. Pada penelitian ini dilakukan pandangan dan pengkajian yang komprehensif terhadap 3 faktor utama dalam penyelenggaraan perkuliahan online di perguruan tinggi meliputi faktor dosen, siswa, dan Learning Management System (LMS). Penelitian dilakukan dengan studi kasus Politeknik Elektronika Negeri Surabaya (PENS) dari sudut pandang dosen dan mahasiswa. Evaluasi faktor-faktor tersebut dilakukan dengan menggunakan AHP untuk pemeringkatan dan mendapatkan nilai kepentingan relatif. Dari perhitungan AHP yang telah dilakukan, diperoleh 10 faktor dengan penilaian tertinggi baik dari responden dosen maupun mahasiswa. Dari 10 faktor tersebut diperoleh 5 faktor yang beririsan antara dosen dan mahasiswa. Kesimpulan yang diperoleh dari penelitian ini dapat digunakan sebagai bahan evaluasi faktor-faktor pembelajaran online di perguruan tinggi pada umumnya, dan PENS khususnya.AbstractThe COVID-19 pandemic and lockdown have forced the world of education to move online. The changes that have taken place as a result of the COVID-19 pandemic, which has occurred for about two years in college, are not expected to be temporary and will even continue. Traditional face-to-face learning evaluation standards cannot simply be applied to online learning; special adjustments are needed for online class conditions. A comprehensive view and assessment of the three main factors in the implementation of online lectures in tertiary institutions, including lecturers, students, and the Learning Management System (LMS), were carried out in this study. The research was conducted using PENS case studies from the perspective of lecturers and students. These factors are ranked and their relative importance values are calculated using AHP. From the AHP calculations that have been carried out, 10 factors with the highest ratings were obtained from both lecturer and student respondents. From these 10 factors, 5 factors were obtained that intersected between lecturers and students. The findings of this study can be used to evaluate the factors of online learning in tertiary institutions in general, and PENS in particular.
Personalized Tourism in Surabaya: A Bayesian Network Approach Faradisa, Rosiyah; Badriyah, Tessy; Maulana, Hanan Ammar; Assidiqi, Moh Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3376

Abstract

This study investigates the application of Bayesian Networks in developing a personalized tourist destination recommendation system focused on Surabaya, Indonesia. The research incorporates push and pulls factors alongside tourist activities as key input variables to model decision-making processes. Two distinct Directed Acyclic Graph (DAG) structures are evaluated: one proposed based on existing theoretical frameworks and another generated from empirical respondent data. The dataset comprises responses from 1,350 tourists visiting twenty-five popular attractions in Surabaya. The analysis reveals that Bayesian Networks effectively identify correlations between various influencing factors. From the tests carried out, the accuracy obtained from the two DAG structures did not significantly differ. The proposed DAG achieved 35% accuracy for the top-ranked destination recommendations, while the data-driven DAG was 25%. Both achieved 75% accuracy in the top five recommendations. The accuracy increased as the number of output states was reduced. Meanwhile, in the test with binary output, BN was able to accurately classify tourist destinations with an average accuracy of 95% for both DAGs. These findings highlight the potential of Bayesian Networks to enhance tourism decision support systems by providing nuanced insights into tourists' preferences and motivations. For further research, hybridization or feature engineering can be employed to improve model accuracy. In addition, determining more appropriate push factors and tourist activities based on the tourism case studies also needs to be done to obtain better tourist preferences. This research highlights the promising role of Bayesian Networks in improving the personalization and effectiveness of tourist recommendations.
Co-Authors Aji, Rendra Suprobo Anggraeni, Martianda Erste Ashafidz Fauzan Dianta, Ashafidz Fauzan Assidiqi, Moh Hasbi Assidiqi, Mohammad Hasbi Badriah, Tessy Darmawan, Zakha Maisat Eka Dwi Susanto Fathoni, Kholid Maulana, Hanan Ammar Rante, Hestiasari Tessy Badriyah, Tessy Tri Budi Santoso Widi Sarinastiti Zainuddin, Muhammad Agus Zikky, Moh