Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Digital Business GLOBAL: Jurnal Lentera BITEP Review: Journal of Multidisciplinary in Social Sciences
Kusumarini, Cahyawati Diah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Analysis of the Content of Tiktok Comments @Dictionarei in A DAY WITH REI PART 350/ SWISS DAY Kusumarini, Cahyawati Diah; Kuncoroyakti, Yohanes Arie; Rahella, Lucky Khairulisa
Review: Journal of Multidisciplinary in Social Sciences Vol. 1 No. 08 (2024): July 2024
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v1i08.450

Abstract

This research uses a qualitative content analysis method with a constructivistic paradigm, with new media theory as its basis. The data collected for this study were 80 randomly selected relevant comments on Tiktok social media regarding their opinions on the video post "A Day With Rei Part 350/Swiss Day". The purpose of this research is to find out the content of the comments. This research found one major theme, namely Opposition to the video post 'A Day With Rei Part 350/Swiss Day'. This theme is the content of several categories that appear in the data that has been analyzed, namely Positive response, Criticism of the creator, Factual question, Personal opinion, Hope and Negative response.
Comparative Analysis of Steam and Epic Games Store Application Satisfaction Levels Using the Pieces Method Kusumarini, Cahyawati Diah; Ansi, Caesario Dwinathaly
Review: Journal of Multidisciplinary in Social Sciences Vol. 2 No. 01 (2025): January 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v2i01.742

Abstract

Application-based game stores are on the rise recently, so there is a tight gap between Steam and the Epic Games Store. This research provided a description of user satisfaction level towards the two applications and served as a measure of the success of these two applications. The type of research conducted was quantitative research that analyzes data collection from a Likert scale questionnaire instrument. The method used was a descriptive analysis of user satisfaction level on both applications using the PIECES method. This research showed that both applications get satisfaction level results of "Satisfied" category in all variables of the PIECES method. Based on each variable value, the Epic Games Store application was superior to the Steam application. Epic Games Store exceled in four variables, namely Information, Economics, Efficiency, and Service, while the Steam application exceled only in Control & Security variable. Performance variables in both applications got the same value.
Creation of an Android-Based AM Futsal Field Rental Application Using Flutter and JS Node with Midtrans Integration as a Payment Gateway Kusumarini, Cahyawati Diah; Avito, Rio Farhan
GLOBAL: Jurnal Lentera BITEP Vol. 3 No. 01 (2025): Februari 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/global.v3i01.745

Abstract

In the modern era, technology has facilitated various aspects of life, including in the sport of futsal, which often requires an optimal training ground. Currently, Futsal AM South Tangerang still uses manual methods for field bookings, resulting in inaccurate reports and limited access. To overcome this problem, this research aims to develop an Android-based futsal field rental application using Flutter and Node.js with Midtrans integration as a payment gateway. The research method used is SDLC (Software Development Life Cycle) with Waterfall model, including planning, analysis, design, implementation, and testing. The results show that the developed application improves the efficiency of the rental process, simplifies data management, and provides a better user experience through integrated features such as login, registration, and payment. Black box testing ensures that the application runs well and is reliable in handling various usage scenarios. This application is expected to simplify the futsal field rental process and overcome the obstacles faced by users and admins.
Eksplorasi Sentimen Aplikasi BCA Mobile Pada Google Playstore Menggunakan Metode Bidirectional Encoder Representations from Transformers (BERT) Leihitu, Elisheva Immanuela; Kusumarini, Cahyawati Diah
GLOBAL: Jurnal Lentera BITEP Vol. 3 No. 03 (2025): Juni 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/global.v3i03.927

Abstract

Penelitian ini bertujuan untuk menganalisis sentiment pengguna terhadap aplikasi BCA Mobile berdasarkan ulasan di Google Play Store. Dengan memahami persepsi pengguna, penelitian ini diharapkan dapat membantu pengembang aplikasi dalam meningkatkan kualitas layanan. Sebanyak 5000 ulasan dikumpulkan melalui proses scraping dan dianalisis menggunakan metode BERT, khususnya model IndoBERT-base. Dataset melalui tahap preprocessing, kemudian dibagi menjadi data pelatihan, validasi, dan pengujian. Model dilatih menggunakan parameter batch size 32, learning rate 2e-5 dan 5 epoch. Hasil penelitian menunjukkan bahwa model berhasil mengenali sentimen positif degan baik (F1-score 0.82 pada data testing), tetapi kesulitan dalam mengenali sentiment netral (F1-score 0.23). akurasi keseluruhan mencapai 73% pada data testing, dengan indikasi overfitting setelah epoch, ke-3, di mana akurasi validasi stagnan meskipun akurasi pelatihan meningkat. Penelitian ini memberikan wawasan tentang persepsi pengguna terhdap aplikasi BCA Mobile dan menyoroti pentingnya distribusi data yang seimbang untuk meningkatkan kemampuan model dalam mengenali semua kategori sentimen. Selain itu, disarankan untuk menambah jumlah data dan mengeksplorasi model dengan kapasitas yang lebih besar untuk meningkatkan hasil analisis, sehingga hasil temuan ini dapat dijadikan sebagai dasar pengambilan keputusan dalam strategi pengembangan fitur dan layanan aplikasi perbankan digital secara lebih responsif terhadap kebutuhan pengguna.
Implementasi Chatbot Telegram untuk Pencarian Informasi Musik Menggunakan Spotify Web API dan Python Ramadhan, Muhammad Faiz; Kusumarini, Cahyawati Diah
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3877

Abstract

Kelimpahan pilihan pada platform streaming musik modern sering kali menimbulkan information overload dan beban kognitif (cognitive load) yang tinggi, sehingga menyulitkan pengguna dalam menemukan musik baru. Penelitian ini bertujuan untuk merancang dan mengimplementasikan chatbot Telegram sebagai antarmuka percakapan yang efisien untuk menyederhanakan proses pencarian informasi musik dari Spotify Web API. Metode pengembangan sistem yang digunakan adalah model Waterfall, dengan chatbot yang dibangun menggunakan bahasa pemrograman Python dan pustaka python-telegram-bot. Alur percakapan dikelola secara terstruktur menggunakan model Finite State Machine (FSM) yang diimplementasikan melalui ConversationHandler. Pengujian fungsionalitas sistem dilakukan dengan metode Black-Box Testing dan evaluasi pengalaman pengguna diukur menggunakan System Usability Scale (SUS). Hasil penelitian menunjukkan bahwa chatbot berhasil lulus 100% dari seluruh skenario pengujian fungsional. Selain itu, hasil pengujian kebergunaan terhadap 29 responden menghasilkan skor rata-rata SUS sebesar 84.83, yang masuk dalam kategori "Excellent" (A+). Hasil ini menegaskan bahwa integrasi FSM dengan API musik mampu memberikan respons yang konsisten, prediktif, dan minim kesalahan, sehingga mengurangi beban kognitif pengguna dalam proses penemuan musik. Responden menyatakan bahwa alur interaksi yang jelas, kecepatan respons, dan relevansi hasil pencarian memberikan pengalaman navigasi yang lebih intuitif dibandingkan antarmuka aplikasi streaming tradisional. Kesimpulannya, implementasi chatbot dengan arsitektur FSM terbukti menjadi solusi yang sangat efektif dan mudah digunakan untuk mengatasi permasalahan information overload dalam penemuan musik, serta memberikan pengalaman pengguna yang superior.
Co-Authors Ansi, Caesario Dwinathaly Avito, Rio Farhan Kuncoroyakti, Yohanes Arie Leihitu, Elisheva Immanuela Rahella, Lucky Khairulisa Ramadhan, Muhammad Faiz