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Khaira, Ulfa
Universitas Jambi
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Analisis Penerimaan Pengguna Aplikasi Canva di Provinsi Jambi Menggunakan Technology Acceptance Model (TAM) Insyra Inayah Putri; Anna Febriane Angelica; Elfira Elfira; Nyimas Azzahra Nur Saiyidah Nafisah; Taufik Hidayat; Ulfa Khaira
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9282

Abstract

Abstrak - Penelitian ini bertujuan untuk menganalisis penerimaan pengguna terhadap aplikasi Canva menggunakan pendekatan Technology Acceptance Model (TAM) dengan lima konstruk utama: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude Toward Using (ATU), Behavioral Intention to Use (BIU), dan Actual Use (AU). Uji instrumen dilakukan melalui pilot test terhadap 30 responden, yang menunjukkan seluruh konstruk memenuhi validitas konvergen dan reliabilitas meskipun terdapat indikator yang kurang optimal. Beberapa konstruk tidak lulus uji validitas diskriminan berdasarkan Fornell-Larcker, namun tetap dinyatakan valid berdasarkan cross loading. Pengumpulan data utama dilakukan terhadap 220 responden dan dianalisis menggunakan metode PLS-SEM dengan bantuan software SmartPLS 4.0. Hasil outer model menunjukkan semua indikator valid dan reliabel. Seluruh hubungan dalam inner model signifikan secara statistik (t 1,96; p 0,05), dengan PU memiliki pengaruh terbesar terhadap ATU (β = 0,545) dan BIU (β = 0,326), serta BIU terhadap AU (β = 0,564). Nilai R² menunjukkan tingkat prediksi sedang hingga kuat, dan Q² menegaskan kemampuan prediktif model. Temuan ini memperlihatkan bahwa model TAM mampu menjelaskan perilaku penerimaan Canva dengan baik dalam konteks pendidikan digital.Kata kunci: Canva; Technology Acceptance Model; SmartPLS; Outer Model; Inner Model; Abstract - This study aims to analyze user acceptance of the Canva application using the Technology Acceptance Model (TAM) approach with five main constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude Toward Using (ATU), Behavioral Intention to Use (BIU), and Actual Use (AU). Instrument testing was conducted through a pilot test involving 30 respondents, which showed that all constructs met convergent validity and reliability, although some indicators were less than optimal. Some constructs did not pass the discriminant validity test based on the Fornell-Larcker criterion but were still considered valid based on cross-loading results.The main data collection involved 220 respondents and was analyzed using the PLS-SEM method with the assistance of SmartPLS 4.0 software. The outer model results indicated that all indicators were valid and reliable. All relationships in the inner model were statistically significant (t 1.96; p 0.05), with PU having the greatest influence on ATU (β = 0.545) and BIU (β = 0.326), and BIU on AU (β = 0.564). The R² values indicated moderate to strong predictive power, and the Q² values confirmed the model's predictive capability. These findings demonstrate that the TAM model effectively explains the acceptance behavior of Canva in the context of digital education.Keyword: Canva, Technology Acceptance Mode; SmartPLS; Outer Model; Inner Model;
Penerapan Support Vector Machine dan Latent Dirichlet Allocation dalam Analisis Sentimen Terhadap Pengalaman Pengguna Aplikasi Alfagift Desi Hartati; Ulfa Khaira; Rizqa Raaiqa Bintana
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9866

Abstract

Abstrak − Aplikasi alfagift merupakan salah satu aplikasi belanja online yang banyak diunduh di Google Play Store, menerima ribuan ulasan dari pengguna setiap harinya. Proses meninjau ulasan ini secara manual menjadi tidak efisien karena keterbatasan tenaga dan waktu, sehingga berpotensi memperlambat respons tim dalam mengambil keputusan strategis. Untuk mengoptimalkan proses ini, penerapan model machine learning sangat diperlukan untuk mengotomatisasi analisis sentimen dan identifikasi tema utama dalam ulasan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Alfagift menggunakan metode Support Vector Machine (SVM) dan mengidentifikasi topik utama dalam ulasan dengan pendekatan Latent Dirichlet Allocation (LDA) . Dataset yang digunakan telah melalui proses preprocessing berupa case folding , tokenizing, filtering , dan stemming . Hasil evaluasi menunjukkan bahwa model SVM dengan kernel polynomial memberikan akurasi tertinggi sebesar 95,9%, menandakan kemampuan yang baik dalam membedakan ulasan positif dan negatif. Pendekatan Latent Dirichlet Allocation (LDA) berhasil mengidentifikasi topik dominan, dengan nilai koherensi optimal sebesar 0,3164 (ulasan positif, 8 topik) dan 0,3177 (ulasan negatif, 7 topik). Berdasarkan analisis, mayoritas pengguna menyampaikan ulasan positif terkait kemudahan belanja dan promo menarik, meskipun beberapa masalah teknis masih menjadi keluhan. Penelitian ini menunjukkan bahwa kombinasi SVM dan LDA efektif untuk mengevaluasi kepuasan pengguna serta mengidentifikasi area perbaikan layanan Alfagift.Kata Kunci: Alfagift; Analisis Sentimen; LDA; SVM; Ulasan Pengguna; Abstract − The Alfagift app is one of the most downloaded online shopping apps on the Google Play Store, receiving thousands of user reviews daily. Manually reviewing these reviews is inefficient due to limited resources and time, potentially slowing down the team's ability to make strategic decisions. To optimize this process, implementing machine learning models is essential to automate sentiment analysis and identify key themes within reviews. This research aims to analyze the sentiment of user reviews of the Alfagift application using the Support Vector Machine (SVM) method and to identify the main topics in the reviews using the Latent Dirichlet Allocation (LDA) approach. The dataset used has undergone preprocessing including case folding, tokenization, filtering, and stemming. The evaluation results show that the SVM model with a polynomial kernel provides the highest accuracy of 95.9%, indicating a good ability to differentiate between positive and negative reviews. The Latent Dirichlet Allocation (LDA) approach successfully identified dominant topics, with an optimal coherence value of 0.3164 (positive reviews, 8 topics) and 0.3177 (negative reviews, 7 topics). Based on the analysis, the majority of users provided positive reviews regarding the ease of shopping and attractive promotions, although some technical issues remain a complaint. This study demonstrates that the combination of SVM and LDA is effective for evaluating user satisfaction and identifying areas for service improvement at Alfagift.Keywords: Alfagift; Sentiment Analysis; LDA; SVM; User Reviews;
Co-Authors Akhiyar Waladi Akhmad Fikri Rosyadi Amanda Iza Sofiani Anisa Bella Panjaitan Anna Febriane Angelica Arsa, Daniel Aryani, Reni Benedika Ferdian Hutabarat Bisma Aulia Bisma Aulia Cepi Ramdan Cepi Ramdan Christina Mutiara Ishak Desi Hartati Desi Musfiroh Devi Listiani Safitri Dewi Lestari Dinda Fatimah Sarah Edi Saputra Edi Saputra Eko Prasetyo Utomo, Pradita Elfira Elfira Elsha Aureal Shopia Ghaitsa Althafah Wandira Ghaitsa Althafah Wandira Guntoro Guntoro Guntoro Guntoro Haezrah Oktavini Hasanatul Iftitah Hasby Kuswanto Hidayat Hidayat Imelda Raudati Imelda Raudati Indra Weni Indryani Indryani Insyra Inayah Putri Jefri Marzal Krisman Pratama Simanjuntak Lailan Syaufina Lilian Putri Salsabila Mardhotillah, Bunga Mauladi Mauladi Mauladi Mauladi Muazzomi, Nyimas Muhammad Alfares Muhammad Rumii Firnanditya Muksin Alfalah Mustakim Mustakim Mutia Fadhila Putri Nabila Febrisa Anggraini Nasution, Afdal Aditya Nasution, Mukhtada Billah Nugroho Ardy Pramono Nyimas Azzahra Nur Saiyidah Nafisah Pebrianto, Dony Yusra Pikir Claudia Septiani Gulo Pradita Eko Prasetyo Putri Hazmawati Qodri, Muhammad Amin Ragil Johanda Reza Wahyu Hardian Rio Gilang, Divo Riyan Ardani Rizki Surya Amanda Rizqa Raaiqa Bintana Robertus Herodian Sitanggang Robi Purnomo Samratul Fuady Sitanggang, Imas S. Sulistiyo, Urip Syalsabylla Syalsabylla Syamsyida Rozi Tasia Maidi Saputri Tasia Maidi Saputri Taufik Hidayat Tesa Ananda Putri Tri Suratno Uswatul Hasni Utomo, Pradita Eko Prasetyo Wisard Widsli Kalengkongan Yolla Noverina Yosi Riduas Hais Yosika Dian Saputri Yuanita Clark Lestari Simbolon Yurinanda, Sherli Zainil Abidin