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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Anik Hanifatul Azizah
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Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Analisis Penerimaan Pengguna Terhadap Sistem ERP Pada Fungsi After Sales Menggunakan Model Technology Acceptance Model 2 (TAM2) Yofa Fauzia Azima; Anik Hanifatul Azizah; R Wahjoe Witjaksono
JRSI (Jurnal Rekayasa Sistem dan Industri) Vol 6 No 02 (2019): Jurnal Rekayasa Sistem & Industri - Desember 2019
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v6i02.328

Abstract

PT.Wijaya Toyota Dago adalah sebuah perusahaan yang bergerak di bidang jasa pelayanan, perawatan, perbaikan, dan penyediaan suku cadang resmi Toyota yang bernaung dibawah manajemen PT.Toyota Astra Motor. Saat ini PT. Wijaya Toyota Dago telah menggunakan sebuah sistem informasi yaitu TDMS (Toyota Dealer Management System). TDMS merupakan suatu sistem internal yang dimiliki Toyota untuk mempermudah kegiatan operasional perusahaan dan dalam melayani kebutuhan para pelanggan. TDMS menginduk pada System Application and Product in data processing (SAP), dimana SAP merupakan salah satu software ERP (Enterprise Resource Planning). Namun pengguna sistem masih belum menggunakan sistem secara maksimal. Dan sejak diimplementasikannya sistem TDMS belum pernah dilakukan analisis terhadap penerimaan pengguna. Berdasarkan masalah yang telah dijabarkan, solusi yang diusulkan yaitu melakukan analisi penerimaan pengguna terhadap sistem TDMS menggunakan model TAM2 dari Venkatesh dan Davis. TAM 2 merupakan model penelitian yang digunakan untuk menilai perilaku pengguna dalam menerima dan menggunakan sebuah teknologi informasi. Berdasarkan hasil analisis diketahui bahwa terdapat pengaruh antara kegunaan yang dirasakan pemgguna dan kemudahan penggunaan terhadap minat pengguna yang artinya jika manfaat penggunaan aplikasi terus ditingkatkan, maka juga dapat meningkatkan minat pengguna dalam menerima sistem TDMS.
ANALISIS SENTIMEN OPINI PUBLIK TERHADAP KASUS KORUPSI BAHAN BAKAR MINYAK OPLOSAN PT PERTAMINA DENGAN HYBRID MODEL DEEP LEARNING Muhammad Ramdhan Awali; Sawali Wahyu; Anik Hanifatul Azizah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3525

Abstract

The corruption case related to oplosan fuel oil involving PT Pertamina has become a national issue that has drawn diverse responses from the public. Sentiment analysis of public opinion on social media can provide important insights for the government and stakeholders in understanding public perceptions of the case. This study aims to analyze public opinion sentiment regarding the alleged fuel adulteration corruption case involving PT Pertamina, using a hybrid deep learning model approach. Data were collected from the social media platform Twitter (X) between February 24 and March 19, 2025, resulting in 12,365 tweets after preprocessing. The study implements four model architectures: IndoBERT, CNN, LSTM, and a hybrid IndoBERT-CNN-LSTM model. Evaluation results show that IndoBERT achieved the highest accuracy at 90%, followed by CNN (86%), hybrid (84%), and LSTM with the lowest accuracy (69%). In addition, the K-Fold cross-validation scheme produced more stable model evaluation results than the Hold-Out method. Based on sentiment distribution analysis, public opinion was dominated by negative sentiment at 72%, while positive and neutral sentiments each accounted for 16%. These findings indicate that the public tends to respond negatively to the Pertamina fuel corruption issue. This study contributes to the understanding of public opinion on social media through a deep learning-based sentiment analysis approach and highlights the importance of selecting appropriate model architectures and validation strategies in the task of classifying Indonesian-language text.
Analisis Sentimen Media Sosial X Program Makanan Sehat Gratis dengan Support Vector Machine dan Naive Bayes Jack Vallen; Anik Hanifatul Azizah
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1665

Abstract

The free nutritious meal program, has sparked various reactions from the public, particularly on social media. This study aims to determine how netizens feel about the program through data analysis from social media platform X (formerly Twitter). Data was collected using web scraping techniques with the keyword “free nutritious meals,” then processed through text cleaning and automatic labeling stages using the Indonesian language version of the RoBERTa model. With this approach, each tweet was efficiently and accurately classified into positive or negative sentiment. The labeled data was then analyzed using two classification algorithms: Support Vector Machine (SVM) and Naïve Bayes. Test results showed that SVM performed better, with an average accuracy of 0.8367 and a deviation of 0.0117. Meanwhile, Naïve Bayes recorded an accuracy of 0.7716 with a deviation of 0.0101. Visualization through WordCloud also shows the dominant words in each sentiment. Words such as “support,” “healthy,” and ‘grow’ appear frequently in positive sentiments, while words such as “budget,” “poison,” and “cost” dominate negative sentiments. These findings illustrate significant public support for the program, but also concerns regarding its implementation and funding. The results of this analysis are expected to provide input for policymakers in understanding public opinion more objectively
Co-Authors Jack Vallen Muhammad Ramdhan Awali R. Wahjoe Witjaksono Sawali Wahyu Yofa Fauzia Azima