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Pengembangan Model Pembelajaran Berbasis Cooperative Learning dalam Meningkatkan Motivasi Belajar Mahasiswa dan Peningkatan Mutu Lulusan Alumni Fasilkom Unsri Berbasis E-Learning Kurnia, Rizka Dhini; Ruskan, Endang Lestari; Ibrahim, Ali
Jurnal Sistem Informasi Vol 6, No 1 (2014): -
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

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Pembelajaran perkuliahan Praktikum Pemrogramn web dengan model Cooperative Learning merupakan perkuliahan berdasarkan kerja kelompok untuk menyelesaikan projek yang diberikan oleh dosen pengampu. Tujuan dengan pembelajaran model Cooperative Learning adalah: (a) dapat meningkatkan hasil belajar akademik; (b) penerimaan terhadap keragaman, yaitu agar mahasiswa menerima teman-temannya yang mempunyai berbagai latar belakang; (c) pengembangan keterampilan sosial, yaitu untuk mengembangkan keterampilan sosial mahasiswa diantaranya: berbagi tugas, aktif bertanya, menghargai pendapat orang lain, memotivasi teman untuk bertanya, mau mengungkapkan ide, dan bekerja dalam kelompok. Sedangkan manfaat dari pembelajaran dengan model Cooperative Learning adalah: (a) mahasiswa yang diajari dengan dan dalam struktur-truktur kooperatif akan memperoleh hasil pembelajaran yang lebih tinggi; (b) mahasiswa yang berpartisipasi dalam pembelajaran kooperatif akan memiliki sikap harga diri yang lebih tinggi dan motivasi yang lebih besar untuk belajar; (c) dengan pembelajaran kooperatif, mahasiswa menjadi lebih peduli pada teman-temannya, dan diantara merekaÂ akan terbangun rasa ketergantungan yang positif untuk proses belajar; (d) pembelajaran kooperatif meningkatkan rasa penerimaan mahasiswa terhadap teman-temannya yang berasal dari latar belakang ras dan etnik yang berbeda. Dari hasil kegiatan pembelajaran Pembelajaran perkuliahan Praktikum Pemrogramn web dengan model Cooperative Learning pada semester gasal 2013 jurusan sistem informasi fakultas ilmu komputer dapat disimpulan bahwa dapat meningkatkan nilai akademik masasiswa dan kemampuan serta pemahaman tentang pemrograman web. Selain proses pembelajaran dengan model Cooperative Learning, kegiatan ini juga menggunakan ICT sebagai salah satu media pendukung yaitu adanya tuturial proses pembelajaran interaktif berbasis multimedia. Sehingga mahasiswa lebih leluasa untuk mencoba kembali dirumah bersama tim kelompok untuk belajar bersama. Dari hasil analisis 80 % mahasiswa dinyatakan berhasil mempelajari materi yang disampaikan dengan model Cooperative Learning. Bahkan berdasarkan hasil diskusi secara langsung dengan mahasiswa, model seperti yang membantu mereka dalam belajar karena mereka dapat secara leluasa bertanya sesama tim, dosen dan dapat berkreasi bersama

Penerapan Metode Fuzzy Sugeno Pada Sistem Pendukung Keputusan Penentuan Konsentrasi Mahasiswa Jurusan Teknik Sipil Indah, Dwi Rosa; Kurnia, Rizka Dhini; Alvionita, Vinna
Jurnal Sistem Informasi Vol 9, No 2 (2017): Oktober
Publisher : Major of Information Systems Faculty of Computer Science Sriwijaya University

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Abstract This research emphasizes on the application of Fuzzy Sugeno method in Decision Support System for Determining Concentration of Students Civil Engineering at Case Study of Civil Engineering Department of State Polytechnic of Sriwijaya. The fuzzy sugeno method is done by defining input and output stages, fuzzy set formation and completion using fuzzy sugeno method. The result is fuzzy sugeno methodcan solve problem in Determining Concentration of Students Civil Engineering at Case Study. In addition, Computer Based Decision Support System for Determination of Civil Engineering Student Concentration using Fuzzy Sugeno resulted in the percentage of eligibility of each concentration of civil engineering department and give the most appropriate concentration recommendation for the students. Keywords: Decision Support System, FuzzySugeno Abstrak Penelitian ini menekankan pada penerapan Metode Fuzzy Sugeno dalam Sistem Pendukung Keputusan Penentuan Konsentrasi Mahasiswa Jurusan Teknik Sipil pada tempat studi kasus Jurusan Teknik Sipil Politeknik Negeri Sriwijaya.Penerapan metode fuzzy sugeno dilakukan dengan tahap pendefinisian input dan output, pembentukan himpunan fuzzy dan penyelesaian dengan metode fuzzy sugeno. Hasilnya adalah metode fuzzy sugeno dapat membantu mengatasi masalah Penentuan Konsentrasi Mahasiswa Jurusan Teknik Sipil di tempat studi kasus. Selain itu Sistem Pendukung Keputusan Berbasis Komputer untuk Penentuan Konsentrasi Mahasiswa Jurusan Teknik Sipil menggunakan Fuzzy Sugeno menghasilkan persentase kelayakan masing-masing konsentrasi jurusan teknik sipil dan memberikan rekomendasi konsentrasi yang paling layak bagi mahasiswa. Kata kunci:Sistem Pendukung Keputusan, Fuzzy Sugeno

DeLone and McLean Model Analysis of Success Factors of SIDEMANG Application in Palembang City Faris, Haninda Ammar; Wedhasmara, Ari; Putra, Apriansyah; Kurnia, Rizka Dhini; Bardadi, Ali; Fitri, Shofiyah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.1894

Indonesia is ranked 77th in the world in electronic-based government systems, especially the City of Plembang is ranked 89th regarding the evaluation of smart city improvement in Indonesia. One of the latest applications used in the past year is the SIDEMANG application, which is an information system that has the use and purpose of facilitating access to administrative services related to personal and agency licensing files online at the village and sub-district levels in Palembang city, but this is also not free from obstacles, especially internet signals. Therefore, an analysis is needed related to the implementation of Information Systems, to assess the success of applications that have been implemented, especially government digital services. DeLone and McLean Information System Success Model is used, to see the significant factors that cause the success of Information System implementation. The data analysis method used in this research is quantitative because the data collected is in the form of numbers and will be analyzed using the SmartPLS application statistical technique, using a sample size of 97 respondents. The results showed that the information quality factor was not significant to the intention or use of the application, the system quality factor was not significant to the intention or use of the application, the system quality factor was not significant to user satisfaction, the service quality factor was not significant to user satisfaction. Recommendations for the Palembang City Communication and Information Office are related to the evaluation and improvement of the SIDEMANG application using the DeLone and McLean Model analysis. In particular, improvements to the quality of information that can influence citizens to use the application, improvements to the quality of the system that can invite and satisfy users in using the application, and improvements to service quality factors on citizen satisfaction.

Analisis Sentimen Ulasan Aplikasi Pembelajaran Bahasa Menggunakan Metode VADER Leonardi, Veronica Hertensia; Ibrahim, Ali; Kurnia, Rizka Dhini; Afrina, Mira
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2285

Perkembangan teknologi saat ini mempermudah proses belajar bahasa melalui aplikasi seperti Duolingo. Penelitian ini bertujuan untuk memahami persepsi pengguna terhadap Duolingo dengan menggunakan analisis sentimen berbasis VADER (Valence Aware Dictionary and Sentiment Reasoner). Ulasan pengguna dari Google Play Store diproses menggunakan Google Collaboratory, menghasilkan 1.831 data yang dikelompokkan sebagai netral, negatif, dan positif. Hasil analisis menunjukkan akurasi keseluruhan sebesar 98 persen. Model ini efektif dalam mengidentifikasi sentimen netral (presisi 100 persen, recall 97 persen, F1-score 99 persen) dan positif (presisi 99 persen, recall 82 persen, F1-score 99 persen). Namun, model kurang efektif dalam mendeteksi emosi negatif, dengan F1-score 74 persen, recall 82 persen, dan presisi 67 persen, yang menunjukkan adanya kesalahan klasifikasi pada beberapa emosi negatif. Awan kata menunjukkan kata-kata positif seperti "good," "helpful", dan "fun," serta kata-kata negatif seperti "technical problems" dan "learning limitations." Tantangan dalam penggunaan VADER termasuk ketidakmampuan menangani konteks bahasa yang kompleks dan nuansa emosional yang mendalam. Untuk meningkatkan klasifikasi sentimen, penelitian ini merekomendasikan penggunaan VADER bersama Deep-Translator. Kombinasi ini dapat membantu mengidentifikasi sentimen negatif dengan lebih baik dan menangani data dengan berbagai bahasa secara lebih efisien. Tujuan penelitian ini adalah untuk memahami sudut pandang pengguna dan meningkatkan akurasi analisis sentimen, sehingga berkontribusi pada pengembangan aplikasi pembelajaran bahasa yang lebih baik.

DeLone and McLean Model Analysis of Success Factors of SIDEMANG Application in Palembang City Faris, Haninda Ammar; Wedhasmara, Ari; Putra, Apriansyah; Kurnia, Rizka Dhini; Bardadi, Ali; Fitri, Shofiyah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.1894

Indonesia is ranked 77th in the world in electronic-based government systems, especially the City of Plembang is ranked 89th regarding the evaluation of smart city improvement in Indonesia. One of the latest applications used in the past year is the SIDEMANG application, which is an information system that has the use and purpose of facilitating access to administrative services related to personal and agency licensing files online at the village and sub-district levels in Palembang city, but this is also not free from obstacles, especially internet signals. Therefore, an analysis is needed related to the implementation of Information Systems, to assess the success of applications that have been implemented, especially government digital services. DeLone and McLean Information System Success Model is used, to see the significant factors that cause the success of Information System implementation. The data analysis method used in this research is quantitative because the data collected is in the form of numbers and will be analyzed using the SmartPLS application statistical technique, using a sample size of 97 respondents. The results showed that the information quality factor was not significant to the intention or use of the application, the system quality factor was not significant to the intention or use of the application, the system quality factor was not significant to user satisfaction, the service quality factor was not significant to user satisfaction. Recommendations for the Palembang City Communication and Information Office are related to the evaluation and improvement of the SIDEMANG application using the DeLone and McLean Model analysis. In particular, improvements to the quality of information that can influence citizens to use the application, improvements to the quality of the system that can invite and satisfy users in using the application, and improvements to service quality factors on citizen satisfaction.

Sentiment Classification of TikTok Reviews on Almaz Fried Chicken Using IndoBERT and Random Oversampling Zaki, Imam Syahputra; Kurnia, Rizka Dhini; Meiriza, Allsela
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1310

The socio-political context surrounding the Indonesian Ulema Council's Fatwa No. 83 of 2023, which catalyzed a significant consumer shift, necessitates an accurate measure of public sentiment toward alternative local brands like Almaz Fried Chicken. Analyzing real-time consumer discourse on the challenging TikTok platform, the study utilized a final dataset of 4,374 unique comments to overcome the inherent problem of dataset imbalance and linguistic informality. The core method involved a seven-stage quantitative approach: data collection, preprocessing, sentiment labeling, data splitting (70:15:15), Random Oversampling (ROS), IndoBERT fine-tuning, and evaluation. This pipeline fine-tuned IndoBERT, a Transformer-based model, integrated with ROS applied exclusively to the training data. Evaluation demonstrated that ROS significantly reduced model bias and enhanced performance: Overall Accuracy increased by 2.0% (from 91% to 93%), and the Macro F1-Score improved by 3.4% (from 0.87 to 0.90). Most critically, the F1-Score for the minority Negative sentiment class surged from 0.78 to 0.84, confirming ROS's effectiveness in accurately detecting critical feedback. These findings provide timely, data-driven insights into brand perception amidst the boycott campaign and establish a robust, reliable IndoBERT-ROS methodology for advanced sentiment monitoring in dynamic social media environments.

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