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All Journal Telematika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Jurnal Teknoinfo EVOLUSI : Jurnal Sains dan Manajemen Media Aplikom Conten : Computer and Network Technology Indonesian Journal of Networking and Security - IJNS Informatics and Computer Engineering Journal CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Yustina Meisella Kristania - STMIK Nusa Mandiri Jakarta, Yustina Meisella Kristania
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science

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ANALISIS PERBANDINGAN ALGORITMA NAIVE BAYES DAN KNN UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI VIDIO DI GOOGLE PLAY STORE Pratmanto, Dany; Widayanto, Aprih; Kristania, Yustina Meisella; Ubaidillah , Ubaidillah; Wijianto, Ragil
CONTEN : Computer and Network Technology Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/conten.v4i2.6891

Abstract

Penelitian ini mengkaji efektivitas algoritma Naive Bayes (NB) dan K-Nearest Neighbors (KNN) dalam analisis sentimen ulasan pengguna aplikasi Vidio di Google Play Store. Evaluasi kinerja kedua model dilakukan menggunakan berbagai metrik, termasuk akurasi, precision, recall, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa KNN mengungguli Naive Bayes dalam beberapa aspek penting. KNN mencapai akurasi 74.92% dibandingkan dengan Naive Bayes sebesar 71.32%. Dalam hal precision, KNN juga menunjukkan performa yang lebih baik dengan nilai 76.52%, sementara Naive Bayes mencapai 71.61%. Meskipun demikian, kedua model menunjukkan kinerja yang sebanding dalam hal recall, dengan KNN mencapai 72.54% dan Naive Bayes 71.46%. Yang menarik, kedua model memiliki nilai AUC yang sangat tinggi dan hampir setara, yaitu 90.10% untuk KNN dan 90.00% untuk Naive Bayes, menunjukkan kemampuan yang sangat baik dalam membedakan sentimen positif dan negatif. Berdasarkan hasil evaluasi secara keseluruhan, algoritma KNN lebih direkomendasikan untuk implementasi analisis sentimen pada ulasan pengguna aplikasi Vidio.
Penerapan Combined Compromise Solution Method Dalam Penentuan Penerima Beasiswa Kristania, Yustina Meisella
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 1 No. 2 (2023): Volume 1 Number 2 April 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v1i2.27

Abstract

This study aims to solve the problem of determining scholarship recipients through a decision support system with output in the form of ranking using the Combined Compromise Solution Method. The application of the Combined Compromise Solution (CoCoSo) method in determining scholarship recipients will result in a comparison of the scores of all students who register to receive scholarships from schools. So that through the decision support system developed it can produce prospective scholarship recipients according to predetermined criteria and the selection process does not take long because the decision support system built is equipped with a web-based application using Laravel 8 which is used to determine scholarship recipients. Calculations for determining scholarship recipients using the Combined Compromise Solution (CoCoSo) show that the results of calculating the final score of the 1st scholarship recipient received a final score of 2.7031 obtained by Dwi Adhawati, the 2nd scholarship recipient received a final score of 1.9111 obtained by Muhammad Ferdi, and the 3rd scholarship recipient with a final score of 1.8684 was obtained by Muhammad Irfan.
Sistem Informasi Inventory Persediaan Barang Striping Motor Berbasis Web dengan Metode Prototype Pada Bengkel Ageng Motor Fitriana, Saghifa; Wicaksono, Ageng; Nouvel, Ahmad; Kristania, Yustina Meisella
Informatics and Computer Engineering Journal Vol 5 No 1 (2025): Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v5i1.7605

Abstract

Information technology is currently experiencing very rapid development and following progress in all fields. Technological developments change the way information is spread quickly and easily. Forming an information system requires systematic processing to meet information needs. Ageng Motor Cilacap still checks inventory manually by the business owner. Data on incoming and outgoing goods is recorded using a form made by the business owner and accompanied by a receipt showing the number of buyers' orders. As a result of this problem, errors sometimes occur in inventory calculations as well as difficulties in recording and making reports on incoming and outgoing goods. With a good information system, it is hoped that the process of managing goods entering and leaving motorbike striping can be carried out more efficiently, accurately and quickly. This certainly brings many benefits to the business world, such as increasing productivity, saving time and costs, as well as increasing customer satisfaction. This research uses the prototype method of software development. Data collection used was interviews, observation and reference studies. The results of this research are the object of research regarding the incoming and outgoing goods inventory procedures that are applied.  
ANALISIS PERBANDINGAN ALGORITMA NAIVE BAYES DAN KNN UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI VIDIO DI GOOGLE PLAY STORE Pratmanto, Dany; Widayanto, Aprih; Kristania, Yustina Meisella; Ubaidillah , Ubaidillah; Wijianto, Ragil
CONTEN : Computer and Network Technology Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/conten.v4i2.6891

Abstract

Penelitian ini mengkaji efektivitas algoritma Naive Bayes (NB) dan K-Nearest Neighbors (KNN) dalam analisis sentimen ulasan pengguna aplikasi Vidio di Google Play Store. Evaluasi kinerja kedua model dilakukan menggunakan berbagai metrik, termasuk akurasi, precision, recall, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa KNN mengungguli Naive Bayes dalam beberapa aspek penting. KNN mencapai akurasi 74.92% dibandingkan dengan Naive Bayes sebesar 71.32%. Dalam hal precision, KNN juga menunjukkan performa yang lebih baik dengan nilai 76.52%, sementara Naive Bayes mencapai 71.61%. Meskipun demikian, kedua model menunjukkan kinerja yang sebanding dalam hal recall, dengan KNN mencapai 72.54% dan Naive Bayes 71.46%. Yang menarik, kedua model memiliki nilai AUC yang sangat tinggi dan hampir setara, yaitu 90.10% untuk KNN dan 90.00% untuk Naive Bayes, menunjukkan kemampuan yang sangat baik dalam membedakan sentimen positif dan negatif. Berdasarkan hasil evaluasi secara keseluruhan, algoritma KNN lebih direkomendasikan untuk implementasi analisis sentimen pada ulasan pengguna aplikasi Vidio.
Co-Authors Afit Muhammad Lukman - STMIK Nusa Mandiri Jakarta, Afit Muhammad Lukman Ahmad Nouvel - AMIK BSI Purwokerto Ahmad Nouvel - AMIK BSI Purwokerto Amin Nur Rais Aprih Widayanto Chandra Kesuma Dany Pratmanto Desiana Nur Kholifah Eris Br Sipayung Fatimah Isnaeni - Universitas BSI Bandung Firda Dini Yulianti Ina Maryani Ina Maryani - STMIK Nusa Mandiri Jakarta Iqbal Asyifudin - AMIK BSI Purwokerto Joko Dwi Mulyanto Juwantoro, Hendi Sudrajat Nani Purwati Nur Amalia Solikhah Ragil Wijianto Rousyati Rousyati Saghifa Fitriana Sopian Aji Suripah Suripah Surya Listanto Thira, Indra Jiwana Tri Winarsih Ubaidillah , Ubaidillah Wicaksono, Ageng