Article Per Year (5 Year)
p-Index From 2021 - 2026
5.072
P-Index
This Author published in this journals
All Journal SMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Paradigma Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Infortech Jurnal AbdiMas Nusa Mandiri Abditeknika - Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Networking and Security - IJNS Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Teknologi Sistem Informasi Adopsi Teknologi dan Sistem Informasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Smatika Jurnal : STIKI Informatika Jurnal Bianglala Informatika Jurnal Infortech
Kresna Ramanda
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Penerapan Algoritma K-Nearest Neighbors (KNN) Dalam Analisis Sentimen Ulasan Pengguna Aplikasi JMO (Jamsostek Mobile) Ghifari Fatihah Rabbani; Kresna Ramanda; Sulaeman Hadi Sukmana; Qudsiah Nur Azizah; Erma Delima Sikumbang
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.11335

Abstract

JMO (Jamsostek Mobile) is an official application launched by BPJS Ketenagakerjaan, designed to support workers in facilitating access to social protection. However, several obstacles are experienced by application users, such as errors in the application, difficulties logging in, and JHT balances not being displayed. To obtain a general overview of JMO application user sentiment, an evaluation is needed to capture user concerns. Therefore, this study will explore the precision of the KNN algorithm in analyzing sentiment from JMO application user reviews. The objectives of this research are to identify and analyze user feedback, classify overall sentiments, implement sentiment analysis, and test the accuracy of the KNN algorithm. This study adopts a quantitative approach, applying numerical data analysis through text mining techniques. From a dataset of 10,000 reviews collected via web scraping and refined Preprocessing, 7,185 reviews were obtained, revealing that 51.38% expressed positive sentiment. The KNN algorithm achieved its highest accuracy 76.2%, precision 76.2%, recall 78.0%, and F1-score 77.1% at K = 21 under 90%-10% data split. Furthermore, the model’s AUC score of 0.7617 indicated fair and reasonably good performance. These findings suggest that the KNN classification model is capable of providing balanced classification results across classes, leading to a fairer and less biased evaluation.
Breast Cancer Prediction Optimization Using Support Vector Machine and Naive Bayes Algorithms Devi Wulandari; Qudsiah Azizah; Diah Puspitasari; Kresna Ramanda; Erma Delima Sikumbang; Sulaeman Hadi Sukmana
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12629

Abstract

Breast cancer is ranked as the second most common cause of death for women worldwide. Breast cancer is often found when it has entered the final stage. In general, this is due to slow handling and treatment, so it is very necessary to detect the disease early. The purpose of this study is to determine the performance of the two algorithms, namely Naïve Bayes and Support Vector Machine (SVM) in classifying breast cancer types which will then be analysed and compared the accuracy of the two algorithms. The dataset used in this study, Breast Cancer Wisconsin, is public data originating from UCI Machine Learning, has a total of 683 data with 10 attributes and has two classes, namely benign class with 458 data and malignant class with 241 data. The dataset was split 80:20, with 80% used as training data and 20% as testing data, and then evaluated using cross-validation. The results of the study show that Support Vector Machine (SVM) has the best performance with an accuracy of 96.89% while Naïve Bayes 96.15%. With this accuracy, These results indicate that the SVM model provides better classification performance than Naïve Bayes for the Breast Cancer Wisconsin dataset.
RANCANG BANGUN APLIKASI STOCK OPNAME PADA PT. ARIE MUTI BERBASIS ANDROID Irmawati Carolina; Kresna Ramanda; Arief Rusman; Ikhwan Akbar
INTI Nusa Mandiri Vol. 14 No. 1 (2019): INTI Periode Agustus 2019
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v14i1.544

Abstract

Stocktaking is a calculation and adjustment of the stock of goods and assets owned by a store or company in a warehouse or storefront with stock data contained in the company's database system. During stock taking, the entry and exit activities of goods cannot be carried out. This causes the company to be irregular in conducting stock taking. The purpose of this research is to create a system and application that can simplify the process of managing stock operations. The research method used in this research is using extreme programming method. With this method and the android application, it is expected that the stock management data management process will be more flexible because there is no need to use a computer but only with an Android-based smartphone. With this application, you only need to use the camera on the smartphone to scan the item in question without the need to type on the computer
Co-Authors - AMIK BSI Jakarta, Rosliana Agustin Abadi, Muhamad Sahrul Adi Supriyatna Agustina, Bella Andi Saryoko Annisa Fitrianti Arief Rusman Awaludin, Rizky Azizah, Qudsiah Nur Baginda Oloan Lubis Bobby Suryo Prakoso Cahyani Budihartanti, Cahyani Deri Saputra, Deri Devi Wulandari Diah Puspitasari Eni Pudjiarti Erma Delima Sikumbang Fattya Ariani Firda Kharismatus Silpa Ghifari Fatihah Rabbani Hadi Sukmana, Sulaeman Handayani, Tiwi Ikhwan Akbar Indara Bala Dewa Irmawati Carolina Januar Rama Fitra Karlena Indriani Monikka Nur Winnarto Muhammad Hilman Fakhriza Muhammad Hilman Fakriza Mutaqin, Asep Rijal Nathanael, Jonathan Nico Dias Palasara Oka Mahendra Oka Mahendra Prayogi, Randi Prima, Harry Pujiati, Tri Wahyuni Qudsiah Azizah Qudsiah Nur Azizah Retno Sari Retno Sari Retno Sari Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Rizka Syaleha Rosliana Agustin - AMIK BSI Jakarta Rosyda, Susy Shafa, Muadzi Sucitra Sahara Sundari, Wiwin Susanto Susanto Susanto Susanto Susy Rosyda Susy Rosyida Suwanda Suwanda Syarifudin, Imam Tri Juliyanto Utami, Yuliantika Siswanti Venny Yulianty Verra Sofica Windu Gata Y Yunita Yudi Yudi yunita yunita