Article Per Year (5 Year)
p-Index From 2021 - 2026
1.405
P-Index
This Author published in this journals
All Journal bit-Tech JSR : Jaringan Sistem Informasi Robotik Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Abdi Jurnal Publikasi Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia JRIIN :Jurnal Riset Informatika dan Inovasi
Ester, Ria
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Transportation Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
PELATIHAN MICROSOFT OFFICE EXCEL UNTUK MENINGKATKAN KEGIATAAN PEMBELAJARAN PADA SMP NEGERI 02 MARGOYOSO Ester, Ria; Galuh Saputri; Agung Siswopranoto
Abdi Jurnal Publikasi Vol. 2 No. 5 (2024): Mei
Publisher : Abdi Jurnal Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi telah menyentuh di berbagai sektor, salah satunya adalah di bidang pendidikan bagi para siswa. Para siswa dituntut untuk bisa paham dan mengikuti perkembangan jaman dengan memanfaatkan teknologi informasi. Namun, pemanfaatan teknologi belum sepenuhnya diterapkan untuk peningkatan kualitas para siswa. Para siswa-siswi sekolah menengah pertama (SMP) dituntut untuk memiliki keterampilan lebih untuk menghadapi tantangan perkembangan teknologi pada umumnya. Karena pada kurikulum SMP sudah diselenggarakan kurikulum merdeka belajar yang artinya menerapkan program pembelajaran tatap muka dan pembelajaran sistem projek. SMP Negeri 02 Margoyoso adalah salah satu sekolah menengah pertama yang menerapkan program kurikulum merdeka belajar. Pada sekolah tersebut belum ada mata pelajaran yang secara khusus melakukan pembahasan lebih mengenai penggunaan aplikasi Microsoft Office di Windows seperti Excel, Word dan Power Point. Meskipun aplikasi ini sangat berguna bagi siswa apalagi dengan kejuruan perkantoran. Aplikasi tersebut berguna baik dalam mengerjakan tugas sekolah ataupun kegiatan dilingkungan sekitar mereka. Untuk itu diadakanlah pelatihan Microsoft Office ini agar bisa meningkatkan pembelajaran siswa baik dalam mengerjakan tugas sehari-hari disekolah dan sebagai bekal dasar mereka setelah keluar sekolah nanti, agar bisa diterapkan di sekolah menengah atas ataupun saat mereka kuliah dan lingkungan kerja. Pada pelatihan kali ini membahas mengenai Microsoft Office Excel. Microsoft Office Excel ini umumnya mencakup berbagai topik, termasuk dasar-dasar spreadsheet, manipulasi data, rumus, fungsi, analisis data dan visualisasi. Pelatihan ini melibatkan latihan langsung untuk meningkatkan keterampilan menggunakan fitur  seperti rumus, fungsi, grafik, dan validasi data. Tujuan dari program kemitraan masyarakat ini adalah untuk meningkatkan keterampilan komputer siswa khususnya Microsoft Office Excel. Setidaknya semudah Microsoft Office Excel dengan alat khusus yang mereka gunakan untuk membuat laporan keuangan seperti misalnya menggunakan margin, memasukkan teks ke dalam tabel, membuat format tanggal dan waktu, dan menggunakan formula penjumlahan, pengurangan, pembagian dan perkalian serta penyusunan bentuk logika matematika yang tepat dan benar yang dapat diterapkan untuk menyelesaikan tugas dan kewajiban di SMP Negeri 02 Margoyoso.
The Application of Data Mining in Predicting Cryptocurrency Prices Using the Support Vector Machine (SVM) Method: Indonesia Ester, Ria; Hidayah, Nasrul; Handayani, Dede
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2245

Abstract

Cryptocurrencies have rapidly emerged as one of the most exciting financial technology innovations in recent years. Among the various digital assets, XRP (Ripple) is one of the most popular, experiencing significant price fluctuations. This study aims to apply the Support Vector Machine (SVM) method in predicting the price of the XRP cryptocurrency, in the hope of providing a clearer picture of the investment prospects. The data used in this study includes the daily price movements of XRP from 2019 to 2023. In the research process, the date variable is selected as the input feature, and the closing price as the output to be predicted. Various kernel functions in SVM, including RBF, Polynomial, and Sigmoid, were tested to determine which one gave the best results. The results showed that the Polynomial kernel produced a Mean Absolute Percentage Error (MAPE) value of 45.40%, indicating better accuracy compared to other kernels. This study also explains the importance of choosing the right kernel function and overcoming the problem of underfitting that may occur due to the high volatility characteristics of cryptocurrencies. These findings not only enrich the understanding of machine learning techniques but also provide new insights for investors in data-based decision making. Recommendations for future research include the use of alternative prediction models and the integration of external information that can affect prices.
Implementasi Sistem Monitoring Jaringan Terpusat Menggunakan Mikrotik Dan Bot Telegram Di PT. Mitra Bersama Jaya Baydowi, Wildi; Ester, Ria; Rinaldi, Renault; Ali Najmi, Raihan
Jurnal Riset Informatika dan Inovasi Vol 3 No 3 (2025): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk merancang sistem monitoring jaringan berbasis Mikrotik yang terintegrasi dengan Bot Telegram, guna memberikan notifikasi real-time atas gangguan jaringan. Metodologi yang digunakan adalah pendekatan PPDIOO dari Cisco, dimulai dari tahapan perencanaan hingga optimasi. Hasil implementasi menunjukkan sistem dapat memberikan notifikasi real-time kepada administrator ketika terjadi gangguan koneksi atau tunnel antar cabang, meningkatkan respon dan efektivitas tim IT.
Perancangan Sistem Informasi Ulangan Online Berbasis Website Pada SMK YP Mulia Jakarta Hadi Agustiana, Akbar; Ester, Ria; Tri Sundari, Anggita
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 3 No. 2 (2025): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

SMK YP Mulia Jakarta is a vocational high school that has adopted technology in its educational process, including the implementation of daily assessments, which were previously managed through Google Forms. Despite its use, this platform has several shortcomings, such as rigid scheduling options, the absence of automatic question randomization, limited participant authentication capabilities, and a lack of automated result analysis. Moreover, since Google Forms is a third-party tool, the school lacks full authority over the system and its data. To overcome these challenges, the author developed a custom web-based online testing system specifically designed to meet the requirements of SMK YP Mulia Jakarta. The system offers features such as configurable test timing, randomized questions, token-based access management, secure user authentication, and real-time, integrated result reporting.
OPTIMASI ALGORITMA KLASIFIKASI DECISION TREE (CART) DENGAN METODE BAGGING UNTUK DETEKSI WEBSITE PHISHING Ester, Ria; Mulani, Sartika Lina
JSR : Jaringan Sistem Informasi Robotik Vol 8, No 1 (2024): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v8i1.351

Abstract

Advances in science and technology (IPTEK), especially information technology, make it easier to support community activities, but at the same time make various devices vulnerable to exploitation by cybercriminals [1]. One such activity is stealing data from Internet users through fake sites (also known as phishing sites) designed to look like the real thing. Phishing websites pose a serious threat to online information security and require an effective approach to detect and prevent them. To combat the proliferation of phishing websites in cyberspace, a classification is needed to predict which websites will be classified as phishing websites using the Decision Tree Classification Algorithm (CART) [2]. To improve the performance of the Decision Tree Classification (CART) algorithm and achieve better optimal accuracy, optimization using the bagging method is needed. A bagging technique that combines the results of several decision tree models is applied to improve the performance and reliability of the CART algorithm in detecting phishing websites [3]. In this research, we collected a dataset containing various characteristics related to the characteristics of phishing websites. The data is then processed and divided into subsets for model training and testing. The aim of this research is to optimize the decision tree classification algorithm (CART) by applying bagging techniques in the context of phishing website detection. Based on test results, applying the Decision Tree Classification Algorithm (CART) to classify phishing websites produces an accuracy of 96.61%, and when combined with bagging techniques, the accuracy increases by 1.13% to 97.74%. This experiment shows that optimization can improve the prediction accuracy of phishing websites by combining the Decision Tree Algorithm (CART) with bagging techniques. Keywords: Phishing Websites, Classification, Prediction, Algorithms, Decision Trees.
Analisis Penerapan Metode WASPAS untuk Penentuan Pola Belajar Mahasiswa Berdasarkan Gaya Belajar Ester, Ria; Yuniarti, Dian Tri; Valentina, Putri Eka; Kusumah Putra, Faris Maulana
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Higher education in the digital era requires learning approaches that are able to adapt to individual student characteristics, including differences in learning styles. This study aims to develop a model for assessing students’ learning patterns and to provide more personalized learning recommendations using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The data used are secondary data obtained from 1,000 students with seven learning criteria, namely academic score, course participation, attendance rate, physical activity, emotional engagement, device usage, and feedback score. The WASPAS method is applied through two main stages, namely the calculation of the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), which are then aggregated to produce a composite WASPAS score for each student. Manual calculations are demonstrated using five student samples, while computations for the entire dataset are performed using Python in the Jupyter Notebook environment. The results show that students’ WASPAS scores range from 0.2815 to 0.9914 with a distribution that tends to be normal. Most students fall into the “fair” to “very good” learning pattern categories, while a small proportion are classified as “very high” and “requiring special attention.” Analysis based on visual, auditory, and kinesthetic learning styles indicates differences in average WASPAS scores across groups, supporting the effectiveness of the WASPAS method in integrating multiple learning criteria simultaneously. These findings demonstrate that WASPAS can be used as a decision support tool to map student learning profiles and assist in designing more adaptive, targeted, and personalized learning strategies in higher education
Co-Authors Agung Siswopranoto Ali Najmi, Raihan Baydowi, Wildi Galuh Saputri Hadi Agustiana, Akbar Handayani, Dede Hidayah, Nasrul Kusumah Putra, Faris Maulana Rinaldi, Renault Sartika Lina Mulani Tri Sundari, Anggita Valentina, Putri Eka Yuliana Yuliana Yuniarti, Dian Tri