Article Per Year (5 Year)
p-Index From 2021 - 2026
1.363
P-Index
This Author published in this journals
All Journal JURNAL SISTEM INFORMASI BISNIS Coding: Jurnal Komputer dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) BAREKENG: Jurnal Ilmu Matematika dan Terapan CYBERNETICS Jurnal Computer Science and Information Technology (CoSciTech)
Menur Wahyu Pangestika, Menur Wahyu
Universitas Muhammadiyah Pontianak
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
Sistem Pendukung Keputusan Wilayah Promosi Menggunakan Metode AHP-SMART pada Universitas Muhammadiyah Pontianak Brianorman, Yulrio
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021832997

Abstract

Promosi merupakan upaya menawarkan produk atau jasa pada dengan tujuan menarik calon konsumen untuk membeli. Melalui promosi, produsen mengharapkan kenaikannya angka penjualan produk atau jasa yang ditawarkan. Jasa yang ditawarkan oleh sebuah perguruan tinggi swasta adalah jasa pendidikan yang dapat diukur angka penjualannya dengan jumlah kursi penerimaan mahasiswa baru. Aktivitas promosi dengan jangkauan luas memerlukan biaya yang besar sehingga perlu penentuan skala prioritas wilayah. UM Pontianak belum memiliki metode khusus untuk penentuan wilayah yang ada di Kalimantan Barat. Oleh karena itu diperlukan sistem pendukung keputusan untuk menentukan prioritas wilayah. Sistem pendukung pengambilan keputusan ini dibuat menggunakan metode AHP-SMART. Metode AHP digunakan untuk menentukan konsistensi dari skala penilaian antara kriteria sementara metode SMART untuk menyelesaikan masalah pedukung keputusan yang memiliki multikriteria. Hasil dari perhitungan AHP menunjukkan consistensi ratio = 0,05. Dengan nilai CR di bawah 0,1 maka hasil perhitungan bobot menggunakan AHP dapat disetujui secara konsisten. Langkah selanjutnya adalah menentukan nilai prioritas dengan metode SMART. Hasil perhitungan SMART menunjukkan nilai 92,96 untuk kota Pontianak sebagai prioritas utama, disusul dengan dengan kabupaten Kuburaya, kabupaten Landak, kabupaten Sambas, kabupaten Bengkayang, kabupaten Sintang, kabupaten Ketapang, kabupaten Sanggau, kota Singkawang, kabupaten Mempawah, kabupaten Kapuas Hulu, kabupaten Sekadau, kabupaten Melawi dan kabupaten Kayong Utara. AbstractPromotion is an offering to potential customers to buy a product or service. Through promotions, producers expect an increase in sales of products or services offered. Services offered by a private tertiary institution are educational services that can be measured by sales figures with the number of new student admission seats. Delivering wide reach promotional activities require large costs so it is necessary to determine the regional priority scale. UM Pontianak does not have a specific method for determining areas in West Kalimantan yet, therefore a decision support system is needed. This decision making support system was created using the AHP-SMART method. The AHP method is used to calculate the consistency of the rating scale between the criteria while the SMART method is to solve the decision support problem that has multi criteria. The results of the AHP calculation show a consistency ratio = 0.05. With a CR value below 0.1, the weight calculation results using AHP can be consistently agreed. Furthermore, to determine the priority value, SMART method was used. The SMART results show the city of Pontianak as a top priority with the value 92.96, followed by Kuburaya district, Landak district, Sambas district, Bengkayang district, Sintang district, Ketapang district, Sanggau district, Singkawang city, Mempawah district, Kapuas Hulu district, district Sekadau, Melawi district and Kayong Utara district.
Perbandingan Random Forest Regressor Dan Decision Tree Regressor Untuk Prediksi Hasil Panen Rizki Faizal; Abdullah, Asrul; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9966

Abstract

Uncertainty in crop yields due to environmental factors remains a major challenge in Indonesia's agricultural sector. This study aims to compare the performance of the Random Forest Regressor and Decision Tree Regressor algorithms in predicting cultivated crop yields. The dataset used was sourced from Kaggle, consisting of 300,000 rows with features such as crop type, soil type, rainfall, fertilizer use, irrigation, and weather conditions. The system was developed using Python and Streamlit. The methodology includes data preprocessing, model training, and evaluation using the Mean Absolute Error (MAE) metric. The test results show that the Decision Tree Regressor achieved a lower MAE (0.43) compared to the Random Forest Regressor (0.48), resulting in more accurate predictions on this dataset. Feature analysis indicates that rainfall and crop type are the most influential factors. Although Random Forest is generally known for its stability, this study demonstrates that Decision Tree can outperform it within the context of the dataset used. The developed system is expected to assist farmers and policymakers in planning agricultural production more efficiently and in a data-driven manner.
Prediction of Diabetes Mellitus Using the Case-Based Reasoning Method Rahimah, Auliyya; Siregar, Alda Cendekia; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10266

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected and treated early. According to data from WHO and the Indonesian Ministry of Health, the prevalence of DM continues to rise each year, highlighting the need for a diagnostic support system that is both fast and accurate. This study aims to develop an expert system capable of predicting Diabetes Mellitus using the Case Based Reasoning (CBR) method. CBR is applied because it solves new problems by comparing them to previous cases based on the similarity of symptoms. The system incorporates 20 symptoms classified into two types of DM: type 1 and type 2. The prediction process follows the four main stages of CBR: retrieve, reuse, revise, and retain. Test results show that the system can predict the disease with an accuracy rate of over 90%, and user feedback through Blackbox Testing and User Acceptance Testing (UAT) confirms its usability. This expert system is expected to serve as an initial consultation tool to help users obtain early information related to potential DM quickly, easily, and efficiently.
COMPARISON OF RANDOM FOREST AND XGBOOST ALGORITHMS IN CREDIT CARD FRAUD CLASSIFICATION Abdullah, Asrul; Khairah, Della Udya; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10470

Abstract

Credit card fraud is a serious issue that can cause significant losses for both consumers and financial service providers. Therefore, a reliable and accurate fraud detection system is essential. The research adopts the CRISP-DM methodology, which includes six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset used was obtained from the Kaggle platform, consisting of 1,048,574 rows and 23 Features, including transaction amount, merchant category, location, and customer attributes. Model evaluation was conducted using a Confusion Matrix with accuracy, precision, recall , and F1-score as performance metrics. The evaluation results indicate that Xgboost outperforms Random Forest, achieving an accuracy of 99.19%, precision of 98.73%, recall of 99.66%, and F1-score of 99.19%. In comparison, Random Forest achieved an accuracy of 97.68%, precision of 97.38%, recall of 98.01%, and F1-score of 97.69%. These results demonstrate that Xgboost is more effective in consistently identifying fraud ulent transactions. Furthermore, this study successfully developed a web-based application using the Streamlit framework, integrating both models interactively to allow users to input data and obtain classification results in real time. Thus, this study has successfully achieved three main objectives: identifying the most suitable algorithm for fraud classification, thoroughly evaluating model performance, and developing an application as a decision support system for credit card fraud detection.
Prediksi Harga Mobil Bekas Menggunakan Algoritma Support Vector Regression Herlangga, Herlangga; Pangestika, Menur Wahyu; Alkadri, Syarifah Putri Agustini
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10545

Abstract

The growth of the automotive industry in Indonesia has contributed to high demand for used cars as a more economical alternative to new cars. However, determining the price of used cars is often a challenge for showrooms and prospective buyers because it involves many factors and is subjective. This study aims to develop a used car price prediction model using the Support Vector Regression (SVR) algorithm with a Radial Basis Function (RBF) kernel approach. A total of 1,000 entries were obtained through web scraping from the cintamobil.com website. The research methodology refers to the CRISP-DM framework, starting from business understanding to model deployment through a web application using Streamlit. The preprocessing process involves handling missing values, outliers, data duplication, and numerical and categorical feature transformations. The SVR model was evaluated using RMSE, MAPE, and MAE metrics to assess prediction accuracy. The results show that SVR is capable of providing fairly accurate price predictions, with parameters C=1, gamma=0.1, and epsilon=0.1 producing the best performance, namely an MAE value of IDR 6,472,572, an RMSE of IDR 8,958,555, and a MAPE of 3.41%. Referring to the prediction accuracy category based on the MAPE value, where a MAPE value ≤ 10% is categorized as high accuracy, it can be concluded that this model has high prediction accuracy. This shows that the SVR model used is capable of estimating used car prices with a low error rate and good accuracy.
Analisis Penerapan Algoritma Random Forest Dalam Klasifikasi Prakiraan Cuaca Saputra, Deny Saputra; Pangestika, Menur Wahyu; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10846

Abstract

Weather plays an important role in various aspects of life, such as agriculture and transportation. However, weather prediction remains challenging because it is influenced by many complex factors. Extreme weather events, such as storms and floods, can cause significant losses, making accurate weather forecast classification systems essential. This study applies the Random Forest algorithm to improve prediction accuracy and optimizes it using Grid Search Cross Validation. The method used is CRISP-DM, consisting of six main stages. The data were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG), containing features such as temperature, humidity, wind speed, cloud cover, visibility, and wind direction, with the labels Weather Condition and Region Name serving as indicators of the classified weather category and location. The final evaluation uses a confusion matrix, yielding an accuracy of 98.84% on the training data and 95.33% on the testing data, demonstrating stable performance and strong generalization capability.
Co-Authors Alda Cendekia Siregar Alkadri, Syarifah Putri Agustini Asrul Abdullah Herlangga, Herlangga Khairah, Della Udya Octariadi, Barry Ceasar Oky Dwi Nurhayati Rahimah, Auliyya Rinaldi Munir Rizki Faizal Saputra, Deny Saputra Siregar, Alda Cendekia Sucipto Sucipto Suryono Suryono Yudi Qadar Pratomo