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All Journal JURNAL SISTEM INFORMASI BISNIS Coding: Jurnal Komputer dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan NUSANTARA : Jurnal Ilmu Pengetahuan Sosial 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

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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.
Comparative Analysis of CNN Architectures for SIBI Image Classification Yulrio Brianorman; Dewi Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20608

Abstract

The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-pixel picture dataset was used for the study. A five-stage technique consisting of Dataset Collection, Dataset Preprocessing, Model Design, Model Training, and Model Testing was applied. Performance evaluation focused on accuracy, precision, recall, and F1-Score. The results identified VGG16 as the top-performing model with an accuracy of 99.60% and an equivalent F1-Score, followed closely by ResNet50 with nearly similar performance. Inception, XCeption, and MobileNetV2 demonstrated balanced performance but with lower accuracy. This study sheds light on the best CNN models to choose for SIBI image classification, and it makes recommendations for further research that include using sophisticated data augmentation methods, investigating novel CNN architectures, and putting the models to practical use.
PREDIKSI PANEN KELAPA SAWIT MENGGUNAKAN METODE RANDOM FOREST REGRESSION Friska Dwi Choirunisa; Menur Wahyu Pangestika; Sucipto Sucipto
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 7 (2026): NUSANTARA : Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i7.2026.1984-1989

Abstract

Tanaman kelapa sawit termasuk dalam komoditas perkebunan unggulan yang memiliki kontribusi signifikan terhadap pertumbuhan ekonomi, sehingga dibutuhkan strategi perencanaan panen yang tepat guna mengoptimalkan pemanfaatan sumber daya yang tersedia. Kelompok Tani "ASRI" hingga saat ini belum memiliki sistem yang mampu memperkirakan hasil panen secara tepat, sehingga proses perencanaan masih bergantung pada cara konvensional. Penelitian ini bertujuan membangun sebuah sistem prediksi hasil panen kelapa sawit dengan menggunakan metode Random Forest Regression yang berlandaskan data historis panen. Variabel yang digunakan mencakup tahun panen, bulan panen, usia tanaman, jenis tanah, pemberian pupuk, kondisi musim, dan luas areal tanam. Alur penelitian meliputi pengumpulan data, tahap preprocessing yang terdiri dari pembersihan data, penanganan data pencilan, normalisasi, dan label encoding, kemudian dilanjutkan dengan pemisahan data menjadi data latih dan data uji, pelatihan model Random Forest Regression, serta evaluasi kinerja model menggunakan Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Berdasarkan hasil pengujian, model menghasilkan nilai MAE sebesar 869,75 kg/bulan dan RMSE sebesar 1111,89 kg/bulan, yang menunjukkan bahwa model mampu menghasilkan prediksi hasil panen dengan tingkat deviasi yang tergolong rendah. Model yang telah dikembangkan selanjutnya diimplementasikan dalam bentuk aplikasi berbasis Streamlit sebagai alat bantu dalam perencanaan panen dan proses pengambilan keputusan di Kelompok Tani "ASRI".
PENERAPAN ALGORITMA TF-IDF DAN COSINE SIMILARITY UNTUK PENCARIAN ARSIP DOKUMEN DI KANTOR DESA KELAKAR Ria Maisya Syarifah; Menur Wahyu Pangestika; Sucipto Sucipto
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 7 (2026): NUSANTARA : Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i7.2026.2019-2024

Abstract

Pengelolaan arsip dokumen di Kantor Desa Kelakar hingga saat ini masih dilakukan tanpa sistem digital, sehingga menyebabkan kesulitan dalam pencarian dokumen, risiko kehilangan arsip, serta pemborosan waktu dan tenaga. Seiring meningkatnya kebutuhan akan pengelolaan arsip yang cepat dan akurat di Kantor Desa Kelakar, diperlukan sebuah sistem pencarian arsip digital yang mampu menampilkan dokumen secara relevan berdasarkan kata kunci pencarian. Penelitian ini menggunakan data berupa arsip dokumen administrasi desa dalam bentuk file PDF dan DOCX, termasuk dokumen hasil pemindaian yang diekstraksi menggunakan teknologi Optical Character Recognition (OCR). Tujuan penelitian ini adalah menerapkan algoritma Term Frequency–Inverse Document Frequency (TF-IDF) dan Cosine Similarity dalam sistem pencarian arsip dokumen berbasis web di Kantor Desa Kelakar. Hasil penelitian menunjukkan bahwa sistem mampu menampilkan dokumen yang relevan sesuai dengan kata kunci pencarian serta mempercepat proses temu kembali arsip. Hasil pengujian akurasi sistem menunjukkan nilai precision sebesar 0,001, recall sebesar 1,0, dan F1-score sebesar 0,0020, yang menunjukkan bahwa sistem mampu menampilkan dokumen yang relevan sesuai dengan kata kunci yang dimasukkan oleh pengguna. Selain itu, hasil white box testing menunjukkan bahwa seluruh fungsi dan alur logika program berjalan sesuai dengan rancangan yang telah ditetapkan tanpa ditemukan kesalahan fungsional yang signifikan. Dengan demikian, penerapan algoritma TF-IDF dan Cosine Similarity pada sistem pencarian arsip dokumen ini terbukti efektif dalam meningkatkan efisiensi dan kualitas pengelolaan arsip di Kantor Desa Kelakar
Rekomendasi Penentuan Prioritas Mustahik Pada BAZNAS Pontianak Menggunakan AHP-TOPSIS: Rekomendasi Penentuan Prioritas Mustahik Pada BAZNAS Pontianak Menggunakan AHP-TOPSIS wahyu, Wahyu; Siregar, Alda Cendekia; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 7 No 1 (2026): 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.v7i1.11058

Abstract

Prioritas mustahik menimbulkan tantangan signifikan dalam administrasi zakat, karena prosesnya sering dipengaruhi oleh faktor subjektif dan kurangnya efisiensi. Studi ini bertujuan untuk merancang Sistem Pendukung Keputusan (SPK) yang dapat memfasilitasi BAZNAS Pontianak untuk memprioritaskan mustahik dengan pendekatan objektif, sistematis, dan transparan, sehingga alokasi zakat menjadi lebih tepat sasaran. Metodologi yang diterapkan meliputi AHP untuk menentukan bobot kriteria melalui perbandingan berpasangan, dan Teknik untuk Urutan Preferensi berdasarkan Kesamaan dengan Solusi Ideal (TOPSIS) untuk memberi peringkat berdasarkan kedekatan dengan solusi ideal. Sistem yang dikembangkan adalah aplikasi web berbasis framework Laravel, yang dievaluasi menggunakan teknik black-box dan MAPE. Evaluasi menghasilkan nilai MAPE sebesar 0,131%, menunjukkan akurasi sistem yang luar biasa tinggi. Temuan ini menegaskan bahwa sistem ini mampu meningkatkan transparansi dan objektivitas distribusi zakat, dan berpotensi untuk diimplementasikan oleh lembaga amil zakat lainnya.
Co-Authors Alda Cendekia Siregar Asrul Abdullah Dewi Utami Friska Dwi Choirunisa G. Gunawan Herlangga, Herlangga Khairah, Della Udya Octariadi, Barry Ceasar Oky Dwi Nurhayati Rahimah, Auliyya Ria Maisya Syarifah Rizki Faizal Saputra, Deny Saputra Siregar, Alda Cendekia Sucipto Suryono Suryono Syarifah Putri Agustini Alkadri Wahyu Wahyu Yudi Qadar Pratomo