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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Information System Journal (INFOS) Intellect : Indonesian Journal of Learning and Technological Innovation
Donni Prabowo, Donni
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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IMPLEMENTATION OF MOORA METHOD FOR DECISION SUPPORT SYSTEM SCHOLARSHIP SELECTION IN SMK MUHAMMADIYAH PRAMBANAN Perdana, Dinar Abdi; Prabowo, Donni; Sari, Bety Wulan
Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i1.2261

Abstract

Decision Support System for Scholarship Selection at SMK Muhammadiyah Prambanan Using the MOORA Method aims to implement the Multi-Objective Optimization method on the basis of Ration Analysis. In determining scholarship recipients based on predetermined criteria and building a system in the form of a website to help provide alternative decisions in determining the acceptance of scholarships at SMK Muhammadiyah Prambanan. Based on the source of the data obtained, using primary data including interview and observation methods supported by secondary data obtained by literature studies that are relevant to the problem. Scholarship data is calculated and then ranked based on the final value generated from the MOORA calculation. The process of scholarships selection is based on criteria including report card grades, dependents of parents, the income of parents, percentage of attendance, and the number of siblings. The results of this study are the Scholarship Selection Decision Support System Using the MOORA Method, where the final value in the form of an alternative that has the greatest preference value will be placed at the top rank. The alternative will be a recommendation to receive a scholarship.
IMPLEMENTATION OF PROFILE MATCHING METHOD FOR THE BEST EMPLOYEE SELECTION SYSTEM PT. JENDELA DIGITAL INDONESIA Prabowo, Donni
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.2464

Abstract

The implementation of profile matching method in selecting the best employees at PT Jendela Digital Indonesia aims to assist managers in making decisions with the right calculations and criteria. Employees are one of the factors that play an important role in advancing the company. Employee performance affects the company in obtaining profits. To spur employee performance, the company selects the best employees every period by giving appreciation and bonuses to selected employees. This selection system using three criteria, namely aspects of cooperation, work performance, and personality. These criteria will be used for calculations using the Profile Matching method. There are five employees who will be submitted to the selection of the best employees in this company. All criteria are given a GAP value and then will provide a ranking. The largest final score will be at the top of the ranking, followed by a smaller final score. The results of this research show that this method can provide results that assist managers in making decisions about the best employees according to the criteria desired by the company.
Implementation of Smarter Method for Prospective Student Council Selection System SMK Negeri 1 Rembang Sari, Bety Wulan; Prabowo, Donni; Lestari, Wahyu Puji
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4591

Abstract

One of the schools that has attempted to make the student council active and the primary platform for student development to encourage student activities at school is SMK Negeri 1 Rembang. OSIS administrators can execute numerous labor programs in both academic and non-academic domains. Participants must pass several selection processes to join the SMK Negeri 1 Rembang OSIS board. This student council board's election procedure still employs manual methods. The selection procedure may take longer and allow for subjective evaluations depending on the number of candidates and the criteria used. As a result, it is essential to develop a decision support system (SPK) that uses Rank Order Centroid (ROC) weighting and the Simple Multi-Attribute Rating Technique Exploiting Rank (SMARTER) method to help choose student council administrators. The SMARTER technique addressed disproportionality because the weights assigned do not provide a hierarchy or order of importance between the current criteria and their sub-criteria. Based on the computation of the final value of the standards and sub-criteria on each alternative, the system produces results in the form of the biggest to most minor order. Blackbox testing of this program demonstrates that it can operate and be used at SMK N 1 Rembang both in terms of functionality and outcomes from the system.
SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT KUCING KAMPUNG DENGAN MENGGUNAKAN METODE CERTAINTY FACTOR Irawan , Mikhael Alexander Andy; Prabowo, Donni; Sari, Bety Wulan; Laksono, Aziz Catur
Information System Journal Vol. 7 No. 02 (2024): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2024v7i02.1915

Abstract

Kucing kampung merupakan hewan peliharaan yang umum di Indonesia, namun kesehatan mereka sering diabaikan, menyebabkan berbagai penyakit. Penelitian ini bertujuan merancang sistem pakar berbasis website menggunakan metode certainty factor untuk mendiagnosa penyakit pada kucing kampung. Sistem ini mengumpulkan data dari pakar dan literatur medis untuk menganalisis gejala dan memberikan solusi yang tepat. Metode certainty factor digunakan untuk menghitung tingkat kepastian diagnosis berdasarkan data pengguna dan basis pengetahuan. Hasil penelitian menunjukkan bahwa sistem ini dapat memberikan diagnosis dengan baik, disertai solusi perawatan dan informasi penyakit lain. Sistem pakar ini memberikan kontribusi dengan menawarkan alternatif akses informasi diagnosa penyakit kucing kampung yang praktis dan efisien bagi masyarakat. Pengujian menunjukkan sistem berjalan sesuai kebutuhan, menjadikannya sistem pakar ini bermanfaat untuk pemelihara kucing kampung
Analisis Perbandingan Prediksi Harga Rumah Dengan Random Forest, Gradient Boosting, dan XGBoost Wulan Sari, Bety; Prabowo, Donni
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 1 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i1.1385

Abstract

House price prediction poses a significant challenge in the property sector, especially in the Yogyakarta region, which exhibits a wide range of price variations. This study aims to compare the performance of three regression algorithms such as Random Forest, Gradient Boosting, and XGBoost, in building predictive models based on features such as land area, building area, number of bedrooms, bathrooms, and garage availability. The dataset analyzed consists of 1,642 entries, with house prices ranging from IDR 7 million to IDR 4.37 billion, an average price of IDR 1.14 billion, and a mode of IDR 775 million. Model evaluation was conducted using Mean Squared Error (MSE) and the coefficient of determination (R²), where XGBoost achieved the best performance with an MSE of 1.56 × 10¹⁴ IDR², an R² of 0.7746, and a Root Mean Squared Error (RMSE) of approximately IDR 12.5 million. These results indicate that XGBoost outperforms the other two models in handling complex tabular data and provides more accurate predictions. The predictive model has practical potential to be utilized by property developers, real estate agents, and local governments as a decision-support tool for price estimation, market evaluation, and data-driven urban planning. These findings highlight that selecting the appropriate algorithm can significantly enhance the quality of house price prediction. Abstrak Prediksi harga rumah menjadi tantangan penting dalam bidang properti, khususnya di wilayah Yogyakarta yang memiliki variasi harga cukup ekstrem. Penelitian ini bertujuan untuk membandingkan performa tiga algoritma regresi yaitu Random Forest, Gradient Boosting, dan XGBoost digunakan untuk membangun model prediksi harga rumah berdasarkan fitur seperti luas tanah, luas bangunan, jumlah kamar tidur, kamar mandi, dan garasi. Data yang dianalisis mencakup 1.642 entri dengan harga rumah berkisar antara Rp 7 juta hingga Rp 4,37 miliar, harga rata-rata sebesar Rp 1,14 miliar, dan modus Rp 775 juta. Evaluasi model dilakukan menggunakan metrik Mean Squared Error (MSE) dan koefisien determinasi (R²), di mana XGBoost menghasilkan performa terbaik dengan MSE sebesar 1,56 × 10¹⁴ rupiah², R² sebesar 0,7746, dan Root Mean Squared Error (RMSE) sekitar 12,5 juta rupiah. Hasil ini menunjukkan bahwa XGBoost lebih unggul dalam menangani data tabular kompleks dan memiliki akurasi prediksi yang lebih baik dibanding dua model lainnya. Model prediktif ini berpotensi digunakan oleh pengembang properti, agen real estate, maupun pemerintah daerah sebagai alat bantu dalam penetapan harga, evaluasi pasar, dan perencanaan tata ruang yang berbasis data. Temuan ini memberikan gambaran bahwa pemilihan algoritma yang tepat dapat meningkatkan kualitas prediksi harga properti.
Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Prabowo, Donni; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
Transfer Learning-Based Convolutional Neural Network for Accurate Detection of Rice Leaf Disease in Precision Agriculture Sari, Bety Wulan; Prabowo, Donni; Pristyanto, Yoga; Aminuddin, Afrig
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.420-432

Abstract

Background: Traditional approaches to rice disease identification depend mainly upon visual examination, which is quite labor-intensive and generally demands a certain skill level from people engaged in this activity. However, these approaches suffer from high time costs and potential errors and are impractical for large-scale daily monitoring. The recent rise of deep learning has offered opportunities for automated detection process improvement, which needs to be fast-accurate as good farmer-centric.   Objective: This study aims to enhance the accuracy of image rice leaf disease classification via feature extraction for rice leaf disease in four instances of pre-trained CNN models and provide an automated solution for early detection ahead of timely care by obtaining insights into crop production through precision agriculture. Methods: This study combined transfer learning with four pre-trained CNN models - InceptionResNetV2, MobileNetV2, DenseNet121, and VGG16. Results: The outcome of this research enables the identification of the optimal model to relate datasets where DenseNet121 achieved the highest accuracy, i.e. 99.10%, followed by MobileNetV2, having a precision of 97.10%. Conclusion: The new framework results in a highly accurate and high-throughput early disease detection element in precision agriculture, better than state-of-the-art approaches based on traditional techniques. Keywords: Deep Learning, DenseNet121, Image Classification, Rice Leaf Diseases, Transfer Learning
Co-Authors Afrig Aminuddin Bety Wulan Sari Bety Wulan Sari, Bety Wulan Irawan , Mikhael Alexander Andy Laksono, Aziz Catur Perdana, Dinar Abdi Wahyu Puji Lestari, Wahyu Puji Yoga Pristyanto