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All Journal ILKOM Jurnal Ilmiah Jurnal Bisnis Kompetitif Bulletin of Informatics and Data Science
Fadilah, Sandi
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Computer Science & IT Electrical & Electronics Engineering Engineering Social Sciences

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Weighted Multi-Criteria Assessment of Rice Quality Using The TOPSIS Method Satria, Budy; Fadilah, Sandi
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.145

Abstract

Rice is a staple food for the Indonesian people, and its availability must be guaranteed by the government. The background of this research is based on the increasing demand for high-quality rice from consumers, thus challenging producers to set optimal rice quality standards. The process of selecting quality rice is still carried out using conventional methods in Bulog warehouses, namely by checking every rice data received by the quality control team tasked with assessing the quality of incoming rice. To overcome this problem, a decision support system is needed that can provide fair, objective, and efficient decisions. This study aims to evaluate the quality of rice from 10 alternatives using five criteria: milling degree, head grain, moisture content, broken grain, and grit grain, with a total weight of 100%. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied. This research was conducted by following a series of steps, including building a Decision Matrix, Normalizing the Decision Matrix, Calculating the Weighted Normalized Decision Matrix, Determining the Ideal Positive and Negative Solutions, Calculating the Distance to the Ideal Positive and Negative Solutions, and Calculating the Preference Score. The results of the study showed that from 10 alternative data, 5 types of rice were obtained with the highest preference values, namely Harum Solok Rice (0.8363), Anak Daro Rice (0.7955), Kuruik Kusuik Rice (0.7210), Ampek Angkek Rice (0.6919), and Saganggam Panuah Rice (0.6727). The conclusion of this study is that the application of the TOPSIS method is effective in objectively assessing rice quality. In further research, it is recommended to utilize a combination of other decision support methods to acquire new knowledge and refine preference values, as well as to develop these methods into user-friendly interfaces
ANALISIS MOTIVASI KERJA, KOMPETENSI, KETERIKATAN KERJA, DAN KINERJA GURU SERTA PEMBINA INSAN CENDEKIA BOARDING SCHOOL (ICBS) HARAU PAYAKUMBUH Yusriadi, Yusriadi; Irawan, Dika; Fadilah, Sandi
Jurnal Ekonomi Bisnis Kompetif Vol 4 No 3 (2025): aktor Penentu Minat Pembelian, Kinerja Karyawan, dan Pengelolaan Hubungan Pelangg
Publisher : Komunitas Manajemen Kompetitif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35446/bisniskompetif.v4i3.2650

Abstract

This study aims to descriptively analyze work motivation, competence, work engagement, and the performance of teachers and supervisors at Insan Cendekia Boarding School (ICBS) Harau Payakumbuh. The study employed a descriptive quantitative approach with a total of 211 respondents. Data were collected using a Likert-scale questionnaire and analyzed through descriptive statistical techniques, including mean scores and class interval categorization. The results indicate that, in general, work motivation, competence, work engagement, and the performance of teachers and supervisors fall into the good category. However, several indicators related to welfare and professional development remain in the fairly good category, particularly those concerning allowances, future security, and the production of scientific publications. These findings provide managerial implications for educational institution administrators in formulating sustainable human resource management policies.
SMOTE-Based Comparative Analysis of Machine Learning Models for Stroke Risk Prediction Using Imbalanced Healthcare Data Siregar, Ratu Mutiara; Satria, Budy; Fadilah, Sandi; Mayola, Liga; Safira, Silky
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3161.180-194

Abstract

Stroke remains one of the leading causes of mortality and long-term disability worldwide, with a significant burden in Indonesia. Early detection is crucial, as up to 90% of stroke cases are potentially preventable through timely intervention. However, predictive modeling for stroke risk is often challenged by imbalanced datasets, where non-stroke cases significantly outnumber stroke cases, potentially biasing classification models. This study aims to perform a systematic comparative evaluation of six machine learning algorithms Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for stroke risk prediction under imbalanced data conditions. The dataset consists of 5,110 patient records with 11 health-related features obtained from a publicly available healthcare dataset. Data preprocessing included anomaly removal, categorical encoding, feature scaling, and class balancing using the Synthetic Minority Oversampling Technique (SMOTE). Model evaluation was conducted using 5-fold cross-validation and assessed through accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that ensemble-based models outperform single classifiers. Random Forest achieved the highest mean accuracy of 97.12% (±0.42) with an F1-score of 0.96, followed closely by XGBoost with 96.85% (±0.51). Both models also exhibited superior recall performance, indicating improved minority class detection. The novelty of this study lies in the systematic evaluation of multiple machine learning models using SMOTE-based balancing and cross-validation on publicly available healthcare data, providing robust comparative insights for imbalanced medical classification problems.
Co-Authors Budy Satria Irawan, Dika Liga Mayola Ratu Mutiara Siregar safira, silky Yusriadi Yusriadi