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All Journal Jurnal Informatika Polinema (JIP) Jurnal Kecerdasan Buatan dan Teknologi Informasi
Verdian, Arry
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science

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Kombinasi Matriks Perbandingan Berpasangan dan Metode Simple Additive Weighting (SAW) untuk Pemilihan Mie Instan Wantoro, Agus; Verdian, Arry
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.4881

Abstract

Mie instan merupakan makanan cepat saji yang banyak diminati masyarakat karena kemudahan dan kekepraktisan dalam pemenuhan kebutuhan pangan. Di Indonesia mie instan memiliki berbagai merk, kemasan dan varian rasa yang berbeda-beda. Salah satu varian rasa yang dimiliki semua merk yaitu rasa ayam bawang kemasan 75 gram. Meskipun mie instan banyak disukai berbagai kalangan karena rasanya yang nikmat, namun kandungan yang ada tidak direkomendasikan untuk dikonsumsi setiap hari karena dapat bedampak buruk bagi kesehatan. Kandungan yang ada pada mie instan seperti Energi (kkal), Lemak (g), Protein (g), Karbo (g), Serat (g), Gula (g), dan Natrium (mg). Berdasarkan informasi gizi pada kandungan mie instan dapat dijadikan sebagai acuan dalam memilih mie instan yang paling sehat untuk dikonsumsi. Tujuan penelitian ini melakukan perbandingan mie instan menggunakan kombinasi metode perbandingan skala prioritas berpasangan dengan metode Simple Additive Weighting (SAW). Perbandingan skala prioritas digunakan untuk memperoleh nilai pembobotan dari masing-masing kriteria. Hasil pembobotan didapatkan kriteria (C1) Energi (kkal) 34%, (C2) Lemak 2,96%, (C3) Protein 22,15%, (C4) Karbo 7,75%, (C5) Serat 9,86%, (C6) Gula 7,99%, dan (C7) Natrium 15,29%. Metode SAW digunakan untuk perhitungan perangkingan. Berdasarkan hasil perangkingan, didapatkan alternatif (A1) Supermi mendapatkan nilai sebesar 76.70, (A2) Gaga 100 sebesar 84.64, (A3) Sarimi sebesar 73,84 (A4) Mie sedap 77.67 (A5) Indomie 73,22 (A6) Lemonilo 73,94 (A7) Mie ABC 76,27, dan (A8) Nissin Ramen 75,68. Hasil perangkingan didapatkan dengan mie instan rangking tertinggi berdasarkan nilai gizi yaitu Mie Gaga 100 dan rangking terendah yaitu Indomie. Hasil penelitian ini memberikan informasi yang bermanfaat bagi masyarakat untuk mempertimbangkan dalam memilih mie instan untuk di konsumsi agar lebih aman bagi kesehatan
EVALUATION OF IMBALANCE CLASS HANDLING STRATEGIES ON MACHINE LEARNING MODEL PERFORMANCE Verdian, Arry; Wantoro, Agus
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.459

Abstract

Breast Cancer Dataset (BCD) represents a critical health problem due to the increasing prevalence of breast cancer and the importance of early detection of recurrence. Machine Learning (ML) approaches have been widely applied to support diagnosis and prediction; however, class imbalance remains a major challenge, where the majority class (“no-recurrence-events”) significantly outnumbers the minority class (“recurrence-events”). This imbalance can lead to biased models that fail to accurately detect recurrence cases. This study aims to evaluate the effectiveness of class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE) on several ML models, including Decision Tree, Naïve Bayes, k-Nearest Neighbors (k-NN), and Random Forest. The dataset used consists of 286 records with 9 features obtained from the UCI Machine Learning repository. Data preprocessing was performed, including handling missing values and outliers, followed by class balancing using SMOTE. Model evaluation was conducted using 10-fold cross-validation and performance metrics such as accuracy, precision, recall, and F1-score. The results show that the application of SMOTE significantly improves model performance, with an average accuracy increase of 11.85%. Among the evaluated models, Random Forest combined with SMOTE achieved the best performance, with an accuracy of 79.79%. In contrast, models such as Naïve Bayes and k-NN demonstrated relatively lower performance. Overall, this study confirms that handling class imbalance using SMOTE can enhance classification performance, particularly in improving the detection of minority classes in breast cancer recurrence prediction tasks.
COMPARATIVE STUDY OF CLASSIFICATION MODELS IN PROCESSING STUDENT TEST SCORES DATASETS Pramestiawan, Rico; Verdian, Arry; Bhuana, Chindu Lintang; Susanto, Lilik Joko
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.475

Abstract

The development of Machine Learning (ML) has contributed significantly to the field of education, particularly in analyzing student academic data to support data-driven decision-making. Predicting student exam results is important for identifying academic performance patterns, detecting potential failures, and improving learning interventions. However, variations in student characteristics and dataset complexity require the selection of appropriate classification models to achieve optimal prediction performance. This study aims to compare the effectiveness of several ML classification models in predicting student exam results using a student academic dataset. The dataset consists of 306 records, seven attributes, and five grade classes (A, B, C, D, and E), including attendance, quiz scores, midterm examination scores, final examination scores, and assignment scores. Data preprocessing was conducted to handle missing values, duplication, inconsistencies, and outliers. The dataset was split into training and testing data with a ratio of 75:25 and evaluated using 10-fold cross-validation. Several classification models were applied, including k-Nearest Neighbour (kNN), Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that Random Forest achieved the best performance with an accuracy of 73.9%, precision of 74.0%, recall of 73.9%, and F1-score of 73.9%, followed by Naive Bayes and Decision Tree. Meanwhile, SVM produced the lowest performance among the tested models. The findings indicate that Random Forest is the most effective method for predicting student exam results and has strong potential to support educational decision-making systems.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION (GINI DECREASE AND RELIEF-F) IN HEART DISEASE DATASET Bhuana, Chindu Lintang; Pramestiawan, Rico; Susanto, Lilik Joko; Verdian, Arry
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.477

Abstract

Heart disease remains one of the leading causes of mortality worldwide and presents a major challenge in healthcare systems. Early detection plays an essential role in improving survival rates and minimizing complications through timely intervention. Recent advances in Machine Learning (ML) have provided new opportunities for developing accurate and efficient prediction systems for heart disease detection. However, one of the major challenges in ML-based prediction is identifying the most relevant features to improve classification performance while reducing computational complexity and noise. This study aims to evaluate the effectiveness of two feature selection techniques, namely Gini Decrease (GD) and ReliefF, combined with several ML models, including Support Vector Machine (SVM), Tree, Naïve Bayes, and Random Forest, for heart disease classification. The study employed the UCI Heart Disease Dataset consisting of 303 records and 14 attributes. Data preprocessing included handling missing values using mean imputation, followed by feature selection and classification using 10-fold cross-validation with an 80:20 training-testing ratio. Experimental results showed that ReliefF outperformed GD, achieving the highest average accuracy of 0.796, compared to GD with 0.767 and all features with 0.771. The SVM model achieved the highest accuracy using GD (0.833), while Random Forest demonstrated optimal performance with ReliefF (0.817). Furthermore, the Tree model exhibited the fastest computational time among all evaluated models. These findings indicate that integrating suitable feature selection methods with ML models significantly enhances heart disease classification performance, particularly in improving predictive accuracy and computational efficiency for early medical diagnosis applications.
Co-Authors Agus Wantoro Bhuana, Chindu Lintang Pramestiawan, Rico Susanto, Lilik Joko