Article Per Year (5 Year)
p-Index From 2021 - 2026
8.849
P-Index
This Author published in this journals
All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Zero : Jurnal Sains, Matematika, dan Terapan MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Kusman Sadik
Program In Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

Published : 83 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 83 Documents
Search

 5   6   7   8   9 
Deteksi Polycystic Ovary Syndrome (PCOS) Berbasis Machine Learning: Kombinasi SMOTE, Random Forest, Gradient Boosting, dan Bayesian Optimization Naufalia Alfiryal; Kusman Sadik; Cici Suhaeni; Agus Mohamad Soleh
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.109931

Abstract

Polycystic ovary syndrome (PCOS) merupakan gangguan endokrin yang umum terjadi pada wanita usia reproduktif. Kondisi ini dapat menyebabkan gangguan ovulasi, ketidakseimbangan hormon, resistensi insulin, serta meningkatkan risiko penyakit kardiovaskular, obesitas, dan gangguan psikologis. Meskipun prevalensinya cukup tinggi, sekitar 75% kasus PCOS masih belum terdiagnosis dalam praktik klinis akibat kompleksitas gejala dan keterbatasan metode diagnosis yang digunakan saat ini. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan pendekatan berbasis machine learning guna meningkatkan akurasi dan efisiensi deteksi PCOS. Penelitian ini membandingkan performa dua algoritma pembelajaran terawasi, yaitu random forest dan gradient boosting, dalam melakukan prediksi PCOS. Dataset yang digunakan diperoleh dari repositori publik dan memuat berbagai fitur klinis yang berkaitan dengan PCOS. Untuk menangani permasalahan ketidakseimbangan kelas, metode synthetic minority over-sampling technique (SMOTE) diterapkan pada data pelatihan. Selain itu, bayesian optimization digunakan untuk melakukan penyetelan hiperparameter pada masing-masing model agar diperoleh performa yang optimal. Evaluasi performa model dilakukan menggunakan beberapa metrik, dengan area under the curve–receiver operating characteristic (AUC-ROC) sebagai metrik utama. Hasil penelitian menunjukkan bahwa model Gradient Boosting memberikan performa terbaik dengan nilai AUC sebesar 0,8983 dan nilai recall sebesar 0,95, yang mengindikasikan sensitivitas tinggi dalam mengidentifikasi kasus PCOS. Temuan ini menunjukkan bahwa kombinasi SMOTE dan bayesian optimization efektif dalam meningkatkan akurasi prediksi, khususnya pada dataset medis yang tidak seimbang. Pendekatan yang diusulkan memiliki potensi untuk diintegrasikan ke dalam sistem pendukung keputusan klinis guna mendukung proses skrining PCOS yang lebih dini dan andal.Polycystic ovary syndrome (PCOS) is a common endocrine disorder among reproductive-aged women. This condition can lead to ovulatory dysfunction, hormonal imbalance, insulin resistance, and an increased risk of cardiovascular disease, obesity, and psychological disorders. Despite its high prevalence, approximately 75% of PCOS cases remain undiagnosed in clinical settings due to the complexity of symptoms and limitations of current diagnostic methods. To address this issue, a machine learning-based approach is proposed to improve the accuracy and efficiency of PCOS detection. This study compares the performance of two supervised learning algorithms random forest and gradient boosting for PCOS prediction. The dataset used was obtained from a public repository and contains various clinical features associated with PCOS. To address the class imbalance problem, the synthetic minority over-sampling technique (SMOTE) was applied to the training data. Additionally, bayesian optimization was employed to fine-tune the hyperparameters of each model for optimal performance. Model performance was evaluated using several metrics, with the area under the curve–receiver operating characteristic (AUC-ROC) as the primary measure. The Gradient Boosting model achieved the best results, with an AUC of 0.8983 and a recall of 0.95, indicating high sensitivity in identifying positive PCOS cases. These findings demonstrate that the combination of SMOTE and Bayesian Optimization is effective in enhancing predictive accuracy, especially in imbalanced medical datasets. The proposed approach shows promise for integration into clinical decision-support systems to facilitate earlier and more reliable PCOS screening.Kata Kunci: Bayesian optimization; gradient boosting; PCOS; random forest; SMOTE.Keywords : Bayesian optimization; gradient boosting; PCOS; random forest; SMOTE.
Bayesian Neural Network untuk Prediksi Diabetes: Uncertainty Quantification dalam Machine Learning Sabrina Adnin Kamila; Kusman Sadik; Cici Suhaeni; Agus Mohamad Soleh
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.103994

Abstract

Penelitian ini bertujuan mengevaluasi dan membandingkan kinerja tiga model machine learning, yaitu random forest (RF), feedforward neural network (FNN), dan bayesian neural network (BNN), dalam klasifikasi diabetes menggunakan Diabetes Health Indicators Dataset dari UCI Machine Learning Repository yang memiliki ketidakseimbangan kelas. Prapemrosesan data meliputi normalisasi fitur menggunakan StandardScaler dan penanganan ketidakseimbangan kelas dengan synthetic minority over-sampling technique (SMOTE). Evaluasi model dilakukan menggunakan metrik akurasi dan skor F1, yang didukung oleh classification report dan confusion matrix. Hasil evaluasi menunjukkan bahwa RF menghasilkan akurasi tinggi (0,8493) namun skor F1 yang rendah (0,3386), yang mengindikasikan rendahnya sensitivitas model terhadap kasus positif diabetes. FNN memberikan performa yang lebih seimbang dengan skor F1 sebesar 0,4490 setelah penyesuaian threshold optimal. Sementara itu, BNN mencapai akurasi 0,8498 dan skor F1 sebesar 0,4043, serta memiliki keunggulan tambahan berupa kemampuan mengukur ketidakpastian prediksi melalui pendekatan Monte Carlo Dropout. Dengan demikian, FNN lebih unggul dalam keseimbangan klasifikasi, sementara BNN lebih relevan untuk aplikasi medis yang membutuhkan informasi tingkat kepercayaan prediksi guna mendukung pengambilan keputusan klinis yang lebih andal.This study aims to evaluate and compare the performance of three machine learning models, namely random forest (RF), feedforward neural network (FNN), and bayesian neural network (BNN), for diabetes classification using the Diabetes Health Indicators Dataset from the UCI Machine Learning Repository, which exhibits significant class imbalance. Data preprocessing includes feature normalization using StandardScaler and class imbalance handling through synthetic minority over-sampling technique (SMOTE). Model performance is evaluated using accuracy and F1-score metrics, supported by classification report and confusion matrix analysis. The results show that RF achieves high accuracy (0.8493) but a low F1-score (0.3386), indicating poor sensitivity to positive diabetes cases. FNN provides more balanced performance with an F1-score of 0.4490 after optimal threshold adjustment. Meanwhile, BNN achieves an accuracy of 0.8498 and F1-score of 0.4043, while offering the additional advantage of uncertainty quantification through Monte Carlo Dropout. Therefore, FNN is more effective for balanced classification performance, while BNN is more suitable for medical applications that require prediction confidence information to support more reliable and informed clinical decision-making.Kata Kunci: Prediksi diabetes, kuantifikasi ketidakpastian, bayesian neural network, classification imbalance, machine learning.Keywords: Diabetes prediction, uncertainty quantification, bayesian neural network, classification imbalance, machine learning.
EVALUASI MODEL KLASIFIKASI DALAM DETEKSI PENIPUAN TRANSAKSI: STUDI KASUS PADA DATA TIDAK SEIMBANG Jefita Resti Sari; Kusman Sadik; Agus M. Soleh; Cici Suhaeni
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.565-576

Abstract

The rise in digital transactions increases the risk of credit card fraud, highlighting the need for a smart and accurate detection system. This study aims to develop a classification model that can effectively detect fraudulent transactions, despite data imbalance challenges. Data processing involves the use of the SMOTE technique to improve the representation of minority classes and Optuna for hyperparameter tuning. Three machine learning models are applied: Logistic Regression, Random Forest, and XGBoost. Model performance is evaluated using precision, recall, f1-score, and ROC-AUC. The results show that Random Forest achieves the best performance, with a precision of 0.91, recall of 0.74, and f1-score of 0.82. Logistic Regression achieves high recall but very low precision, while XGBoost produces a competitive AUC but a lower f1-score than Random Forest. This research highlights the importance of algorithm selection, data balancing with SMOTE, and parameter tuning to build an effective and adaptive fraud detection system for imbalanced data
DETECTION OF ADULTERATION IN COCONUT MILK USING CUCKOO SEARCH-OPTIMIZED XGBOOST ON HIGH-DIMENSIONAL FTIR SPECTRAL DATA I Gusti Ngurah Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.8376

Abstract

Coconut milk adulteration is an important issue because it can reduce food quality and endanger consumers. This study aims to develop a rapid and accurate detection method for coconut milk adulteration using a combination of FTIR spectroscopy technology and the XGBoost machine learning algorithm optimized with the Cuckoo Search Algorithm (CSA). FTIR spectral data from traditional and instant coconut milk samples were analyzed using Standard Normal Variate (SNV) and Savitzky-Golay (SG) preprocessing to reduce noise and clarify spectral features. The XGBoost model was then optimized through CSA with hyperparameter tuning. The results showed that the combination of SNV+SG preprocessing increased the model accuracy by 84.44%, with a precision of 92.73% and an F1-score of 79.94%. In addition, CSA optimization provided a 19.7% increase in accuracy compared to the model without tuning. These findings prove the effectiveness of the CSA-XGBoost approach in analyzing high-dimensional spectral data and is a potential solution in efficiently detecting the authenticity of coconut milk. In conclusion, this approach has the potential to be widely applied to test the authenticity of other food products quickly, non-destructively and accurately.
Perbandingan Kinerja Algoritma Cheng and Church dan Qualitative Biclustering pada Data Produksi Perikanan Budidaya Indonesia Dewi Zulyani Pomalingo; Muhammad Nur Aidi; Kusman Sadik
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39884

Abstract

Biclustering is a two-dimensional clustering approach that simultaneously identifies relationships between objects and variables, making it more suitable than conventional clustering methods for analyzing data with local patterns. Although numerous biclustering algorithms have been developed, comparative studies evaluating their performance on official statistical data remain limited. This study aimed to evaluate the performance of the Cheng and Church (CC) and Qualitative Biclustering (QUBIC) algorithms using Indonesia’s aquaculture production data for 2023. The dataset comprised the production of 14 aquaculture commodities across 34 provinces. Optimal parameters for both algorithms were determined through a manual tuning procedure. Their performance was subsequently evaluated using the Mean Squared Residue (MSR), Average Squared Residue (ASR), and the Liu–Wang index. The results showed that the optimal parameter for the CC algorithm was δ = 0.009, while the optimal QUBIC parameters were r = 1, q = 0.06, and c = 0.75. CC produced 7 biclusters with an ASR of 0.0052, whereas QUBIC produced 8 biclusters with an ASR of 0.4360. The lower MSR and ASR values of CC compared with QUBIC indicate that CC was more effective in forming homogeneous biclusters in aquaculture production data. The number of province memberships showed that both algorithms were able to capture patterns across most provinces, although CC had slightly broader coverage. The Liu and Wang index value of 0.1951 indicated a relatively low similarity between the bicluster membership structures produced by CC and QUBIC. Thus, although both algorithms were applied to the same aquaculture production data, CC and QUBIC tended to produce different clustering structures.
PERFORMANCE COMPARISON OF MISSFOREST AND MICE IN HANDLING MISSING CATEGORICAL DATA Nurhidayah Nurhidayah; Kusman Sadik; Aji Hamim Wigena
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3271-3282

Abstract

Missing data represent a common challenge in statistical modeling and can substantially reduce the performance of classification algorithms. This study examines the impact of missing values on the performance of the XGBoost model by considering different proportions of missingness (50% and 75%) and various combinations of affected variables under the Missing Completely at Random (MCAR) and Missing at Random (MAR) mechanisms. Two imputation methods, MissForest and Multiple imputation by chained equations (MICE), were compared with a baseline model without imputation. The analysis of variance revealed that the interaction between imputation method, missing data proportion, and variable combinations had a significant effect on accuracy and specificity, while sensitivity remained relatively stable across scenarios. Tukey tests confirmed that MissForest consistently outperformed the other approaches, producing the highest accuracy and specificity, especially at 50% missingness with three variables affected. Moreover, the evaluation of categorical distributions before and after imputation indicated that MissForest better preserved category balance compared to MICE. These findings highlight that the performance of imputation methods strongly depends on the characteristics of missing data. Overall, MissForest demonstrated clear superiority in handling missing categorical data, maintaining distributional integrity while enhancing the classification performance of the XGBoost model. This study advances statistical learning by giving empirical evidence and practical tips for choosing strong imputation strategies in categorical datasets. This improves the reliability of predictive modeling when data is missing.
THREEFOLD HIERARCHICAL SMALL AREA ESTIMATION MODEL FOR ESTIMATING PREVALENCE OF STUNTING IN WEST NUSA TENGGARA Logananta Puja Kusuma; Kusman Sadik; Anang Kurnia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3341-3354

Abstract

Stunting is a condition of growth failure in toddlers due to chronic malnutrition, becoming an issue in various regions of Indonesia. West Nusa Tenggara Province has one of Indonesia's highest stunting prevalence rates, calculated at 29.8% in 2024, which is more than twice the national target of 14%. Appropriate and efficient policy-making for stunting reduction requires reliable estimates for small areas like districts. This study aims to produce more reliable district-level estimates of stunting prevalence for districts level area, while direct estimation from the Survei Kesehatan Indonesia (SKI) is unreliable due to limited sample size. This research develops a threefold hierarchical Bayesian (HB) Small Area Estimation (SAE) model based on the Poisson-Gamma distribution to model the number of stunted toddlers as discrete count data with overdispersion. The proposed model incorporates auxiliary variables from official sources and includes three hierarchical random effects representing district, regency/municipality, and grouped regency/municipality levels based on geographical structure. The results show that the threefold HB SAE model achieves convergent parameter estimates and provides more stable and precise district-level stunting prevalence estimates compared to direct estimators. The multilevel model performs better than one random effect model as reflected by the lower LOOIC. The findings also suggest that districts in Sumbawa Island, particularly in the eastern part and areas located farther from regency/municipality capitals, tend to have higher stunting prevalence. However, this study is limited by the assumption of independence among area-level random effects and restricted availability of auxiliary variables. This study contributes methodologically by extending Poisson-based SAE literature through the application of a threefold HB framework in stunting estimation and provides district-level stunting statistics that support evidence-based policymaking and targeted interventions aligned with SDG Target 2.2.
On the Use of Zero-Inflated Mixed Models for Count Data: A Simulation and Empirical Evidence Anang Kurnia; Zafira Fakhriyah; Kusman Sadik; Dian Handayani
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1303

Abstract

This paper evaluates the performance of classical count regression models (Poisson, Negative Binomial, Generalized Poisson), zero-inflated models (Zero-Inflated Poisson/ZIP, Zero-Inflated Negative Binomial/ZINB, Zero-Inflated Generalized Poisson/ZIGP), and zero-inflated mixed models (ZIPMM, ZINBMM, ZIGPMM) for over-dispersed count data, particularly due to excess zeros and unobserved heterogeneity. Using simulation and empirical studies, we evaluated the performance of the models based on their predictive capability and their ability to yield valid inferences through hypothesis testing. The simulation, replicated 1000 times, involves 27 scenarios that combine various sample sizes, proportions of zero counts, and response variable distributions. Our findings indicate that ZIGPMM and ZINBMM provide the smallest root mean square error (RMSE) values. Although the Poisson model yields a relatively small RMSE, it does not adequately account for overdispersion, leading to underestimated standard errors and potentially misleading significance tests. The negative binomial model yields dispersion estimates closest to 1, indicating good performance, whereas ZIGP, ZINB, ZIGPMM, and ZINBMM perform better when zero counts are extremely high. Empirical analysis of data on under-five mortality due to pneumonia in Java Island, Indonesia, indicates that ZINB, ZINBMM, and ZIGPMM have the smallest Akaike Information Criterion (AIC), making them the most suitable models. These models show that exclusive breastfeeding and vitamin A have no significant effect on under-five child mortality due to pneumonia, while severe malnutrition has a statistically significant impact (α=0.05).
Performance Comparison of Random Forest and XGBoost Optimized with Cuckoo Search Algorithm for Coconut Milk Adulteration Detection Using FTIR Spectroscopy Sentana Putra I Gusti Ngurah; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.7817

Abstract

Coconut milk has emerged as a strategic food commodity in the global tropical region, with market demand growing at 7.2% per annum since 2021. This increasing demand has led to sophisticated adulteration practices, including dilution with water. Such adulteration not only reduces the nutritional value but also poses serious health risks, including food poisoning and allergic reactions. This study developed an innovative detection method combining Fourier Transform Infrared (FTIR) spectroscopy with a sophisticated machine learning algorithm. We analyzed 719 coconut milk samples (wavelength range 2500-4000 nm) consisting of traditional market products and instant commercial products. This study aims to develop an FTIR-based coconut milk adulteration detection model by optimizing RF and XGBoost parameters using CSA and evaluating the comparative performance of the two models in identifying different types of adulterants. The spectral data underwent rigorous preprocessing using a combination of Standard Normal Variate (SNV) and Savitzky-Golay (SG) techniques to overcome the effects of noise and light scattering, which significantly improved feature extraction. The results show that CSA-optimized XGBoost achieves superior performance with 92% accuracy and 91% F1 score, outperforming Random Forest in all evaluation metrics. The model shows particular strength in precision (98%), indicating its outstanding ability to minimize false positives in adulteration detection. Stability tests through 30 experimental repetitions reveal that the combination of XGBoost+CSA maintains consistent performance with minimal variance, confirming its reliability for industrial applications. Comparative analysis shows that the combination of SNV+SG preprocessing improves the accuracy of the baseline model by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This research makes significant contributions to food science and safety. This study demonstrates the effectiveness of CSA in optimizing spectroscopic models, achieving 19.5% higher precision. The combination of SNV+SG preprocessing improves the baseline accuracy by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This study not only provides a rapid and non-destructive adulteration detection solution but also proves the effectiveness of the CSA approach in optimizing the spectroscopic model. These findings have important implications for strengthening food safety regulations and developing real-time quality control systems in the coconut milk industry.
Effect of Feature Normalization and Distance Metrics on K-Nearest Neighbors Performance for Diabetes Disease Classification Muhammad Yusran; Kusman Sadik; Agus M Soleh; Cici Suhaeni
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8012

Abstract

Diabetes is a global health issue with a steadily increasing prevalence each year. Early detection of the disease is an important step in preventing severe complications. The K-Nearest Neighbors (KNN) algorithm is often used in disease classification, but its performance is highly influenced by the choice of normalization method and distance metric used. This study aims to evaluate the effect of various normalization methods and distance metrics on the performance of the KNN algorithm in diabetes disease classification. The three normalization methods were employed: z-score normalization, min-max scaling, and median absolute deviation (MAD). In addition, the seven distance metrics were assessed: Euclidean, Manhattan, Chebyshev, Canberra, Hassanat, Lorentzian, and Clark. The dataset used is Pima Indians Diabetes which consists of 768 observations and 8 features. The data were split into 80% training data and 20% test data, and using 5-fold cross-validation to determine the optimal k value. The results show that the MAD-Canberra combination produces the highest overall accuracy, recall, and F1-score of 87.32%, 82.33%, and 81.94%, respectively. The highest precision was obtained from the Baseline-Hassanat combination at 86.96%, while the lowest performance was observed for the Z-Score-Chebyshev combination with F1-Score 58.02%. These results highlight that no single combination universally outperforms others, underscoring the need for empirical evaluation. Nonetheless, combining MAD normalization with metrics such as Canberra or Hassanat can serve as a strong starting point for developing KNN-based classification systems, especially in medical contexts that are sensitive to misclassification.
Co-Authors . Erfiani A.Tuti Rumiati Aam Alamudi ACHMAD FAUZAN Adib Roisilmi Abdullah Agus M Soleh Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Ahmad Rifai Nasution Aji Hamim Wigena Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Akmala Firdausi Amalia, Rahmatin Nur Anadra, Rahmi Ananda Shafira Anang Kurnia Andespa, Reyuli Andi Okta Fengki ASEP SAEFUDDIN Astari, Reka Agustia Astari, Reka Agustia Asysta Amalia Pasaribu Aulya Permatasari Azka Ubaidillah Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Budi Susetyo Cici Suhaeni Cici Suhaeni Cici Suhaeni Dewi Zulyani Pomalingo Dhita Elsha Pangestika Dian Handayani Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eriski Isnanda Erwan Setiawan Evita Purnaningrum Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Fradha Intan Arassah Freya, Wa Ode Rona Gede Awantara Gerry Alfa Dito Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Iqbal, Teuku Achmad Jefita Resti Sari Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma Logananta Puja Kusuma Lukmanul Hakim M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Nur Aidi Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Naufalia Alfiryal Nisrina Az-Zahra, Putri Nur Khamidah Nurhidayah Nurhidayah Nusar Hajarisman Panji Lokajaya Arifa Parwati Sofan Purnama Sari Rifqi Aulya Rahman Rita Rahmawati Rizaldi Boer Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sabrina Adnin Kamila Sahamony, Nur Fitriyani Satriyo Wibowo Sentana Putra I Gusti Ngurah Siregar, Jodi jhouranda Siti Aisyah SITI NURADILLA Siti Raudlah Sitti Nurhaliza Soleh, Agus M Sri Kustiara Suhaeni, Cici Sundari, Marta Supriatin, Febriyani Eka Tendi Ferdian Diputra Titin Suhartini Titin Suhartini, Titin Tri Wahyuni Uswatun Hasanah Utami Dyah Syafitri Viarti Eminita Widhiyanti Nugraheni Yenni Angraini Yenni Kurniawati Yuli Eka Putri Zafira Fakhriyah