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All Journal Jurnal Gamma EKSAKTA: Journal of Sciences and Data Analysis JURNAL SAINS PERTANIAN EQUATOR Jurnal Ekonomi : Journal of Economic Paradigma Pro Bisnis Jurnal Dinamika Pengabdian (JDP) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Teknologi Informatika dan Komputer Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Enthusiastic : International Journal of Applied Statistics and Data Science Proceedings Series on Physical & Formal Sciences Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Blend Sains Jurnal Teknik Jurnal Informatika dan Teknologi Pendidikan Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Borneo Journal of Language and Education Innovative: Journal Of Social Science Research Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat STATISTIKA Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Medic Nutricia : Jurnal Ilmu Kesehatan Jurnal Ilmu Komunikasi dan Sosial Politik Journal of Intelligent Systems and Information Technology Jurnal Sains dan Teknologi Kesehatan Emerging Statistics and Data Science Journal Jurnal Hukum dan Ekonomi Syariah
Achmad Fauzan, Achmad
brawijaya university
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Journal : knowledge engineering and data science

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From Data to Insight: A Machine Learning Approach in Classifying Dairy Cow Productivity Level and Identifying Important Influencing Variables Fauzi, Fatkhurokhman; Fauzan, Achmad; Widiyanto, Rhendy K P; Notodiputro, Khairil Anwar; Sartono, Bagus
Knowledge Engineering and Data Science
Publisher : citeus

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Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing classification method. The novelty of this study lies in integrating model-agnostic interpretability with multiple supervised classifiers to generate transparent, data-driven insights into dairy productivity determinants. Results indicate that the top-performing methods, SVM and NN, achieved predictive accuracies ranging from 70% to 89%. Specifically, the SVM model achieved an accuracy of 0.799, a precision of 0.845, and an F1-score of 0.795, while the NN model obtained an accuracy of 0.786, a precision of 0.806, and an F1-score of 0.791. A permutational multivariate analysis of variance (PERMANOVA) on evaluation metrics revealed no statistically significant difference between the two methods. By applying PVI, nine key variables were consistently highlighted by both models as significant predictors for classifying dairy cow productivity levels (e.g., high vs. low yield) in Indonesia. These variables include farm altitude, the numbers of dairy heifers, lactating cows, and dry cows, the average duration of lactation and dry periods per cow annually, the daily amounts of forage, concentrate, and agricultural by-product feed provided per cow. These findings not only enhance model interpretability but also offer practical guidance for farm-level decision-making, the development of data-driven decision support systems, and the design of targeted policy interventions to improve dairy productivity in Indonesia, demonstrating the real-world applicability of machine-learning-based insights to strengthen dairy farm performance.
A Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) Approach for Identifying Potential Villages in Buleleng Regency Amalina, Dina Nur; Fauzan, Achmad
Knowledge Engineering and Data Science
Publisher : citeus

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Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office (BPS) for all districts and the Statistical Service Information System. The variables used in this study are based on aspects of population, communication, tourism, trade, health, religion, social affairs, and public welfare. Tuning parameters were performed to determine the optimal parameters, resulting in optimal parameters, such as minimum cluster size = five and minimum samples = 2, which produced two main clusters. The first cluster comprises six villages, while the second includes 118 villages. Additionally, a noise cluster representing outliers, consisting of 24 villages, was identified. The findings indicate that the first cluster exhibits higher village potential than the second cluster. Based on these results, it is recommended that the government prioritize the second cluster when designing and implementing targeted programs and policies to reduce poverty by developing village potential.
Co-Authors A Fahira Nur Adhar Arifuddin Adhi Pribadi Agung Dwi Ramadhan, Agung Ahmad, A. Sri Sartika Sufiina Amalina, Nur Dina Anang Kurnia Andreas Wahyu Gunawan Ariyadi, Fandy Akhmad Ayu Wulandari Bagus Sartono Bariklana, Muhammad Buana, Arya Galih Cahyani, Laras Niken Dwi Christopher Yudha Erlangga Dafrian, Rafli Danang Bagus Wibowo Dany Hilmanto Daryono Daryono, Daryono Denta, Anggeria Dewi, Sawitri Diana Apriyanti, Diana Dwitra Gusti Alriscki Efi Miftah Faridli Elsa Pudji Setiawati Evicenna Naftuchah Riani Fachry Abda El Rahman Fadliansyah, Azhimy Fauzi, Fatkhurokhman Faza Izzatul Muttaqin Fazira, Nabila Dwi Febrianti, Nur Qadri Feri Wibowo Fitri Amalia Fitri Yanti Ghefira Nurhaliza, Celine Hafidz, Syauqi Jauzza Hakim, Raden Bagus Fajriya Humairah, Nanda Lailatul Lahan Adi Purwanto LUKMAN, LUKMAN Marisi Aritonang Marisma, Murni Maulana, Ashabul Akbar Maziyah Mazza Basya Mohamad Irkham Mamungkas Munandar Munandar Nabilah, Muna Faizatun Najlah Nur Faizin Nur Kusmiyati Oktaviani, Nabila Pradana, Wahyu Aji Pratiwi, Erlina Lutfiayu Primandari, Arum Handini Purwanto, Kalis Purwanto, Muhammad Idris Rahmawati, Octavia Ramadhan, Muhammad Rizal Ramadhan, Rizki Fauzian Rantisi, Muhamad Zia Ridho Muktiadi Rinaldi Sjahril, Rinaldi Rosdiana, Siti Roza Azizah Primatika, Roza Azizah Sadat, Fauzan Safira, Nuzulia Nur Santoso, Alfian Saputra, Aditia Shenny Oktoriana Siregar, Ayu Lestari Siregar, Rania Febriyola Supriyono Supriyono Suri, Mulhamatus Latifatus Suria, Muh. Yunus Sutisna, Irwan Syahdony, Farrel Nolan Wahyudin, M. Abyan Widiyanto, Rhendy K P Widyastuti, Galuh Widyawati, Dwi Kartika Wilis Dwi Pangesti Wiranti, Ridha Yanasari, Herlinda Yuliana Watiningrum, Rahayu Yuliana, Alfa Zahidah, RA Ghina