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All Journal Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) bit-Tech International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pengabdian kepada Masyarakat Multica Science and Technology Indonesian Journal of Networking and Security - IJNS Malcom: Indonesian Journal of Machine Learning and Computer Science Kapas: Kumpulan Artikel Pengabdian Masyarakat SmartComp E-Amal: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Indonesia
Joang Ipmawati
Universitas Nahdlatul Ulama Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Physics Public Health Social Sciences Other

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Journal : bit-tech

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Analisis Status Gizi Anak Menggunakan Metode Klastering pada Dataset Anthropometri Joang Ipmawati; Ilham Unggara
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1869

Abstract

Stunting merupakan salah satu masalah kesehatan masyarakat yang serius di Indonesia, memengaruhi pertumbuhan fisik dan kognitif anak-anak. Penelitian ini bertujuan untuk mengelompokkan status gizi anak-anak berdasarkan data antropometri untuk mengidentifikasi kelompok risiko tinggi stunting. Dataset yang digunakan terdiri dari 120.000 entri dengan variabel umur (bulan), tinggi badan (cm), jenis kelamin, dan status gizi, yang diperoleh dari sumber sekunder. Metode penelitian menggunakan algoritma K-Means untuk klasterisasi data dengan jumlah klaster optimal ditentukan melalui metode Elbow dan Silhouette Score. Proses analisis melibatkan tahap preprocessing, klasterisasi, dan validasi serta evaluasi eksternal. Hasil penelitian menunjukkan bahwa data dapat dikelompokkan ke dalam empat klaster dengan karakteristik yang berbeda. Klaster 0 dan Klaster 2 didominasi oleh anak usia muda (0–35 bulan) dengan rata-rata tinggi badan masing-masing 74.34 cm dan 73.13 cm, yang mencerminkan kelompok risiko tinggi stunting. Sebaliknya, Klaster 1 dan Klaster 3 mencakup anak-anak dengan pertumbuhan optimal, dengan rata-rata tinggi badan di atas 100 cm. Analisis korelasi menunjukkan hubungan signifikan antara tinggi badan, umur, dan status gizi, mendukung pentingnya intervensi gizi pada kelompok risiko tinggi. Penelitian ini memberikan kontribusi penting dalam upaya pencegahan stunting melalui identifikasi kelompok risiko tinggi secara lebih terarah. Temuan ini relevan untuk mendukung program kesehatan masyarakat di Indonesia, khususnya dalam merancang intervensi berbasis data untuk meningkatkan status gizi anak-anak. Pendekatan klasterisasi berbasis machine learning yang digunakan membuktikan efektifitasnya dalam memetakan pola pertumbuhan anak, sehingga dapat digunakan untuk mendukung perencanaan kebijakan yang lebih efisien.
An An Explainable Machine Learning Approach Using Random Forest and SHAP for Employee Attrition Prediction Ipmawati, Joang; Kusnawi, Kusnawi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3281

Abstract

Understanding and predicting employee attrition is a strategic challenge for modern organizations because high turnover rates impact operational costs, productivity, and the loss of valuable company knowledge. Conventional statistical approaches, such as logistic regression, have limitations in capturing complex and non-linear relationships between workforce variables. This study proposes an Explainable Machine Learning approach by integrating the Random Forest algorithm and the SHAP (SHapley Additive Explanations) method to predict and interpret employee attrition behavior more transparently.  However, existing HR analytics research rarely combines tree-based ensemble models with robust explainability, creating a gap in developing accurate yet interpretable solutions.The dataset used is HR-employee-attrition, with 1,470 entries and 35 features covering demographics, compensation, and job satisfaction. After preprocessing and parameter optimization, the Random Forest model achieved 83% accuracy, an ROC-AUC of 0.789, and a PR-AUC of 0.414. Model performance was validated through a 70:30 stratified split supported by cross-validation to ensure predictive consistency, indicating good classification performance despite class imbalance. SHAP analysis identified five key features influencing attrition: OverTime, MonthlyIncome, Age, YearsAtCompany, and JobSatisfaction. Unlike conventional black-box models, the proposed approach provides global and local explanations that clarify the contribution of each feature to individual predictions. Practically, these insights enable HR departments to identify high-risk employees earlier and design targeted retention interventions based on data-driven evidence.The findings demonstrate that integrating Random Forest with SHAP produces models that are both accurate and interpretable. Future research may explore integrating SHAP explanations into interactive HR decision-support systems and evaluating more advanced explainable deep learning methods.
Co-Authors - STMIK AMIKOM Yogyakarta, Emha Taufiq Luthfi A, Ferry Wahyu Abdulloh, Ferian Fauzi Adelia Octora Pristi Sahida Adelia Octora Pristisahida Adha, Munirah Fatimah Aditya Wahyu Pratama Afrianto, Novan Afrig Aminuddin Ahmad Asyhari Akhmad Fakhurrozi Alif Yudha Suryatama Aristianda, Hedry Asadulloh, Bima Pramudya Aulia, Kunni Rofiqotul Azzahro, Marissa Bledug Kusuma Diyon Saputro Elsa Virantika Febrianto, Wimar Ferry Wahyu A Hedry Aristianda Heni Pujiastuti Hidayat, Prasetyo Nur Hikmah, Harisna I Nyoman Apraz Ramatryana Ihwan Mualana Ilham Unggara Irwan Novianto Jumanur Rohman Kusnawi Kusnawi Kusrini . Laksana, Feri Febria Lilis Kurniasari M Andika Fadhil Eka Putra M Rifki Ramdani Majid Rahardi Millaty, Marosimy Mualana, Ihwan Muhamad Adhitya Saputra Muhamad Nasruddin Manaf Ngulum, Mochamad Bahrul Novan Afrianto Nur Azmi Ainul Bashir NURUL FAIZAH P, Wigar Kumara Prasetyo Nur Hidayat Puji Prabowo, Dwi Ramdani, M Rifki Restiadi Bayu Taruno Rifqi Syarif Nasrulloh Rio Ardiansyah saifulloh Saifulloh, saifulloh Septian Rico Hernawan Suryatama, Alif Yudha Taruno, Restiadi Bayu unggara, Ilham Wigar Kumara P Wimar Febrianto Yana Hendriana Yogananda, Amrina Amalia Yudho Bismo Utomo, Yudho Bismo Zulfatun Ruscitasari Zulfatun Ruscitasari Zulkhairi Zulkhairi Sofyan