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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Respati Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia SENADA : Semangat Nasional Dalam Mengabdi TECHNOVATAR Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : bit-Tech

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Penerapan Metode Moving Average untuk Memprediksi Stok Parfum Rizal Khadarusman; Kusrini; Kusnawi
bit-Tech Vol. 7 No. 1 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i1.1563

Abstract

Kekurangan stok parfum karena mengalami kesulitan dalam memprediksi kebutuhan stok untuk bulan berikutnya adalah hal yang tidak boleh terjadi. Proses pengadaan stok yang memerlukan waktu menambah tantangan ini, mengakibatkan dampak negatif pada omzet penjualan toko. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan metode data mining, khususnya teknik peramalan, untuk memprediksi kebutuhan stok parfum. Moving average dipilih karena kesederhanaan dan efektivitasnya dalam menangani data yang bersifat time-series. Metode ini menghitung rata-rata penjualan dalam periode waktu tertentu untuk memberikan estimasi kebutuhan stok di masa depan. Dengan pendekatan ini, kami dapat mengurangi dampak variabilitas data harian yang mungkin disebabkan oleh faktor musiman atau peristiwa tertentu, dan fokus pada tren yang lebih stabil. Dataset yang dianalisis mencakup data penjualan toko parfum dari Maret 2022 hingga September 2022, dan tiga parameter berbeda diuji dalam penelitian ini: periode 2 bulan, 3 bulan, dan 4 bulan. Hasil penelitian menunjukkan bahwa parameter 2 bulan memberikan prediksi paling akurat, dengan nilai error Mean Absolute Percentage Error (MAPE) sebesar 13,3%, menunjukkan tingkat akurasi yang baik. Kesimpulan dari penelitian ini adalah bahwa metode moving average dengan parameter 2 bulan efektif untuk memprediksi kebutuhan stok parfum, yang dapat membantu toko dalam mengelola persediaan dengan lebih efisien dan mengurangi risiko kekurangan stok. Temuan ini menunjukkan perlunya perencanaan persediaan yang lebih baik dan penggunaan metode prediksi yang tepat dalam industri parfum. Penelitian lebih lanjut mungkin diperlukan untuk mengoptimalkan pendekatan ini atau mengeksplorasi metode alternatif.
An An Explainable Machine Learning Approach Using Random Forest and SHAP for Employee Attrition Prediction Joang Ipmawati; Kusnawi Kusnawi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
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 Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dharma Kusumah, Prema Adhitya Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hasirun, Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Irawanto, Indra Joang Ipmawati Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini KUSRINI Kusrini - - Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, Kusirini Kusrini, Kusrini M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pattimura, Yudha Bagas Pebri Antara Pitaloka, Nadhira Triadha Pramono, Aldi Yogie Prastyo, Rahmat Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri RAMADHAN, SYAIFUL Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Rohim, Ni’matur saifulloh Saifulloh, saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Sudirman, San Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko, Taryoko Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri