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All Journal Maspari Journal CSID Journal of Infrastructure Development (CSID-JID) Jurnal Optimasi Teknik Industri (JOTI) Enrichment: Journal of Multidisciplinary Research and Development
Zulkarnain Zulkarnain
Departemen Teknik Industri, Universitas Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Developing An Intelligent Logistics and Distribution System For A Large Number of Retail Outlets: A Big Data Analytics Approach Zulkarnain, Zulkarnain; Komarudin, Komarudin; Arofah, Fauziah; Rahman, Irvanu
CSID Journal of Infrastructure Development Vol 3, No 1 (2020)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32783/csid-jid.v3i1.106

Abstract

The logistics and distribution system in the retail industry in Indonesia has its own complexity. The growth and productivity of the retail outlets in Indonesia have been increasing from year to year. Distribution activities in this study are related to the formation of salesman visit routes involving around 38,900 customer base retail outlets, which are quite numerous in number, calling for a challenging approach to find the optimized solution. Therefore, the case study in this research will be discussed on the concept of the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), considering the work balance and visit pattern constraints. The methods used in this research are the balanced K-means and the Minimum Span Tree – Kruskal’s Walk algorithm, which are proven to solve the problem with a shorter computation time and a more balanced daily route than the current conditions as the results.
TINGKAT KERAMAHAN BUBU EKOR KUNING YANG DIOPERASIKAN NELAYAN DI PERAIRAN KEPULAUAN SERIBU Dahri Iskandar; Yudha Bimasakti; Zulkarnain .; Mulyono S Baskoro; Sugeng Hariwisudho; Budhi H Iskandar
Maspari Journal : Marine Science Research Vol 13, No 2 (2021)
Publisher : UNIVERSITAS SRIWIJAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56064/maspari.v13i2.14574

Abstract

Bubu merupakan alat tangkap yang banyak digunakan untuk menangkap berbagai jenis ikan demersal dan ikan karang. Bubu memiliki keunggulan dan kelemahan dalam menangkap ikan. Adanya kelebihan dan kelemahan yang dimiliki oleh alat tangkap bubu maka penelitian ini dilakukan dengan tujuan untuk menentukan keramahan bubu yang dioperasikan oleh nelayan di Perairan Kepulauan Seribu. Penelitian ini dilakukan dengan menggunakan bubu yang terbuat dari kawat dengan ukuran p x l x t : 87,5 x 62,5 x 27,5 cm. Data berupa ukuran, jumlah, bobot dan jenis ikan hasil tangkapan, dikumpulkan dari hasil penangkapan dan dianalisis untuk mencapai tujuan penelitian. Hasil penelitian menunjukan total jumlah hasil tangkapan yang diperoleh selama penelitian sebanyak 624 ekor terdiri dari 23 spesies. Spesies yang dominan tertangkap adalah ikan ekor kuning (Caesio cuning) sebanyak 213 ekor, ikan ini termasuk famili Caesionidae. Distribusi ukuran ikan yang dominan tertangkap pada bubu selama penelitian memiliki panjang cagak berkisar antara 10-34 cm. Hasil tangkapan utama yang tertangkap selama penelitian memiliki bobot 83,003 gram dengan persentase 71,35%, sedangkan hasil tangkapan sampingan memiliki bobot 33,334 gram dengan proporsi 28,65%. Proporsi hasil tangkapan utama berupa ikan ekor kuning yang berukuran layak tangkap dan tidak layak tangkap adalah 48,36% : 51,64%. Bubu Ekor Kuning merupakan alat tangkap yang ramah lingkungan ditinjau dari indikator alat tangkap ramah lingkungan.Kata kunci: Alat tangkap, bubu, ikan ekor kuning, Kepulauan Seribu, ramah lingkungan.
Model Pengaruh Knowledge Management terhadap Kinerja Logistics Service Provider Mohammad Ikrar Pramadi; Romadhani Ardi; Zulkarnain Zulkarnain
Jurnal Optimasi Teknik Industri (JOTI) Vol 3, No 2 (2021)
Publisher : Teknik Industri Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/joti.v3i1.7733

Abstract

Penelitian ini bertujuan untuk membuat model dan menguji faktor-faktor penting dalam implementasi Knowledge Management(KM) di perusahaan-perusahaan Logistics Service Provider(LSP) di Indonesia dan pengaruhnya terhadap kinerja LSP itu sendiri. Faktor-faktor penting yang menunjang implementasi KM untuk mencapai kinerja LSP yang diharapkan adalah budaya, struktur organisasi, strategi dan kepemimpinan, sumber daya manusia dan teknologi informasi. Kinerja LSP diukur berdasarkan pendekatan SCOR (Supply Chain Operation Reference). Validasi model penelitian dilakukan melalui wawancara semi terstruktur dengan ahli, sedangkan pelaksanaan survei dilakukan dengan menyebarkan kuesioner terhadap 63 perusahaan LSP di Indonesia yang menerapkan KM. Structural Equation Model(SEM) menggunakan Partial Least Square(PLS) digunakan dalam melakukan pengolahan dan analisis data. Hasil penelitian dihipotesiskan dapat menunjukkan bahwa faktor-faktor penting implementasi KM berpengaruh terhadap keberhasilan implementasi KM serta memberikan pengaruh terhadap kinerja LSP. Hasil penelitian ini ini memberikan bukti empiris untuk pentingnya implementasi KM di LSP di Indonesia. Temuan penelitian ini memberikan dasar untuk fokus pada faktor-faktor penting implementasi KM dalam menentukan strategi yang tepat untuk menunjang kinerja LSP yang lebih baik.
Development of Loan Default Prediction Models in Indonesia’s Multifinance Industry Muhammad Jauhar Fawwaz; Zulkarnain Zulkarnain
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 2 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i2.357

Abstract

This study develops a predictive model for loan default in Indonesia’s multifinance industry by implementing and comparing three machine learning methods: Logistic Regression, Support Vector Machine (SVM) with RBF kernel, and XGBoost. Using imbalanced datasets from three multifinance companies representing different portfolio characteristics vehicle, multipurpose, and electronics financing the research applies the SMOTE technique to address class imbalance and enhance model sensitivity. Results show that XGBoost outperforms both Logistic Regression and SVM in accuracy (0.9970), precision (0.9482), recall (0.9987), and AUC (0.9996), while also being the most computationally efficient. Feature importance analysis highlights late payment history, financial ratios, credit scores, and demographic variables as key predictors, with XGBoost capturing complex non-linear interactions. The study introduces a novel multi-layered framework for credit risk management, including scoring engines, early warning systems, and segment-based risk strategies. Segment analysis reveals higher default risks among younger, divorced, and less-educated borrowers, as well as for unsecured loans and high debt-to-income ratios. The model’s adaptability across varying institutional datasets demonstrates the need for company-specific calibration. Compared to previous single-model or single-company approaches, this research provides a comprehensive, scalable, and high-performing solution for predictive credit risk modeling in the Indonesian context. Simulation results suggest that the implementation of this framework could reduce NPF by up to 2.3 percentage points and enhance risk-adjusted returns by 3.8–4.2%, offering substantial practical value to multifinance companies.
Co-Authors Arofah, Fauziah Budhi H Iskandar Dahri Iskandar Irvanu Rahman, Irvanu Komarudin, Komarudin Mohammad Ikrar Pramadi Muhammad Jauhar Fawwaz Mulyono S. Baskoro Romadhani Ardi, Romadhani Sugeng Hariwisudho Yudha Bimasakti