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All Journal KUMARA CENDEKIA Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal Pendidikan Dasar Nusantara Mozaik Humaniora Jurnal Sejarah Citra Lekha SHAHIH : Journal of Islamicate Multidisciplinary Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri EKONOMIS : Journal of Economics and Business Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL PENDIDIKAN TAMBUSAI Almana : Jurnal Manajemen dan Bisnis PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Masyarakat dan Budaya Pusaka : Jurnal Khazanah Keagamaan Proceedings of International Conference on Da'wa and Communication JISA (Jurnal Informatika dan Sains) Jurnal Ilmiah Manajemen Kesatuan JMK Jurnal Manajemen dan Kewirausahaan Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Abditeknika - Jurnal Pengabdian Kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Inkubis: Jurnal Ekonomi dan Bisnis Jurnal Simki Postgraduate MANUSKRIPTA Marifah: Jurnal Pendidikan dan Pemikiran Peradaban Islam
Samidi Samidi, Samidi
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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MyPertamina Transaction Increase Strategy Through Optimizing Application Use at Gas Stations Based on Machine Learning Hutapea, Fresly Leo Chandra; Widianto, Sunu; Samidi, Samidi
Inkubis : Jurnal Ekonomi dan Bisnis Vol. 8 No. 1 (2026): INKUBIS Jurnal Ekonomi Dan Bisnis
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/inkubis.v8i1.150

Abstract

Background: The digital transformation in the energy sector is accelerating cashless payment adoption. MyPertamina, PT Pertamina's mobile payment platform, has been deployed across gas stations nationwide. However, only 5.65% of 1.3 million registered users (September 2024) are active, and only 11.5% qualify as loyal users (≥4 transactions/month). This performance gap, which exists between registered, active, and loyal users, is the focus of this study, as existing research has overlooked the role of gas station operational governance in shaping transaction behavior. Objective: This study aims to (1) identify factors influencing MyPertamina usage at DKI Jakarta gas stations, (2) develop a machine learning-based prediction model to classify transaction behavior (MyPertamina vs. cash), and (3) create a G-STIC framework to increase adoption, usage intensity, and loyalty. Methods: A quantitative case study using the CRISP-DM framework analyzed secondary POS transaction data from 8,000 transactions (5,200 MyPertamina; 2,800 cash) at DKI Jakarta gas stations (2024). Stratified sampling was used, and the models—Decision Tree, Gradient Boosted Trees, and Decision Stump—were evaluated based on accuracy, precision, and recall. Results: Gradient Boosted Trees achieved the highest accuracy (97.75%). Gas Station Type and Class showed the strongest correlations with MyPertamina usage, suggesting further investigation of the Gas Station Code correlation. Conclusion: MyPertamina adoption is influenced by operational governance and service standards. The G-STIC framework provides actionable strategies for increasing digital transaction adoption, contributing to both academic literature and managerial practice in the energy retail sector.
Forecasting Indonesian Shrimp Exports for Business Strategy and Sustainable Blue Economy Policy: Evidence from Machine Learning Ikhsani, Ajeng Yuniar; Samidi, Samidi
Almana : Jurnal Manajemen dan Bisnis Vol. 10 No. 1 (2026): April
Publisher : Bandung: Prodi Manajemen FE Universitas Langlangbuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36555/almana.v10i1.3001

Abstract

Fisheries are a strategic sector in Indonesia and are often linked to the country’s blue economy agenda. Shrimp remains a major export commodity, where performance is influenced by managerial and policy factors such as product quality compliance and cold-chain readiness, which are frequently discussed in relation to rejection risks in destination markets. This study provides a forecasting-based input for business strategy and policy by developing a machine-learning model to project Indonesia’s shrimp export trends and linking the results to blue economy policy analysis. XGBoost, CatBoost, and LightGBM were compared to identify the most suitable model. XGBoost produced the best results, with RMSE 1.87, MAE 0.48, and R² 1.00. In the first quarter, export values peaked in January, and whiteleg shrimp (udang vaname) dominated exports. The findings indicate that forecasting can support more targeted export planning, including aligning quality control and cold-chain capacity with peak periods, strengthening market coordination, and improving trade cooperation. Overall, this study highlights how predictive insights can inform practical strategies and policy direction while remaining aligned with sustainable blue economy goals.
Data-Driven Approach in Determining Problem Credit Management Strategies in the Small & Medium Enterprises Banking Sector Syarif, Tisa Armalina; Samidi, Samidi
Jurnal Ilmiah Manajemen Kesatuan Vol. 14 No. 1 (2026): JIMKES Edisi January 2026
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jimkes.v14i1.4906

Abstract

The increase in non-performing loans in the SME sector represents a major challenge for the banking industry, as it directly affects financial stability and profitability. Conventional credit risk assessment approaches are considered insufficient in detecting potential defaults at an early stage. This study aims to apply a data-driven approach to determine effective strategies for managing non-performing loans in the SME banking sector. The research utilizes historical SME credit data from Bank X Regional 11 and applies a Random Forest machine learning algorithm to predict credit collection. The analyzed variables include payment behavior, credit structure, and the financial condition of debtors. The results indicate that the last payment date is the most influential variable in predicting credit risk, followed by loan tenor, loan realization timing, interest rate, and savings balance. The Random Forest model demonstrates high accuracy and stability in credit risk classification. Based on these findings, non-performing loan management strategies are formulated using the G-STIC framework integrated with the 3-C approach (Character, Capacity, Capital), emphasizing early warning systems, credit structure adjustments, and debtor-based risk control. This study provides practical insights for banks in enhancing objectives and sustainable SME credit risk management.
UPAYA MENINGKATKAN KEMAMPUAN FISIK MOTORIK HALUS MELALUI MEDIA REALIA PADA ANAK KELOMPOK A TK TUNAS BANGSA PATI TAHUN AJARAN 2015/2016 Puspitaningrum, Fatiha Rahma; Wahyuningsih, Siti; Samidi, Samidi
Kumara Cendekia Vol 6, No 4 (2018): Kumara Cendekia
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (327.246 KB) | DOI: 10.20961/kc.v6i4.35353

Abstract

Co-Authors AA Sudharmawan, AA Andharu, Dika Arnis Rachmadhani, Arnis Asmita, Maya Ayom Estu Royani, Ayom Estu Azida Batubara, Noor Bagusputra, Argan Imam Bambang Sugiarto Devit Setiono Dewi Kusumaningsih Dijerja, Gahara Diroatmodjo, Indah Safira Eddy Noviana Eka Fauzy, Muhammad Alvy Eka W, Samuel Yusach Eko Aprianto Fauzah, Titi Isnaini Firdaus, Hanafi Firmansyah, Eko Gatot Tri Pranoto Hardika, Deny Henni, Carmel Novella Henny Henny, Henny Hutapea, Fresly Leo Chandra Ikhsani, Ajeng Yuniar Iqbal Anshori, Muhammad Iqbal, Aldy Mercyano Johny Alfian Khusyairi Joko Daryanto La Ode Rabani Latif, Abd. Lilam Kadarin Nuriyanto, Lilam Kadarin Lusa, Sofian Minardi Minardi Moedjiono Moedjiono Nuryadi, Hepi Puspitaningrum, Fatiha Rahma Putra, Adhitya Eka Rachman, M Harviandi Rahmah, Yulinar Aini Romadhan, Fitrah Rosidin Rosidin Salsabila, Gina Sarkawi B Husain Setiadi, Indra Tresna Siti Wahyuningsih Soepriadi, Raden Sri Mulyati Suladi, Ronal Yulyanto Syafran Nurrahman Syahdinullah S, Sutan Syarif, Tisa Armalina Tjahjanto, Tjahjanto Utama, Aditio Pra Widianto, Sunu Widyarto, Fajar