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All Journal ILKOM Jurnal Ilmiah JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Informatika Ekonomi Bisnis JSR : Jaringan Sistem Informasi Robotik Jurnal Ilmu Komputer Journal of Applied Data Sciences LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Jurnal Komtekinfo Jurnal Testing dan Implementasi Sistem Informasi RECORD Journal of Loyality and Community Development
Zupri Henra Hartomi
Universitas Hang Tuah Pekanbaru
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Transportation Other

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PENERAPAN DATA MINING MENGGUNAKAN METODE CLUSTERING EVALUASI DATA PENJUALAN PT ASPACINDO KEDATON MOTOR: PENERAPAN DATA MINING MENGGUNAKAN METODE CLUSTERING EVALUASI DATA PENJUALAN PT ASPACINDO KEDATON MOTOR Hartomi, Zupri Henra; Sabna, Eka; Yulanda, Yulanda; Amartha, Mohd; sanjaya, Rifki
Jurnal Ilmu Komputer Vol 11 No 2 (2022): Jurnal Ilmu Komputer
Publisher : STMIK Hang Tuah Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33060/JIK/2022/Vol11.Iss2.275

Abstract

Perkembangan teknologi komunikasi dari waktu kewaktu dirasakan semakin pesat, salah satunya adalah dalam usaha penjualan. PT. Aspacindo Kedaton Motor Pekanbaru merupakan usaha yang bergerak dalam bidang Penjualan sepeda motor. Dalam hal ini penginputan penjualan hanya dijadikan sebagai laporan tanpa ada pengembangan data yang lebih lanjut untuk dijadikan sebuah pengetahuan. Oleh karena itu dibutuhkan Penegeloaan Data Mining dengan motode klustering untuk mengolah data transaksi penjualan, sehingga diproleht sebuah keputusan yang dapat digunakan untuk menganalisis data penjualan. Tujuan utama dari metode clustering adalah pengelompokan sejumlah data/obyek ke dalam cluster, dimana cluster tersebut akan berisi data yang sama dengan groupnya masing-masing. Manfaatnya mempermudah analisis data yang besar dan membantu memberikan informasi data penjualan. Hasil dari penelitian ini diperoleh perbandingan daerah mana menghasilkan banyak penjualan yaitu kluster 1 pada daerah Tenayan Raya, kluster 2 pada daerah Limapuluh. Dan kluster 3 pada daerah Payung sekaki. Dari pola yang di peroleh di harapkan dapat memberi pengetahuan untuk PT. Aspacindo Kedaton Motor Pekanbaru sebagai pendukung untuk mengambil kebijakan.
Aplikasi Pengolahan Data Kelahiran, Kematian, Datang dan Pindah Menggunakan Bahasa Pemrograman PHP Zalmadani, Hendro; Hartomi, Zupri Henra
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1175

Abstract

Padang Birik-Birik Village is one of the villages in North Pariaman District, Pariaman City. The process of recapitulating birth, death, arrival, and moving letters received at the Padang Birik-Birik Village Office has problems, namely the process of making a cover letter for birth certificates and death certificates as well as arrival and moving letters. For the cover letter for birth certificates, it is done by filling out form F-2.09, for the cover letter for death certificates, it is done by filling out form F-2.20, to make a cover letter for arrival, it is done by filling out form F-1.01, Furthermore, for and moving letters, it is also done by filling out form F-1.04 which is done by hand. Another problem is the difficulty of finding archives/files of population data in the filing cabinets that are piled up and so many, because the process of finding archives/data files is done conventionally, namely looking at documents one by one. In developing the system, the author uses the waterfall method where the data collection techniques used include observation and interviews. For the development method using a structured method with several tools and work techniques such as flowcharts, use cases and activity diagrams. The programming language used in designing and implementing the system is PHP and the database used is MySql. The results of the study obtained that with the existence of this Application information system can facilitate village staff for the Process of Filling Death Forms, Births, coming and moving can be done quickly and efficiently.
Integration of Machine Learning Models Random Forest and XGBoost for Credit Card Fraud Detection in a Python Flask-Based Application Herianto Heri; Zupri Henra Hartomi; Rian Ordila; Yuda Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4821

Abstract

Credit card fraud is one of the major challenges in modern digital payment systems. The increasing volume of online transactions raises the potential for unauthorized use of cardholder data. This research aims to develop a robust and accurate fraud detection system by integrating two machine learning algorithms, Random Forest and XGBoost, both of which are known for their high performance in data classification. The research process begins with the collection and preprocessing of credit card transaction data, followed by model training using the selected algorithms. The model’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. To enable real-time application, the model is implemented in a web-based system using the Python Flask framework, allowing direct integration into financial transaction environments. The need for adaptive systems that can respond to emerging fraud patterns serves as a key motivation for this study. By combining two complementary algorithms within a single web application platform, the system is expected to detect fraudulent activities quickly and accurately. The expected outcomes of this research include: (1) an optimized fraud detection model based on Random Forest and XGBoost, (2) a prototype web application developed with Python Flask for system implementation, and (3) a scientific publication describing the development and results of the proposed system. The targeted outputs are a publication in a nationally accredited journal (Sinta 4) and intellectual property registration. This research is expected to provide a significant contribution to preventing credit card fraud through the effective application of machine learning technologies
An IoT-Driven Hybrid Stacking Ensemble with Deep Meta-Learning for Vending Machine Sales Forecasting Yulisman Yulisman; Zupri Henra Hartomi; Rian Ordila; Uci Rahmalisa; Arie Linarta; Yuda Irawan
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1395

Abstract

Accurate sales prediction is essential for optimizing inventory management and supporting dynamic pricing strategies in the retail industry, particularly for vending machines (VMs) integrated with IoT technologies. The availability of real-time transactional and environmental data from IoT sensors provides opportunities to improve forecasting accuracy by capturing complex temporal patterns and external influences on consumer behavior. However, traditional time series models and single machine learning approaches often struggle to model nonlinear relationships and long-term dependencies in such data. This study proposes a hybrid stacking ensemble model that integrates machine learning and deep learning techniques to enhance the prediction of daily sales volume per Stock Keeping Unit (SKU) in IoT-enabled vending machines. The proposed framework employs Random Forest Regressor (RF), Support Vector Regression (SVR), and XGBoost Regressor (XGB) as Level-0 base learners. Their predictions, along with corresponding residuals, are utilized as meta-features for a Long Short-Term Memory (LSTM)-based meta-learner, enabling effective modeling of both nonlinear and temporal characteristics. The model incorporates diverse features derived from IoT data, including lagged sales, rolling statistics, temporal attributes (day of week and weekend indicators), and environmental variables such as temperature and humidity collected from IoT sensors. Hyperparameter optimization of the LSTM meta-model is performed using Optuna to improve model stability and generalization. The proposed approach is evaluated using 10-Fold Time Series Cross-Validation to preserve temporal data structure. Experimental results show that the proposed model achieves an R² of 0.9967 and an RMSE of 0.0899, outperforming the best individual base model, XGBoost (R² = 0.9946, RMSE = 0.1121). Although the improvement is marginal, it consistently demonstrates the advantage of combining machine learning and deep learning through a stacking ensemble strategy. These findings indicate that integrating meta-features, residual learning, and IoT-based feature engineering can improve predictive performance and support adaptive decision-making in real-time vending machine operations.
Implementasi Sistem Pengelolaan Manajemen Masjid pada Masjid Nurul Falah III Kelurahan Tuah Karya Kecamatan Tuah Madani Pekanbaru Yulisman Yulisman; Herianto Herianto; Zupri Henra Hartomi
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2024): Desember 2024
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v6i2.407

Abstract

Masjid adalah sebuah tempat ibadah yang digunakan oleh orang muslim dan untuk pengelolaan manejmen Masjid di serahkan kepada Jemaah di sekitar masjid yang bersifat sosial dan iklas. Beriring perkembangan Teknologi Informasi dimasa sekarang ini, masih banyak masjid belum memanfaatkan perkembangan teknologi informasi dalam pengelolaan manajemen masjid terutama pengelolaan keuangan dan administrasi. Masjid Nurul Falah III Kelurahan Tuah Karya Kecamatan Tuah Madani, merupakan tempat dan mitra kegiatan Pengabdian Kepada Masyarakat ini, dan kegiatan pengabdian ini fokus membantu permasalahan pada sistem pengelolaan manajemen masjid terutama administrasi dan keuangan yang selama ini belum menerapkan dan memanfaat teknologi informasi secara maksimal. Sistem ini dibangun bertujuan untuk membantu pengurus Dewan Kamakmuran Masjid (DKM) Nurul Falah III dalam mengelola sistem manajemen keuangan dan administrasi masjid agar tersimpan secara terkomputerisasi dan bisa diakses secara online dan bisa memberikan informasi yang tepat waktu. Model yang digunakan dalam membangun sistem pengelolaan manajemen masjid adalah model prototype, dan bahasa pemrograman PHP serta basis data MySQL. Sistem pengelolaan manajemen masjid berhasil dibangun dan diimplementasikan ke mitra yakni masjid Nurul Falah III. Sistem dapat memberikan informasi laporan keuangan (operasional dan anak yatim) lebih transparan dan lengkap dengan grafiknya serta untuk administrasi (surat keluar dan masuk) terarsipkan secara terpusat dan arsip surat mudah di cari secara cepat dan akurat.
Co-Authors Abdi Muhaimin Agus Alamsyah Amartha, Mohd Anam, M Khairul Arie Linarta Bambang Kurniawan Desy Arischa Eka Sabna Elisawati, Elisawati Hadi Asnal, Hadi Herianto Heri Herianto Herianto Herianto Herianto Julius Santony Kurniawan, Bambang Mitrin, Abdullah Mohd Rinaldi Amartha Muhaimin, Abdi Nurhadi Rahmalisa, Uci Renaldi, Reno Rian Ordila Rian Ordila Rometdo Muzawi Sabna, Eka sanjaya, Rifki Saputra, Haris Tri Yayang Sahira Yuda Irawan Yuhandri Yuhandri, Yuhandri Yulanda, Yulanda Yulisman Yulisman Yulisman, Yulisman Zalmadani, Hendro