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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Akrab Juara : Jurnal Ilmu-ilmu Sosial Profitabilitas Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Deni Gunawan
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Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Medicine & Pharmacology Other

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SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA RESTORAN CAKI CAKE KARAWANG Deni Gunawan; Dwi Puji Hastuti; Ria Andriani; Susafa’ati Susafa’ati
Jurnal Akrab Juara Vol 3 No 1 (2018)
Publisher : Yayasan Akrab Pekanbaru

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Abstract

Sektor pertanian merupakan sektor primer dan memegang peranan penting bagi perekonomian nasional. Salah satu hasil dari sektor pertanian adalah beras yang merupakan makanan pokok warga negara Indonesia. Penelitian ini bertujuan untuk mengetahui pengaruh secara simultan dan parsial produksi, dan harga terhadap konsumsi beras Kabupaten Kerinci, serta untuk menganalisis variabel yang berpengaruh paling dominan terhadap konsumsi beras di Kabupaten Kerinci periode tahun 2010 - 2015. Teknik analisis data yang digunakan adalah analisis regresi linier berganda. Hasil penelitian menunjukkan secara simultan variabel produksi dan harga berpengaruh signifikan terhadap konsumsi beras di Kabupaten Kerinci tahun 2010 - 2015. Secara parsial variabel produksi beras dan konsumsi beras tidak berpengaruh terhadap konsumsi beras di Kabupaten Kerincitahun 2010-2015. Variabel produksi dan harga berpengaruh positif dan signifikan terhadap konsumsi beras di Kabupaten Kerinci tahun 2010 - 2015. Kata Kunci: produksi, konsumsi beras, harga.
Analysis of Machine Learning Algorithms for Early Detection of Alzheimer’s Disease: A Comparative Study Deni Gunawan; Robi Aziz Zuama; Muhamad Abdul Ghani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 3 (2024): June 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i3.579

Abstract

This study aims to analyze and compare the performance of various machine learning algorithms in predicting Alzheimer's disease based on patient clinical data. The algorithms tested include Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The dataset used in this research consists of clinical data from patients, encompassing various health parameters. The results indicate that the Decision Tree and Random Forest algorithms provide the best performance, with an overall accuracy of 93%. Random Forest performs slightly better in recall for class 0 but slightly worse in recall for class 1 compared to Decision Tree. Logistic Regression also shows good performance with an overall accuracy of 83%, while K-Nearest Neighbors has the lowest performance with an overall accuracy of 72%. This research offers insights into the effectiveness of various machine learning algorithms in detecting Alzheimer's disease and underscores the importance of selecting the appropriate model based on data characteristics and application needs. For future research, it is recommended to further optimize the model hyperparameters, increase the dataset size, add new relevant features, and combine several models using ensemble learning techniques. External validation and the development of more interpretable models are also crucial to build trust in the use of machine learning in the healthcare field.
WEB-BASED SALES INFORMATION SYSTEM AT CAKI CAKE KARAWANG RESTAURANT Deni Gunawan; Dwi Puji Hastuti; Ria Andriani; Susafa’ati
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 10 No. 1 (2025): Februari
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

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Abstract

The developments in information and communication technology now is rapidly, particularly in the technology website that can be used on mobile. This technology is not only used by private businesses but the businesses use this technology too, one example is a place of business or a restaurant. The restaurant business person should have a strategy different from other businesses, so it can always be remembered by visitors of the restaurant. For a restaurant businesses can use technology which can help speed up the process of performance, and provide a difference that can attract the attention of visitors. Generally, the restaurant is a form of business that provides a variety of foods and beverages. Every restaurant must serve many orders, if applying the manual booking process will have an impact on the accumulation of orders that are not structured notes, mistakes in writing the order on the name of the menu or the amount of the order. Therefore, by applying the technology developed at this time into the restaurant booking system is expected to control the activities of the booking process to minimize errors. Methods undertaken to produce information with a waterfall model, namely requirements analysis software which then design programs that translate into code generation with PHP web script, JavaScript, JQuery, CSS and framework bootstrap then conducted testing programme and support software are maintenance programs. With the digitalization process can reduce errors presentation sequence order and able to provide information regarding the status of orders.
IMPLEMENTASI PENDEKATAN PEMBELAJARAN MENDALAM (DEEP LEARNING) DALAM MENINGKATKAN PEMAHAMAN KONSEP SISWA DI SEKOLAH DASAR Wibowo, Gandi Wibowo; Deni Gunawan; Dinny Mardiana
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 3 (2025): Volume 10 No3 September, 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i3.27960

Abstract

Elementary education faces transformative challenges in the 21st century, where learning practices are not merely about knowledge transfer but require the development of deep conceptual understanding in students. This research aims to obtain information/overview, identify, and analyze the impact of implementing a deep learning approach in enhancing students' conceptual understanding in elementary schools. The research subjects consisted of fifth-grade teachers and students in elementary schools. This study used a qualitative case study approach with data collection techniques conducted through participant observation, in-depth interviews, and documentation studies. The implementation stages were based on the deep learning process: understanding, applying, and reflecting. The results show that the implementation of a deep learning approach, which includes indicators of mindful learning, meaningful learning, and joyful learning, has a positive impact on improving students' conceptual understanding in IPAS (Science, Social Studies, and Arts) learning. Due to limitations in the research subjects, future researchers are advised to expand the scope of subjects and subjects, and to conduct further quantitative research to statistically test the effectiveness of deep learning on improving student learning outcomes.  
Pengembangan dan Deployment Sistem Pendukung Keputusan Strategi Pemasaran Program Donasi ZIS: Integrasi Algoritma K-Medoids dan Generative AI Alif Rizqi Mulyawan; Nurul Ichsan; Salman Alfarizi; Deni Gunawan; Hasan Basri
PROFITABILITAS Vol 5 No 2 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i2.11668

Abstract

Perkembangan teknologi digital mendorong Lembaga Amil Zakat (LAZ) untuk mengadopsi strategi pemasaran berbasis data guna meningkatkan efektivitas penghimpunan dana zakat, infak, dan sedekah (ZIS). Tantangan utama yang dihadapi LAZ adalah keterbatasan dalam memahami karakteristik dan perilaku donatur secara komprehensif, sehingga strategi pemasaran yang diterapkan belum sepenuhnya tepat sasaran. Oleh karena itu, penelitian ini bertujuan untuk merancang, mengembangkan, dan mengimplementasikan Sistem Pendukung Keputusan (Decision Support System / DSS) yang inovatif dalam mendukung modernisasi strategi pemasaran pada LAZ. Penelitian ini didasarkan pada temuan empiris sebelumnya yang menunjukkan bahwa algoritma K-Medoids memiliki ketahanan yang lebih baik terhadap keberadaan outlier dibandingkan metode klasterisasi lainnya, sehingga efektif digunakan dalam segmentasi donatur berdasarkan pola dan perilaku transaksi. Metode penelitian yang digunakan meliputi analisis kebutuhan sistem, perancangan arsitektur, pengembangan aplikasi, serta tahap implementasi dan pengujian sistem. Sistem yang dikembangkan, yaitu ZIS-Smart-DSS, dibangun menggunakan arsitektur berbasis web dengan framework Python Flask sebagai pengelola backend, basis data relasional untuk pengelolaan data transaksional donatur, serta integrasi Application Programming Interface (API) dengan Large Language Models (LLM) untuk mengotomatisasi pembuatan konten pemasaran yang adaptif dan personal. Hasil segmentasi donatur yang dihasilkan oleh algoritma K-Medoids dimanfaatkan sebagai dasar dalam memberikan rekomendasi strategi pemasaran yang lebih tepat sasaran. Hasil penelitian menunjukkan bahwa ZIS-Smart-DSS mampu mengintegrasikan proses analisis data, pengambilan keputusan, dan eksekusi strategi pemasaran secara efektif. Sistem ini memberikan dukungan signifikan bagi pengelola LAZ dalam memahami karakteristik donatur serta meningkatkan relevansi dan efektivitas komunikasi pemasaran berbasis kecerdasan buatan.
Co-Authors Abdul Nadjib Aleksander Sakalessy Alif Rizqi Mulyawan Dinny Mardiana Dwi Puji Hastuti Etty Sri Hertini Hasan Basri Heppi Syofya, Heppi Lisbet Situmorang Muhamad Abdul Ghani Muhamad Risal Tawil Muhammad Taufik Rusydi Nanda Dwi Rizkia Nurul Ichsan Ria Andriani Ridwan Sya’rani Robi Aziz Zuama Salman Alfarizi Sintya Purwandaningsih Solkhah Mufrikhah Susafa’ati Susafa’ati Susafa’ati Suseno Hendratmoko Wibowo, Gandi Wibowo