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Hadi Syahputra
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Pengenalan Teknologi Artificial Intellegence Pada Panti Asuhan Al Hidayah Padang Hadi Syahputra; Musli Yanto; Selvi Zola Fenia
Jurnal Pengabdian Masyarakat Dharma Andalas Vol 4 No 2 (2025): Jurnal Pengabdian Masyarakat Dharma Andalas
Publisher : LPPM Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jpmda.v4i2.2478

Abstract

The rapid development of information technology in the era of the Industrial Revolution 4.0 has had a major impact on various aspects of life, including the world of education and the daily lives of the younger generation. One of the most prominent technologies today is Artificial Intelligence (AI). Understanding of AI is still very limited, especially among teenagers who come from economically disadvantaged backgrounds, including children in orphanages. Children at the AL-Hidayah Padang Orphanage often face limited access to information and cutting-edge technology, including knowledge about AI. As part of the role of the tri dharma of higher education, lecturers have a responsibility to participate in educating the nation's life through community service activities. Through the PKM program entitled "Introduction to Artificial Intelligence Technology at the Al Hidayah Padang Orphanage", lecturers and students want to make a real contribution by introducing the basic concepts of AI to foster children and the orphanage's guidance. This socialization is expected to spark curiosity, enthusiasm for learning technology, and open their insights to future opportunities in the digital and artificial intelligence fields. This activity aims not only to provide a theoretical understanding of AI, but also to provide practical and interactive experiences through simple demonstrations, AI-based educational games, and inspiring discussions. With a fun and easy-to-understand approach, this activity is expected to be the first step in building technological literacy among the foster children and their caregivers at the Al Hidayah Orphanage in Padang.
Improvement of Interpolation Performance with Statistical Method in Total Suspended Solid Identification Hadi Syahputra; Yuhandri Yuhandri; Sumijan sumijan
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1190

Abstract

Total Suspended Solids (TSS) is one of the key parameters used to determine water quality, which can be observed through the density level of suspended particles. The determination of TSS aims to ensure that river pollution levels can be controlled to maintain good environmental quality. However, the identification of TSS is still performed manually, which requires a relatively long processing time. This condition highlights the need for an effective and efficient identification process. Based on these considerations, this study aims to develop an extraction technique to identify TSS in river water using the Interpolation Mean Square (IMS) algorithm. The development of the extraction technique within the IMS algorithm is crucial for improving the performance of linear interpolation methods. Mean Square is proposed as a parameter in the interpolation process to optimize the extraction algorithm. The segmentation process based on the performance of the IMS algorithm involves exploring and grouping image intensity values. The resulting segmented image clusters are subsequently selected based on the values produced by the Mean Square computation, which are then processed as the final segmentation output. The experimental results show an improvement in the performance evaluation results of the IMS algorithm providing an increase of 7% to 10% over the previous linear interpolation method. The evaluation results produced by the IMS algorithm are 90.19% accuracy, 99.99% sensitivity, and 83.33% specificity. These results indicate that the improved interpolation method presented in the IMS algorithm produces optimal results in determining TSS. Improving the performance of the interpolation method through the development of an IMS-based extraction technique has succeeded in producing optimal identification results. The superiority of the IMS algorithm provides novelty in the development of interpolation techniques for automated segmentation. Furthermore, the findings of this study can effectively support the West Sumatra Environmental Agency in addressing river water pollution issues.
Penerapan Metode Trend Moment Untuk Memprediksi Penjualan Kayu Dalam Meningkatkan Efisiensi Stok Pada CV Intan Jaya Muhammad Jaya Tinsky; Abulwafa Muhammad; Hadi Syahputra
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.1041

Abstract

Pengelolaan persediaan yang efektif sangat diperlukan untuk mencegah masalah kelebihan atau kekurangan barang yang dapat merugikan perusahaan, khususnya perusahaan perdagangan kayu. Metode Trend Momentdipilih karena kemampuannya dalam menganalisis data masa lalu dan memperkirakan kebutuhan persediaan di masa mendatang.Studi ini dimulaidengan mengumpulkan data penjualan kayu pada CVIntan Jaya dengan jenis kayu yaitu Kayu Banio, Kayu Timbalun,dan Kayu Bayua dalam jangkawaktu 2 tahun (Januari 2023 sampai dengan Desember 2024).Data ini dianalisis untuk mengetahui tren dan pola musiman yang dapat berdampak pada permintaan produk. Hasil analisis menunjukkan bahwa metode Trend Momentdapat memberikan ramalan yang tepat mengenai perkembangan penjualan produk. Dengan menerapkan metode ini, perusahaan dapat merumuskan strategi pemasaran yang lebih fokus, menyesuaikan volume produksi berdasarkan permintaan pasar, serta meminimalkan risiko kelebihan ataukekurangan persediaan. Temuan dari penelitian ini mengindikasikan bahwa pendekatan ini dapat menghasilkan tingkat akurasi yang tinggi dalam memperkirakan volume penjualan. Dengan memanfaatkan data uji ramalan untuk kayu banio 4cm*6cm pada bulan Januari 2025, nilai Absolute Percentage Error(APE) yang diperoleh adalah sebesar 1.0309%. Pada penelitian ini Metode Trend Momentterbukti efektif dalam mendukung peramalan produk guna meningkatkan ketepatan pengelolaan persediaan.
Sistem Pakar Rekomendasi Jurusan Kuliah Berdasarkan Minat Dan Nilai Akademik Siswa Hadi Syahputra; Rofil M. Nur; Aulia Rifa
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1160

Abstract

Pemilihan jurusan kuliah merupakan keputusan penting bagi siswa tingkat akhir sekolah menengah atas karena berpengaruh terhadap masa depan pendidikan dan karier. Namun, banyak siswa mengalami kesulitan dalam menentukan jurusan yang sesuai akibat kurangnya pemahaman potensi diri, keterbatasan informasi jurusan, serta minimnya layanan bimbingan karier berbasis data. Penelitian ini bertujuan untuk membangun sistem pakar rekomendasi jurusan kuliah berdasarkan minat dan kemampuan akademik siswa menggunakan metode forward chaining. Penelitian dilakukan pada siswa tingkat akhir SMAN 2 Koto XI Tarusan dengan jumlah responden sebanyak 100 siswa. Data yang digunakan meliputi minat siswa yang diperoleh melalui kuesioner serta nilai akademik pada mata pelajaran Matematika, Bahasa Indonesia, dan Bahasa Inggris. Sistem pakar dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu memberikan rekomendasi jurusan kuliah secara objektif, terarah, dan berbasis data, sehingga dapat membantu siswa dalam pengambilan keputusan yang lebih tepat serta mengurangi risiko kesalahan dalam memilih jurusan kuliah.
Co-Authors Ahmad Zulfa Aulia Rifa Aziz Hakim Asshidiqqi Dodi Guswandi Fenia, Selvi Zola Firdaus Liga Mayola Muhammad Abi Aufa Muhammad Jaya Tinsky Muhammad, Abulwafa Nanda Tommy Wirawan Okta Andrica Putra Risa Nadia Ernes Rofil M. Nur Romi Wijaya Sumijan Sumijan Vesi Puspita Sari Yanto, Musli Yuhandri Yuhandri, Yuhandri