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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research
Siregar, Dzilhulaifa
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Optimasi Penjadwalan Produksi dengan Jaringan Syaraf Tiruan dan Algoritma Genetika Siregar, Dzilhulaifa; Sofinah Harahap, Lailan; Fadlan Alamsyah, Muhammad
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2324

Abstract

Optimal production scheduling is essential to improve operational efficiency in the manufacturing industry. This study proposes a combination of Neural Networks (NN) and Genetic Algorithms (GA) to solve production scheduling problems. NN is used to predict processing time based on historical data, while GA optimizes the production sequence to minimize idle time and increase throughput. Simulation results show that this combined method provides a more efficient scheduling solution compared to conventional methods.
Design, Development, and Implementation of a Desktop-Based Laundry Management Application for Optimizing Operational Efficiency Rahmadani, Noni Fauzia; Syahputri, Rifdah; Nugroho, Agung; Nasution, Luftia Rahma; Siregar, Dzilhulaifa; Dewi, Aulia Kartika
TIN: Terapan Informatika Nusantara Vol 5 No 9 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i9.7045

Abstract

Manual management of laundry operations often faces various challenges, such as recording errors, limited monitoring of employee activities, and lack of transparency in financial reporting. To address these issues, this research aims to design and develop a desktop-based laundry management application that integrates order management, problem reporting, and financial management efficiently. The application is designed for three main user roles: staff, shop heads, and owners. Staff are responsible for inputting customer orders and reporting operational issues, shop heads monitor staff activities and handle problems, while owners can access financial reports and order activity recaps to support strategic decision-making. This research employs the Research and Development (R&D) method with the Waterfall software development model, encompassing requirements analysis, system design, implementation, and testing. Data collection was conducted through literature studies and direct observation of operational processes in multiple laundry businesses. The application was developed using the Java programming language and MySQL database and operates locally without requiring an internet connection. Testing results indicate that the system improves order processing efficiency by reducing recording time by approximately X% compared to manual methods, accelerates financial transaction recording, and enhances transparency in operational reporting. With this system, laundry management is expected to become more effective, accurate, and easily accessible to all users.
Analisis Data Biologis dalam Mengidentifikasi Gen atau Protein yang Memiliki Pola Ekspresi Serupa Akmal, Muhammad Haikal; Pangestu, Dimas; Siregar, Dzilhulaifa; Harahap, Khaila Mukti; Furqan, Mhd.
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1008

Abstract

Ekspresi protein dalam data biologis umumnya memiliki kompleksitas tinggi dan dimensi besar, sehingga menyulitkan pengenalan pola secara langsung. Studi ini memanfaatkan algoritma Spectral Clustering untuk mengeksplorasi struktur tersembunyi dalam kumpulan data ekspresi protein. Langkah awal mencakup pembersihan data dengan imputasi nilai hilang menggunakan metode rata-rata serta normalisasi fitur numerik menggunakan StandardScaler. Dataset terdiri dari 1.080 observasi dan 77 atribut numerik hasil percobaan pada tikus. Proses pengelompokan dilakukan dengan pendekatan berbasis graf, menggunakan parameter empat klaster dan afinitas nearest neighbors. Selanjutnya, dilakukan reduksi dimensi melalui teknik Principal Component Analysis (PCA) untuk menghasilkan representasi dua dimensi yang mudah divisualisasikan. Hasil pengelompokan memperlihatkan pemisahan yang mencerminkan perbedaan biologis antar sampel. Hal ini menunjukkan bahwa metode tak terawasi seperti Spectral Clustering efektif dalam mengungkap struktur laten pada data ekspresi protein dan dapat menjadi dasar bagi analisis klasifikasi berbasis karakteristik biologis.
Co-Authors Akmal, Muhammad Haikal Dewi, Aulia Kartika Fadlan Alamsyah, Muhammad Harahap, Khaila Mukti Mhd Furqan Nasution, Luftia Rahma Nugroho, Agung Pangestu, Dimas Rahmadani, Noni Fauzia Sofinah Harahap, Lailan Syahputri, Rifdah