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All Journal CHAIN: Journal of Computer Technology, Computer Engineering and Informatics d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Intelligence Data Science (IJIDS)
Marline Sofiana Paendong
Universitas Sam Ratulangi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics

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OPTIMASI PEMBAGIAN TUGAS KARYAWAN PADA BENGKEL INDOMOBIL NISSAN DATSUN KOMBOS DENGAN MENGGUNAKAN METODE HUNGARIAN Dewi Permata Sari; Marline Sofiana Paendong; Yohanes Andreas Robert Langi
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol 9, No 2 (2020): September 2020
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.9.2.2020.29186

Abstract

Sumber Daya Manusia adalah salah satu faktor yang sangat penting bahkan tidak dapat dilepaskan dari sebuah organisasi, institusi maupun perusahaan. Sumber Daya Manusia juga merupakan usaha yang dituntut kemampuannya dalam meningkatkan efisiensi dan mengefektifkan penggunaannya sehingga Sumber Daya Manusia merupakan kunci yang menentukan berkembangnya suatu perusahaan. Dalam Bengkel Indomobil Nissan Datsun Kombos, jenis pekerjaan yang dilakukan oleh karyawan berbeda-beda dikarenakan tingkat kemahiran atau produktifitasnya. Tujuan penelitian  untuk mengetahui pembagian tugas karyawan pada Bengkel Indomobil Nissan Datsun Kombos sehingga mendapat waktu kerja yang optimal. Metode Hungarian dapat digunakan untuk mengetahui pembagian tugas karyawan sehingga mendapat waktu kerja yang optimal. Hasil pembagian tugas karyawan yang diperoleh dengan metode Hungarian jika dibandingkan dengan penempatan karyawan sebelumnya menunjukkan adanya efisiensi waktu sebanyak 191.4 menit per setiap 5 pekerja melakukan 5 pekerjaan. 
Sistem Pakar Diagnosa Penyakit Lambung Menggunakan Metode Forward Chaining Dan Certainty Factor Scheryl Pongantung; Marline Sofiana Paendong; Luther Alexander Latumakulita
Indonesian Journal of Intelligence Data Science Vol 3 No 2 (2024): Volume 3 No 2 2024
Publisher : Faculty of Mathematics and Natural Sciences Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/ijids.v3i2.50076

Abstract

Limited knowledge about the early symptoms of stomach diseases has motivated the author to develop a system that helps the community obtain information. This system aims to provide assistance to the public in obtaining information, consultation, and early treatment for stomach diseases without having to have direct meetings with experts. The expertise of a medical professional in diagnosing stomach diseases can be implemented into an application. In this Expert System, Forward Chaining method is used for reasoning and the Certainty Factor method is used to calculate confidence levels. Based on data processing from one of the users, the research results show that GERD is the most likely diagnosis, with a Certainty Factor value of 96.5%.
Applying Analytical Hierarchy Process in a Decision Support System for Study Program Recommendation Aldyth Najma Rova Marthin; Mahardika Inra Takaendengan; Marline Sofiana Paendong
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.274

Abstract

Choosing a study program is a critical academic decision because it affects students' learning direction, skill development, and career readiness. This study designs, implements, and evaluates a web-based Decision Support System for study program recommendation using the Analytical Hierarchy Process. The model uses four criteria: interest and talent, technology-related hobby, academic score, and job prospects. The research used teacher criteria data before web implementation and student alternative data after the system was implemented. Teacher matrices were screened using the Consistency Ratio requirement, and the valid matrix produced criteria weights of 0.436 for interest and talent, 0.320 for job prospects, 0.192 for technology-related hobby, and 0.053 for academic score. The system was developed with Python and Flask, then evaluated using Black Box Testing and User Acceptance Testing. The main scenario produced Informatics Engineering as the first recommendation with a score of 0.4880 or 49 percent. Across 24 post-implementation student responses, Informatics Engineering was also the most frequent top recommendation, appearing in 10 responses, followed by Mathematics in 9 responses. However, only 16 of 96 student alternative matrices met the CR threshold, which indicates that automatic consistency validation is needed. Black Box Testing confirmed that all tested core functions worked as expected, and UAT produced an acceptance percentage of 81 percent. These results show that the proposed system can provide systematic and usable recommendation support, while consistency control remains the main technical improvement needed.
Co-Authors Aldyth Najma Rova Marthin Dewi Permata Sari Luther Alexander Latumakulita Mahardika Inra Takaendengan Scheryl Pongantung Yohanes Andreas Robert Langi