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Akademika
Published by Universitas Nurdin Hamzah
ISSN : 19073984     EISSN : 25411760     DOI : https://doi.org/10.53564
Core Subject : Science,
Computer Science & IT
Jurnal Akademika merupakan media publikasi hasil penelitian dari para akademisi serta praktisi yang berkenaan dengan teknologi informasi dengan beberapa topik bahasan meliputi sistem informasi, jaringan komputer, keamanan sistem, multimedia, kecerdasan buatan, dan sistem pakar. Jurnal Akademika dikelola dibawah Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Nurdin Hamzah yang rutin terbit 2 kali setahun pada bulan april dan november.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 503 Documents
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Implementasi Metode Analytical Hierarchy Process (AHP) Pada Sistem Pendukung Keputusan Pemilihan Security Terbaik Di PT. Adimulio Palmo Junaidi Surya; Novhirtamely Kahar; Rika Angelina; Teuku Djauhari
JURNAL AKADEMIKA Vol 18 No 2 (2026): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/8n7eck64

Abstract

This study discusses the design of a decision support system for selecting the best security guard at PT Adimulio Palmo Lestari using the Analytical Hierarchy Process (AHP) method. The purpose of this study is to assist the company in determining the most suitable security guard based on several criteria, namely discipline, ethics, achievement, experience, and attendance. The calculation process is carried out by comparing each criterion in pairs to obtain priority weights. The results of the study indicate that the decision support system based on the AHP method is able to provide objective and consistent security rankings. Thus, this system is expected to support faster, more precise, transparent, and accurate decision making in the process of selecting the best security guard.
Implementasi Sistem Managemen Absensi Siswa Berbasis Web Di SMAS Pertiwi 1 Kota Jambi Lisdarti Lisdarti; Ahmad Louis; Titto Ardia Akbar
JURNAL AKADEMIKA Vol 18 No 2 (2026): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/jh49zq75

Abstract

Student attendance is an important aspect of education as it reflects discipline, responsibility, and academic achievement. However, the manual attendance system still used at SMAS Pertiwi 1 Kota Jambi has several weaknesses, such as vulnerability to human error, delays in report preparation, and inefficiency in data management. To overcome these problems, this study aims to design and implement a web-based attendance management system to improve efficiency, accuracy, and transparency in recording student presence. The system was developed using PHP and MySQL and designed through a system analysis approach with Data Flow Diagram (DFD), involving students, teachers, administrators, and the principal as main entities. The attendance mechanism requires students to upload a photo as proof of presence, which is then validated by teachers according to the class schedule. Validated data are automatically compiled and accessible to administrators and the principal for monitoring and reporting. The results show that the system effectively addresses the weaknesses of manual attendance, offering ease in recording, managing, and reporting, and is expected to become an innovative solution for SMAS Pertiwi 1 Kota Jambi.
Evaluasi SMOTE dan SMOTE+Tomek untuk Mengatasi Ketidakseimbangan Kelas pada Prediksi Stunting Balita Berbasis Pembelajaran Mesin Marrylinteri; M. Irwan Bustami; Irawan Irawan; Maria Rosario B; Sansan Rosita
JURNAL AKADEMIKA Vol 18 No 2 (2026): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/sajy3k06

Abstract

Class imbalance is a recurring obstacle in machine learning based screening of child nutritional status. This study evaluates and compares the effect of SMOTE and SMOTE+Tomek Links on the classification of toddler nutritional status using K-Nearest Neighbours (KNN) and Random Forest (RF). The data consist of 9,426 anthropometric records with four predictors, labelled into three classes: normal (7,212; 76.51%), moderate stunting (1,612; 17.10%) and severe stunting (602; 6.39%), a majority to minority ratio of about 12:1. Min-Max scaling and resampling were fitted on training data only, inside a pipeline, and the models were assessed on an independent 20% test set (n = 1,886) with stratified 5-fold cross validation. At baseline, RF reached 0.975 accuracy and 0.932 macro-F1, while KNN displayed an accuracy paradox: 0.913 accuracy but only 0.392 recall on severe stunting (47 of 120 cases). SMOTE raised KNN recall to 0.733 and macro-F1 from 0.753 to 0.816. For RF, SMOTE improved both criteria at once: recall rose from 0.825 to 0.908 (99 to 109 cases) and macro-F1 from 0.932 to 0.949. SMOTE+Tomek performed almost identically, removing only 30 of 17,307 training samples. RF with SMOTE is therefore recommended

Page 51 of 51 | Total Record : 503

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