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All Journal Akademika Fortech : Journal of Information Technology
Novhirtamely Kahar
Universitas Nurdin Hamzah
Computer Science & IT

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PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA ANALISIS POLA PENDAFTARAN CALON JAMAAH HAJI DI KANTOR WILAYAH KEMENTERIAN AGAMA PROVINSI JAMBI Hikmatul Ilmi; Novhirtamely Kahar
FORTECH (Journal of Information Technology) Vol 10 No 1 (2026): Fortech (Journal Of Information Technology)
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/ewcdte47

Abstract

This study aims to analyze the registration patterns of prospective hajj pilgrims at the Regional Office of the Ministry of Religious Affairs of Jambi Province using the K-Means Clustering algorithm. The main issue addressed is the high volume of hajj applicants each year without adequate pattern analysis, which complicates quota planning and pilgrim guidance. The dataset includes the number of hajj applicants per regency/city from 2018 to 2024, with attributes such as applicant count, age, and gender. The clustering process was carried out using K-Means with k = 3, resulting in three main clusters: high, medium, and low registration rates. The results show that Batanghari Regency and Jambi City belong to the high cluster, while Tebo and Sarolangun are in the low cluster. These findings provide better insights for policymakers in managing hajj quotas and improving outreach strategies.
IMPLEMENTASI DATA MINING METODE NAÏVE BAYES UNTUK PREDIKSI KELAYAKAN PENDONOR DI UDD PMI KOTA JAMBI Novhirtamely Kahar; Besse Eka Mardiana Putri
FORTECH (Journal of Information Technology) Vol 10 No 1 (2026): Fortech (Journal Of Information Technology)
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/a0f3eb75

Abstract

The Blood Donation Unit (UDD) of PMI Kota Jambi plays an important role in maintaining the availability of safe and eligible blood supplies. One of the challenges faced is that the determination of donor eligibility is still carried out manually and relies heavily on medical staff examinations, which may lead to inefficiency and delays in service. Therefore, a predictive model is needed to assist in determining donor eligibility more quickly and objectively.This study aims to apply data mining techniques using the Naïve Bayes algorithm to predict the eligibility of blood donors at UDD PMI Kota Jambi. The data used consist of historical donor records, including attributes such as age, gender, body weight, blood pressure, hemoglobin level, and donor history.
Penerapan Data Mining Model Algoritma C4.5 Dan Naïve Bayes Pada Diagnosis Awal Penyakit Hipertensi Novhirtamely Kahar; Gustina Gustina; Widia Widia
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/vrq8e855

Abstract

Hypertension is a global health problem whose prevalence continues to increase, including in Jambi Province. Early detection of hypertension is crucial to prevent serious complications. This study aims to compare the performance of the C4.5 and Naïve Bayes algorithms in the early diagnosis of hypertension using patient data from Simpang Kawat Community Health Center, Jambi. The data used are hypertension patient data consisting of 200 training data and 50 test data with variables such as age, gender, smoking, BMI, cholesterol levels and blood pressure. The research methods include data collection, data cleaning, algorithm implementation using RapidMiner, and performance evaluation based on accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 90%, precision of 93.48% and recall of 95.56%. Meanwhile, the C4.5 algorithm achieved 86% accuracy, precision of 91.30% and recall of 93.33%. The Naïve Bayes algorithm demonstrated superior performance in predicting hypertension based on the tested data, both on data with independent attributes, and tended to provide stable accuracy results. Meanwhile, the C4.5 algorithm produced an easily understood model due to its systematic and easily explained decision tree structure. The results of this study concluded that the Naïve Bayes algorithm is more effective for the early diagnosis of hypertension.
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.
Co-Authors Besse Eka Mardiana Putri Djauhari, Teuku Gustina Gustina Hikmatul Ilmi Junaidi Surya, Junaidi Rika Angelina Widia Widia