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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Mantik Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Nani Hidayati
STIKOM Tunas Bangsa
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Other

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PENGELOMPOKAN PERSENTASE BUTA HURUF UMUR 15-44 MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS Saifullah Saifullah; Nani Hidayati
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 3 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i3.329

Abstract

Data Mining is a method that is often needed in large-scale data processing, so data mining has important access to the fields of life including industry, finance, weather, science and technology. In data mining techniques there are methods that can be used, namely classification, clustering, regression, variable selection, and market basket analysis. Illiteracy is one of the factors that hinder the quality of human resources. One of the basic things that must be fulfilled to improve the quality of human resources is the eradication of illiteracy among the community. The purpose of this study is to determine the clustering of illiterate communities based on provinces in Indonesia. The results of the study are illiterate data clustering according to the age proportion of 15-44 namely 1 high group node, low group has 27 nodes, and medium group 6 nodes. The results of this study become input for the government to determine illiteracy eradication policies in Indonesia based on provinces.Kata Kunci: Illiterate, Data mining, K-Means ClusteringData Mining termasuk metode yang sering dibutuhkan dalam pengolahan data berskala besar, maka data mining mempunyai akses penting pada bidang kehidupan diantaranya yaitu bidang industri, bidang keuangan, cuaca, ilmu dan teknologi. Pada teknik data mining terdapat metode-metode yang dapat digunakan yaitu klasifikasi, clustering, regresi, seleksi variabel, dan market basket analisis. Buta huruf merupakan salah satu faktor yang menghambat kualitas sumber daya manusia. Salah satu hal mendasar yang harus dipenuhi untuk meningkatkan kualitas sumber daya manusia adalah pemberantasan buta huruf di kalangan masyarakat Adapun tujuan penelitian ini adalah menetukan clustering masyarakat buta huruf berdasarkan propinsi di Indonesia. Hasil dari penelitian adalah data clustering buta huruf menurut propisi umur 15-44 yaitu 1 node kelompok tinggi,  kelompok rendah memiliki 27 node, dan kelompok  sedang  6 node. Hasil penelitian ini menjadi bahan masukan kepada pemerintah untuk menentukan kebijakan pemberantasan buta huruf di Indonesia berdasarakn propinsi.Kata Kunci: Buta Huruf, Data mining, K-Means Clustering
MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI EKSPOR MINYAK SAWIT MENURUT NEGARA TUJUAN UTAMA Saifullah Saifullah; Nani Hidayati; Solikhun Solikhun
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 6, No 2 (2019): TEKNOVASI OKTOBER 2019
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v6i2.306

Abstract

This study aims to find the best architectural model in predicting palm oil exports according to the main destination countries. The role of the agricultural sector in the national economy is very important and strategic. Oil Palm is an industrial plant producing cooking oil, industrial oil, and bio-diesel fuel. Indonesia is the largest producer and exporter of palm oil in the world. In addition to the increasingly open export opportunities, the domestic market for palm oil and palm kernel oil is still quite large. Prediction is a process for estimating how many needs in the future. State revenues in the export sector must be able to be predicted to help set the state's financial regulations specifically on palm oil exports. By using Artificial Neural Networks and backpropagation algorithms, architectural models will be sought to predict the amount of palm oil exports according to the main destination country. This study uses 12 input variables, and 1 hidden layer. Using 4 architectural models to test the data to be used for prediction, namely models 12-4-1, 12-8-1, 12-16-1 and 12-32-1. The results of the best architectural model are architectural models 12-16-1 with 100% accuracy accuracy.
Clasterization Of Zeeida Product Sales Using K-Means Method In Medan Distributors Nani Hidayati; Kasini; Sabrina Aulia Rahmah
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2545

Abstract

Product clustering is one of the determinants of product development in sales activities. Zeeida Herbal products are engaged in health and beauty, of all the products sold, not all of them are sold, some are less well sold. Sales at the distributor of zeeida Medan products are still not computerized, namely by using manual recording. Every buyer who purchases either an agent, sub-agent, reseller or general customer who makes purchases through social media such as WhatsApp, Facebook, marketplace, and other E-Commerce is recorded in the manual bookkeeping, so there is often stock accumulation and even stock shortages at distributors. In this study, the authors apply the k-means clustering algorithm to classify products that do not sell (C0), sell very well (C1) and sell (C2). Clustering is a technique of one of the data mining functionality, the Clustering Algorithm is an algorithm for grouping a number of data into a certain data group (cluster). From this study, the output generated from the last 4 months, namely January-April 2022, shows that from 47 Zeeida products, sales of Zeeida products did not sell well in cluster 0, there were 39 products, while sales were very good in cluster (C1), there were 4 products and sales were sold in cluster. (c2) there are 4 products.
DAMPAK MATEMATIKA MATRIKS PADA INOVASI KOMPUTER (STUDI LITERATURE REVIEW) Zulaini Masruro Nasution; Fitri Anggraini; Ika Okta Kirana; Nani Hidayati
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 3 No. 2 (2026): Volume 3 No 2 Juli 2026
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/adthzn94

Abstract

Matematika matriks memiliki peran yang sangat penting dalam perkembangan ilmu komputer. Penelitian ini bertujuan untuk menjelaskan dampak signifikan dari matematika matriks pada inovasi dalam ilmu komputer. Metode penelitian yang digunakan adalah metode deskriptif kualitatif studi literatur yaitu mengambil 10 jurnal yang berkaitan dengan matrik dan ilmu komputer . Hasil penelitian ini menunjukkan bahwa matematika matriks secara substansial mempengaruhi berbagai aspek ilmu komputer, termasuk pengembangan algoritma, pemrosesan citra, kecerdasan buatan, dan pemrosesan bahasa alami. Implikasi dari temuan ini adalah bahwa pemahaman yang lebih dalam tentang matematika matriks dapat membuka peluang baru dalam pengembangan teknologi ilmu komputer. Penelitian ini memberikan wawasan yang berharga bagi praktisi dan peneliti di bidang ini serta memperkuat hubungan erat antara matematika dan ilmu komputer.
Rancang Bangun Sistem Informasi Absensi Mahasiswa Menggunakan Teknologi Pengenalan Wajah Berbasis Python Nani Hidayati; Kasini Kasini
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 9 No. 3 (2026): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The research was motivated by the fact that many higher education institutions still rely on manual attendance methods, which are prone to recording errors, time-consuming, and susceptible to irregularities. To address these issues, a facial recognition-based attendance system was designed using Python as the primary programming language. The system employs an object-oriented programming (OOP) approach to facilitate the development of components such as face detection, student data storage, attendance validation, and report generation. The implemented facial recognition technology enables automated, real-time attendance recording, resulting in greater data accuracy and minimal manual intervention. Furthermore, the system offers rapid and flexible attendance reporting features, making it easier for lecturers and administrative staff to monitor student attendance. Simulation results indicate that the system improves efficiency, accelerates the recording process, and reduces potential irregularities compared to conventional attendance methods.
Co-Authors Fitri Anggraini Ika Okta Kirana Kasini, Kasini Sabrina Aulia Rahmah Saifullah Saifullah Solikhun Solikhun Zulaini Masruro Nasution