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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

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

Analisa Terhadap Perbandingan Algoritma Decision Tree Dengan Algoritma Random Tree Untuk Pre-Processing Data Saifullah Saifullah; Muhammad Zarlis; Zakaria Zakaria; Rahmat Widia Sembiring
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 2 (2017): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i2.41

Preprocessing data is needed some methods to get better results. This research is intended to process employee dataset as preprocessing input. Furthermore, model decision algorithm is used, random tree and random forest. Decision trees are used to create a model of the rule selected in the decision process. With the results of the preprocessing approach and the model rules obtained, can be a reference for decision makers to decide which variables should be considered to support employee performance improvement

The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm Asro Pradipta; Dedy Hartama; Anjar Wanto; Saifullah Saifullah; Jalaluddin Jalaluddin
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.30

Graduating on time is one element of higher education accreditation assessment. In the Strata 1 level, students are declared to graduate on time if they can complete their studies <= eight semesters or four years. BAN-PT sets a timely graduation standard of >= 50%. If the standard is not met, it will reduce the value of accreditation. These problems encourage the Universitas Simalungun Pematangsiantar to conduct evaluations and strategic steps in an effort to increase student graduation rates so that the targets of BAN-PT can be achieved. For this reason it is necessary to know in advance the pattern of students who tend not to graduate on time. In this study, C4.5 Algorithm is proposed to predict student graduation. This algorithm will process student profile datasets totaling 150 data. This dataset has a graduation status label. The value of the label is categorical, that is, right and late. The features or attributes used, namely the name of the student, gender, student status, GPA. The results of the C4.5 algorithm are in the form of a decision tree model that is very easy to analyze. In fact, even by ordinary people. This model will map the patterns of students who have the potential to graduate on time and late.

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

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.

Analisa Terhadap Perbandingan Algoritma Decision Tree Dengan Algoritma Random Tree Untuk Pre-Processing Data Saifullah Saifullah; Muhammad Zarlis; Zakaria Zakaria; Rahmat Widia Sembiring
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 2 (2017): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Preprocessing data is needed some methods to get better results. This research is intended to process employee dataset as preprocessing input. Furthermore, model decision algorithm is used, random tree and random forest. Decision trees are used to create a model of the rule selected in the decision process. With the results of the preprocessing approach and the model rules obtained, can be a reference for decision makers to decide which variables should be considered to support employee performance improvement

The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm Asro Pradipta; Dedy Hartama; Anjar Wanto; Saifullah Saifullah; Jalaluddin Jalaluddin
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Graduating on time is one element of higher education accreditation assessment. In the Strata 1 level, students are declared to graduate on time if they can complete their studies <= eight semesters or four years. BAN-PT sets a timely graduation standard of >= 50%. If the standard is not met, it will reduce the value of accreditation. These problems encourage the Universitas Simalungun Pematangsiantar to conduct evaluations and strategic steps in an effort to increase student graduation rates so that the targets of BAN-PT can be achieved. For this reason it is necessary to know in advance the pattern of students who tend not to graduate on time. In this study, C4.5 Algorithm is proposed to predict student graduation. This algorithm will process student profile datasets totaling 150 data. This dataset has a graduation status label. The value of the label is categorical, that is, right and late. The features or attributes used, namely the name of the student, gender, student status, GPA. The results of the C4.5 algorithm are in the form of a decision tree model that is very easy to analyze. In fact, even by ordinary people. This model will map the patterns of students who have the potential to graduate on time and late.

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