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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Building of Informatics, Technology and Science Jurnal Teknologi Informasi Mura
Rahmadayanti, Fitria
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Penerapan Metode Decision Tree Dalam Menentukan Kelulusan Mahasiswa Rahmadayanti, Fitria; Anggraini, Inda
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.045 KB) | DOI: 10.47065/bits.v3i3.1154

Abstract

The purpose of this study is to produce a prediction system for determining the determination of student graduation on time with the Decision Tree method at Pagaralam High School of Technology. If many students graduate not on time or exceed the specified limit will result in the accumulation of students in large numbers due to the imbalance of the number of students entering and exiting each graduation period so that it can cause the academic process does not run optimally. Decision Tree is a classification algorithm that can predict large amounts of data. The development method used is the Rapid Application Develoment (RAD) method consisting of Requirement Planning (Requirements Planning), Workshop Design, Implementation (Implementation). This research can help the Pagaralam High School of Technology in seeing whether students will graduate on time or not
PENGELOMPOKAN TINGKAT RESIKO PENYAKIT JANTUNG BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS rahmadayanti, fitria; Muntari, Siti; Putriani, Resti
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 8 No 2 (2023): JUTIM (Jurnal Teknik Informatika Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v8i2.2108

Abstract

Heart disease is one of the non-communicable diseases that can cause death, This disease occurs due to a narrowing of the blood vessels so as to cause impaired heart function Some of the causes of heart disease are one of them based on age, basically heart disease can be prevented by various factors including a healthy lifestyle, besides that early detection of heart disease is also needed to prevent death in sufferers One way to do early detection is to use data mining. The use of the k-means algorithm can be done to cluster the grouping of heart diseases by age to find out someone is exposed to the cause of high and low heart disease. Based on these problems, this study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method with several stages such as Business Understanding, Data Understanding, Data preparation, Modeling, Evaluation, and Deployment. The clustering method with the k-means algorithm in this study shows a new insight, namely grouping the risk level of heart disease based on 3 clusters. Cluster 0 is an age category with a fairly low risk level of heart disease or Low, which is 355 out of 1025 age categories tested, then cluster 1 is an age category with a moderate or Medium heart disease risk level, which is 208 out of 1025 age categories tested, and finally cluster 2 is an age category with a fairly high age category or High, which is 462 out of 1025 age categories tested.
PENERAPAN METODE DATA MINING PADA KASUS KRIMINALITAS INDONESIA Rahmadayanti, Fitria; Rahayu, Rika
Jurnal Teknologi Informasi Mura Vol 15 No 1 (2023): Jurnal Teknologi Informasi Mura Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v15i1.2054

Abstract

Kriminalitas merupakan masalah yang sering terjadi di lingkungan masyarakat yang dapat mengancam keselamatan dimana saja termasuk di negara Indonesia. Berdasarkan data dari Badan Statistic tindak Kriminaitas di Indonesia sebesar 90 per 100.000 penduduk pada tahun 2021. hal itu berarti ada 90 dari 100.000 penduduk yang menjadi korban kriminalitas sepanjang tahun lalu. Ini menjadi PR pemerintah dan kepolisian republic indonesia khususnya untuk dapat menangani dan mengupayakan penanggulangan kriminalitas di Indonesia. Tujuan dari penelitian ini adalah dapat membantu pihak kepolisian di Indonesia menganalisis data-data kriminalitas yang terjadi berdasarkan jenis kejahatan sehingga mempermudah pemerintah Indonesia dalam mengambil suatu keputusan. Metode yang digunakan pada penelitian adalah metode CRIS-DM yang terdiri dari 6 tahap yaitu Business Understanding (Pemahaman Bisnis), Data Understanding (Pemahaman Data), Data Preparation (Persiapan Data), Modelling (Pemodelan), Evaluation (Pengujian) dan Deployment (Penyebaran). Pada penelitian dilakukan pengelompokan data kriminalitas di Indonesia menggunakan Algoritma K-Means, Data yang diloah di bagi menjadi 3 Cluster yaitu Cluster tindak kriminalitas tingkat tinggi (C0), Cluster tindak kriminalitas tingkat sedang (C1) dan Cluster tindak kriminalitas tingkat renda (C2). Hasil algoritma K-Means diperoleh dengan hasil Cluster 1 dengan kategori tindak kriminalitas sangat tinggi memiliki 22 items, Cluster 2 dengan kategori tinggi memiliki 1 items dan Cluster 3 dengan kategori sedang memiliki 11 items.
PENERAPAN ALGORITMA C4.5 PADA PRODUK PENJUALAN MAKANAN RINGAN Rahmadayanti, Fitria; Asminah, Asminah; Cahaya, Della Tri
Jurnal Teknologi Informasi Mura Vol 16 No 1 (2024): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v16i1.2294

Abstract

Advances in technology and information today create smart innovations in business, which we can call business intelligence. Competition in the business sphere forces business people to always think about strategies and breakthroughs that can ensure the sustainability of the business being run.The purpose of this study is to accurately analyze the C4.5 algorithm in classifying the best-selling food sales products. The results of research that have been obtained that product sales that are less than 207 include food that is less in demand while sales of products that are more than 207 are among the best-selling foods. Product sales of more than 207 are the best-selling foods where there are 5 best-selling food products. While the sales of products that are less than 207 are less in demand food where there are 5 food products that are less in demand.
PENERAPAN METODE DATA MINING PADA KASUS KRIMINALITAS INDONESIA Rahmadayanti, Fitria; Rahayu, Rika
Jurnal Teknologi Informasi Mura Vol 15 No 1 (2023): Jurnal Teknologi Informasi Mura Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v15i1.2054

Abstract

Kriminalitas merupakan masalah yang sering terjadi di lingkungan masyarakat yang dapat mengancam keselamatan dimana saja termasuk di negara Indonesia. Berdasarkan data dari Badan Statistic tindak Kriminaitas di Indonesia sebesar 90 per 100.000 penduduk pada tahun 2021. hal itu berarti ada 90 dari 100.000 penduduk yang menjadi korban kriminalitas sepanjang tahun lalu. Ini menjadi PR pemerintah dan kepolisian republic indonesia khususnya untuk dapat menangani dan mengupayakan penanggulangan kriminalitas di Indonesia. Tujuan dari penelitian ini adalah dapat membantu pihak kepolisian di Indonesia menganalisis data-data kriminalitas yang terjadi berdasarkan jenis kejahatan sehingga mempermudah pemerintah Indonesia dalam mengambil suatu keputusan. Metode yang digunakan pada penelitian adalah metode CRIS-DM yang terdiri dari 6 tahap yaitu Business Understanding (Pemahaman Bisnis), Data Understanding (Pemahaman Data), Data Preparation (Persiapan Data), Modelling (Pemodelan), Evaluation (Pengujian) dan Deployment (Penyebaran). Pada penelitian dilakukan pengelompokan data kriminalitas di Indonesia menggunakan Algoritma K-Means, Data yang diloah di bagi menjadi 3 Cluster yaitu Cluster tindak kriminalitas tingkat tinggi (C0), Cluster tindak kriminalitas tingkat sedang (C1) dan Cluster tindak kriminalitas tingkat renda (C2). Hasil algoritma K-Means diperoleh dengan hasil Cluster 1 dengan kategori tindak kriminalitas sangat tinggi memiliki 22 items, Cluster 2 dengan kategori tinggi memiliki 1 items dan Cluster 3 dengan kategori sedang memiliki 11 items.
PENERAPAN ALGORITMA C4.5 PADA PRODUK PENJUALAN MAKANAN RINGAN Rahmadayanti, Fitria; Asminah, Asminah; Cahaya, Della Tri
Jurnal Teknologi Informasi Mura Vol 16 No 1 (2024): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v16i1.2294

Abstract

Advances in technology and information today create smart innovations in business, which we can call business intelligence. Competition in the business sphere forces business people to always think about strategies and breakthroughs that can ensure the sustainability of the business being run.The purpose of this study is to accurately analyze the C4.5 algorithm in classifying the best-selling food sales products. The results of research that have been obtained that product sales that are less than 207 include food that is less in demand while sales of products that are more than 207 are among the best-selling foods. Product sales of more than 207 are the best-selling foods where there are 5 best-selling food products. While the sales of products that are less than 207 are less in demand food where there are 5 food products that are less in demand.
Co-Authors Anggraini, Inda Asminah Asminah, Asminah Cahaya, Della Tri Muntari, Siti Putriani, Resti Rahayu, Rika