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All Journal Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Ilmu Komputer dan Bisnis Jurnal Informatika Software dan Network (JISN) Jurnal TAM (Technology Acceptance Model) IJISCS (International Journal of Information System and Computer Science)
Rahmatullah, Sidik
STMIK Dian Cipta Cendikia Kotabumi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Other

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ALGORITMA C4.5 UNTUK MEMPREDIKSI KELAYAKAN PENERIMA BANTUAN PANGAN NON TUNAI Rizal Abi Islahudin; Sidik Rahmatullah; Asep Afandi; Sriyani Safitri
Jurnal Informatika Vol 22, No 2 (2022): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v22i2.3367

Abstract

Pemerintah telah menyiapkan program Bantuan Pangan Non Tunai (BPNT) untuk membantu masyarakat miskin dan membutuhkan. Bantuan Pangan Non Tunai (BPNT) harus disalurkan secara tepat, teratur, dan transparan untuk memastikan bahwa penerima bantuan memang benar-benar mereka yang membutuhkan. Oleh karena itu, diperlukan suatu sistem yang dapat mengubah data menjadi informasi dan mengidentifikasi calon penerima bantuan sembako nontunai maupun yang tidak berhak (BPNT). Sistem prediksi yang akan dibuat pada proyek ini menggunakan RapidMiner 7.1 untuk pengujian dan Algoritma C4.5, metode klasifikasi dari data mining. Hasil Implementasi Data Mining dengan metode Algoritma C4.5 untuk memprediksi kelayakan penerima dan hasil penerima bantuan pangan nontunai (BPNT) diperoleh nilai akurasi prediksi sebesar 99%, yang kemudian divalidasi oleh aplikasi RapidMiner 7.1 dengan akurasi hasil 98,50%.
PERBANDINGAN PENGOLAHAN DATA PREDIKSI PERSEDIAAN GAS LPG 3KG MENGGUNAKAN REGRESI LINIER BERGANDA DAN K-MEANS Annisa Rismanitanti; Rima Mawarni; Sidik Rahmatullah; Dwi Marisa Efendi; Sulis Nurbaiti
Jurnal Informatika Vol 22, No 2 (2022): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v22i2.3376

Abstract

he oil and natural gas sector is a sector that is used with great importance for Indonesia's national development. An interesting commodity to watch out for in the oil and gas industry is liquefied petroleum gas (LPG). LPG is a hydrocarbon gas that has been liquefied under pressure to facilitate storage, transportation, and handling and the main ingredients consist of propane/C3, butane/C4 or can be mixed to produce mixed LPG..At this time PT. BLORA MUSTIKA does not focus on when household needs increase and when not, the meaning of this is that LPG gas data is not used properly and is only recorded, this of course makes PT BLORA MUSTIKA unable to predict demand from sub-distributors and results in frequent an empty supply of LPG gas causing difficulties for the community to obtain 3 Kg LPG gas. This problem can be calculated and compared with the Multiple Linear Regression and K-Means methods.By using the Multiple Linear Regression and K-Means method, it is hoped that it will make it easier for PT. BLORA MUSTIKA in determining demand predictions from sub-distributors so that there is no shortage of LPG gas supplies and which method can be obtained which is more effective and efficient.
SISTEM INFORMASI PENJUALAN SEMBAKO PADA TOKO BAPAK NASRUL BERBASIS WEB Sidik Rahmatullah
Jurnal Informatika Software dan Network (JISN) Vol. 4 No. 1 (2023): Jurnal Informatika Software dan Network (JISN)
Publisher : Jurnal Informatika Software dan Network (JISN) diterbitkan oleh Lembaga Penelitian AMIK Dian Cipta Cendikia Pringsewu

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Abstract

Kemajuan teknologi saat ini memaksa segala pekerjaan yang dilakukan oleh manusia dituntut untuk cepat dan tepat. Dengan semakin berkembangnya teknologi seperti saat ini, pasti akan berdampak pada segala aspek, salah satunya adalah aspek penjualan sembako. Komputerisasi merupakan salah satu solusi agar kita sebagai pelaku bisnis dapat melakukan pekerjaan secara cepat dan efisien.                 Penelitian pada Toko sembako Bapak Nasrul  ini dilakukan menggunakan metode pengembangan sistem extreme programming dengan tahapan yaitu planning Planing tahap ini peneliti melakukan perencanaan dalam mamahami konsep kebutuhan sistem yang akan dibangun. Tahapan Kedua Design data yang di peroleh dari planing kemudian di rancang menggunakan UML. Tahapan yang ketiga Coding adalah tahap pengkodean perangkat lunak dengan paduan alur sistem yang sudah dirancang pada tahap design dari modul per modul Tahapan yang keempat Testing merupakan tahap pengujian sistem agar mendapat feedback dari orang yang telah melakukan pengujian untuk mentukan sistem berjalan dengan normal atau tidak. pembuatan sistem ini dibuat menggunakan bahasa pemrograman PHP dan manajemen database menggunakan MySQL. Untuk meningkatkan pelayanan dan kepuasan terhadap pelanggan, maka toko tersebut melakukan peningkatan pelayanan dengan cara membangun sistem yang dapat mempercepat proses pembayaran sehingga mencegah terjadinya antrian pada saat melakukan pembayaran pada kasir toko sembako tersebut.
DIAGNOSIS OF SKIN DISEASES IN TODDLERS USING NAÏVE BAYES AND FORWARD CHAINING METHODS Sidik Rahmatullah; Rima Mawarni
IJISCS (International Journal of Information System and Computer Science) Vol 5, No 1 (2021): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v5i1.954

Abstract

The Center for Public Health is a functional organizational unit that implements health care that is thorough, integrated, evenly acceptable, and affordable to the community. The purpose of this research is to create an Expert System Application to detect skin diseases in toddlers according to the data in the public health center. The system development method used is the Extreme Programming (XP) method with working stages including planning, design, coding, and testing. The system is designed using Unified Modeling Language (UML) which includes use cases, activity diagrams, and chart classes, the software used is PHP (Hypertext Preprocessor) with MySQL databases and uses the Naïve Bayes and Forward Chaining methods. The end result of the creation of this App is to make it easier for users or the public in detecting skin diseases for toddlers.
APPLICATION OF DATA MINING IN PREDICTING THE AMOUNT OF RESTAURANT TAX REVENUE USING C4.5 Afriza, Roby; Nurmayanti, Nurmayanti; Parida, Merri; Rahmatullah, Sidik
Jurnal TAM (Technology Acceptance Model) Vol 14, No 2 (2023): Jurnal TAM (Technology Acceptance Model)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v14i2.1505

Abstract

Regional taxes are one of the important sources of regional income to finance the implementation of regional government in the context of serving the community and realizing regional independence. Restaurant tax is one of the regional taxes collected by the Way Kanan Regency Regional Revenue Agency and one of the determinants of the increase in Way Kanan District Original Revenue (PAD). Adapum This research raises the problem of not achieving the Restaurant Tax target in 2023. In this study using Data Mining there are various methods in data mining including the C4.5 algorithm. The C4.5 approach can forecast an increase in restaurant taxes, and the computation of the C4.5 algorithm yields the following results. From the results of calculating the 2018-2022 data above using Microsoft Excel, it is known that Class Recommendations totaling 185 are classified as Yes and No, 0 are classified as Yes but No, Next Class No totaling 65 is classified as No, and 0 Yes is classified as No, with a total data of 250. Microsoft Excel and Google Colab programs have been used to implement the C4.5 algorithm. Implementation of the C4.5 Algorithm has been carried out using Microsoft Excel and Googlel Colab applications. The result is that the description of all formulas and predictive results is simpler than the results of manual calculations through Microsoft excel using the C4.5 Algorithm which has an accuracy of 75%, and then proven by Google Colab with results of 100% accuracy.
Co-Authors Afandi, Asep Afriza, Roby Ahmad Fikri Habibillah Annisa Rismanitanti Arief Hendriawan Daliman Desri Arisandi Dewi Triyanti Dita Destia Efendi, Dwi Marisa Elis Amalia Ema Utami Faselia, Reni Ferly Ardhy Firmansyah Firmansyah Herman Affandi Iko Prastiyo Karmila Permatasari Kevin Aditya Pratama Mawarni, Rima Melpin Aprido Jenius Mintoro, Sigit Monica Nindy Pramitha Mukrim, Muhammad Ihsan Mukrim Ngajiyanto, Ngajiyanto Nurmayanti Nurmayanti Nurmayanti, Nurmayanti Pakarti Riswanto Parida, Merri Rendy Abdurahman Rizal Abi Islahudin Rustam Rustam Rustam Rustam Sani Handika Lubis Sri Wahyuni Sriyani Safitri Sulis Nurbaiti Supriyanto . Supriyanto Supriyanto Supriyato Supriyato