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All Journal Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal CoreIT Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Almufida: Jurnal Ilmu-Ilmu Keislaman Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Abdidas Jurnal Kesehatan Pasak Bumi Kalimantan LOSARI: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Islamic Economics and Business Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Educate: Jurnal Ilmu Pendidikan dan Pengajaran Mudabbir: Journal Research and Education Studies Medistra Medical Journal (MMJ) Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi
Ramadhani, Siti
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : Jurnal CoreIT

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Rancangan Sistem Informasi Manajemen Aset di PT. Sentral Tukang Indonesia Ridwan, Muhammad; Muhammad, Muhammad; Ramadhani, Siti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 3, No 2 (2017): Desember 2017
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1013.068 KB) | DOI: 10.24014/coreit.v3i2.4415

Abstract

Sentral Tukang Indonesia adalah perusahaan retail yang menjual aksesoris bangunan yang beralamat di Jl. Riau No.131 C-D, Pekanbaru. Sentral Tukang saat ini menghadapi masalah dalam manajemen aset dari proses perawatan aset karena masih menggunakan ingatan perorangan saja sedangkan asetnya banyak. Jika perorangan tersebut terlupa akan menjadi masalah apalagi terkait jenis aset yang melakukan pembayaran dan jika terlambat akan dikenakan denda. Penelitian ini untuk memudahkan sentral tukang untuk melakukan pencatatan perawatan aset perusahaan tersebut. Penelitian ini dibuat dengan menggunakan Visual Basic 6.0 sebagai program aplikasi desktop dan MySQL sebagai aplikasi databasenya. Proses penelitian dilakukan observasi dikarena penulis juga bekerja di sentral tukang selama setahun dan  penulis mendapatkan masalah aset ini. Tahap pembuatan aplikasi ini yaitu analisa kelemahan sistem lama, pencarian data, perancangan, pembuatan, pengetesan, dan implementasi dari perancangan sistem informasi manajemen aset di sentral tukang. Hasil dari penelitian ini program desktop yang mengelola pencatatan perawatan aset dan juga sebagai pengingat akan perawatan aset-aset yang berada di Sentral Tukang. Kata Kunci – Sistem Infomasi, Manajemen Aset, Perawatan Aset
Review of Original Differential Evolution Algorithm: Research Trends, Original Setting Parameters Wang, ShirLi; Budiman, Haldi; Ramadhani, Siti; FooNg, Theam; Morsidi, Farid
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.29903

Abstract

Abstract: Differential Evolution (DE) has emerged as a widely embraced optimization algorithm, consistently showcasing robust performance in the IEEE Congress on Evolutionary Computation (CEC) competitions.Purpose: This study aims to pinpoint key regulatory parameters and manage the evolution of DE parameters. We conduct an exhaustive literature review spanning from 2010 to 2021 to identify and analyze evolving trends, parameter settings, and ensemble methods associated with original differential evolution.Method: Our meticulous investigation encompasses 1,210 publications, comprising 543 from ScienceDirect, 12 from IEEE Xplore, 424 from Springer, and 231 from WoS. Through an initial screening process involving title and abstract skimming to identify relevant subsets and eliminate duplicate entries, we excluded 762 articles from full-text scrutiny, resulting in 358 articles for in-depth analysis.Findings: Our findings reveal a consistent utilization of tuning parameters, self-adaptive mechanisms, and ensemble methods in the final collection. These results deepen our understanding of DE's success in CEC competitions.Value: offer valuable insights for future research and algorithm development in optimization fields.  
APPLICATION OF K-NEAREST NEIGHBOR REGRESSION METHOD FOR RICE YIELD PREDICTION Handayani, Lestari; Alfarabi.B, Alif; Aprilia, Tasya; Wulandari, Indah; Jasril, Jasril; Ramadhani, Siti; Budianita, Elvia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.30907

Abstract

Rice plants with the Latin name Oryza Sativa are food plants that are widely used as the main food crop in various countries, one of which is Indonesia. Indonesia is ranked 4th as the largest rice consuming country in the world. This requires the availability of rice to be maintained. Unstable rice production can be a problem. One of the districts that has experienced a decline in rice production in recent years is the district of Lima puluh kota located in West Sumatra province. This requires prediction of rice production so that it can be used as a benchmark for the future. This study uses data on rice production in fifty cities from 2013 to 2023. The method used to predict is k-nearest neighbor regression (KNN Regression). The data division uses rasio 90 : 10. In testing the data used is divided into 2, namely normal data and data that has been normalized. The test results produce the smallest mean absolute percentage error (MAPE) value of 6.98% on normal data, the value of k is 6 with data division using k-fold 5. Based on the resulting MAPE value, it can be said that KNN Regression can predict rice production results very accurately.
Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification Ramadhan, Aweldri; Budianita, Elvia; Syafria, Fadhilah; Ramadhani, Siti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26834

Abstract

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN
Co-Authors Adawiyah, Jamiatul Alfarabi.B, Alif Aprilia, Tasya Aqsho, Muhammad Arsyad, Kamaruddin Aulia, Febri Azzahra, Dini Azzahra, Dini Bakti Sutopo Budiman, Haldi Chairina Chairina, Chairina Day, Dhea Wulandari El Saputra, Yoga Elin Haerani Eliwarti, Eliwarti Elvia Budianita Fadhilah Syafria Fitri Wulandari FooNg, Theam Ginting, M. Afdly Fauzi Handayani, Lestari Handayani Indah Wulandari Jasril Jasril Juliati Lestari Handayani Masyhur Masyhur Morsidi, Farid muhammad fikri Muhammad Muhammad Muhammad Ridwan Musyafa?ah, Anis Nawawi, Kholil Panjaitan, Naura Hamidah Qomariah, Nurin Ramadhan, Aweldri Roziana Ariffin Ruminem, Ruminem safitri, gita Sari, Rita Puspa Shir Li Wang Sipayung, Siti Aisyah Siregar, Isnaini Sahara Siregar, Sa'adah Sumayyah Dzulkifly Surya Agustian Syah Utama, Isruel Syarifah Gustiawati Mukri Tarigan, Mardinal Theam Foo Ng Tumiran Vincentia Ade Rizky Wang, ShirLi Widiastuti, Ida Ayu Kade Sri Widoyoko, Riza Dwi Tyas Yusra, Yusra Z, Tomi