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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) JUSIM (Jurnal Sistem Informasi Musirawas) Bulletin of Computer Science Research Journal of Informatics Management and Information Technology
Asep Toyib Hidayat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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MEMBERSHIPPLICATION BERBASIS ANDROID DENGAN PENERAPAN KOTLIN PROGRAMMING LANGUAGE DI WIJAYA FITNESS CENTER (WFC) Asep Toyib Hidayat; Rio Rio; I Gede Olka Santosa
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 8 No 1 (2023): JUSIM (Jurnal Sistem Informasi Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

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

Abstract

Perkembangan teknologi informasi dan e-commerce saat ini menjadi fokus penting dalam dunia bisnis. Dengan memanfaatkan teknologi tersebut, pelaku bisnis dapat meningkatkan omset dan memberikan layanan informasi kepada konsumen. Dalam penelitian ini akan dibuat sebuah sistem berbasis android untuk Wijaya Fitness Center (WFC) yang berlokasi di Megang, Lubuk Linggau Utara II, Kota Lubuklinggau, Sumatera Selatan. WFC menyediakan tiga paket olahraga dan memiliki pengunjung lebih dari 50 orang setiap harinya. Namun saat ini, sistem manajemen data masih bersifat manual dan mengalami kendala dalam hal keamanan dan akses informasi. Dengan adanya sistem berbasis android, diharapkan dapat mempermudah proses pendaftaran anggota, pelaporan harian, dan memastikan keamanan data.
Penentuan Program Indonesia Pintar (PIP) Pada Siswa Kurang Mampu dengan Metode Preference Selection Index (PSI) Berbasis Web Nelly Khairani Daulay; Asep Toyib Hidayat; Shelfia Shepty
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.296

Abstract

The Indonesia Smart Program (PIP) is a scholarship assistance provided to individuals with the aim of continuing their education as tuition assistance. This program is the government's idea to reduce the high number of children who drop out of school due to lack of funds. This high dropout rate will also later lead to a high crime rate because children who drop out of school cannot work properly. To determine whether or not students are eligible to receive the Indonesia Pintar Program, a decision support system (SPK) is needed that can provide input for schools to assess properly, which students really deserve the scholarship. By using the Preference Selection Index (PSI) method, it is hoped that this method will be able to select the best alternative from a number of alternatives based on criteria from predetermined aspects.  In the Preference Selection Index (PSI) method which is used as a measure of assessment is the value of alternatives, matrix normalization, average performance value, preference variation value, preference value deviation, criteria weight, calculate the final value, and determine the ranking that will determine the optimal alternative, namely students who are entitled to a scholarship.  The purpose of this research is to determine which students are eligible and not eligible to receive assistance from the government in the form of PIP. The results of this study are in the form of rankings with the highest value of 0.7686 and ranked first or rank 1 and the lowest value of 0.7091 is ranked 5th.
Sistem Klasifikasi Kelayakan Penerima Bantuan Langsung Tunai Menggunakan Metode K-Nearest Neighbor (KNN) Berbasis Website Dewi Sriwani; Lukman Hakim; Nelly Khairani Daulay; Asep Toyib Hidayat
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.702

Abstract

Poverty is the condition of a person's inability to fulfill the basic needs of life, which is often measured by income that is lower than the average in a region. In Indonesia, the poverty rate, including in Kabupaten Jombang, continued to increase from 2012 to 2017, with various government efforts to overcome this through social programs such as BLT (Direct Cash Assistance), Community Health Insurance, and the Family Hope Program (PKH). However, despite these programs, the data collection process for beneficiaries in some areas, such as Tanah Periuk Village, is still done manually, causing inaccurate targeting in the provision of assistance. For this reason, a more efficient solution is needed in determining the eligibility of beneficiaries. One of the technologies that can be used is data mining, especially the classification method, to analyze beneficiary data based on certain criteria. This research uses the K-Nearest Neighbor (K-NN) algorithm to build a classification system for the eligibility of direct cash transfer recipients in Tanah Periuk Village, with the aim of improving accuracy and efficiency in the beneficiary selection process. This system is web-based, which allows ease of processing and updating data centrally. The results of this study provide eligibility scores for BLT (Direct Cash Assistance) recipients based on the criteria provided. The criteria that are assessed are: House Condition, Income, Occupation and Number of Dependents. One of the families who “DESERVE” to receive assistance is Martina with semi-permanent house conditions, an income of IDR 1,000,000/month, a housewife's job, and 3 dependents.
DETEKSI PENYAKIT TANAMAN APEL BERDASARKAN CITRA DIGITAL MENGGUNAKAN BROAD LEARNING SYSTEM (BLS) Risan Sandora; Asep Toyib Hidayat; Bunga Intan; Nelly Khairani Daulay
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 11 No 1 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Maret
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i1.2950

Abstract

This study applies the apple leaf disease classification method by combining the ResNet50 architecture as a feature extractor and the Broad Learning System (BLS) as a classifier. The developed system is used to identify five classes of apple leaf diseases, namely Apple scab, Black rot, Cedar apple rust, Healthy and Powdery mildew. The dataset used is 3,298 apple leaf images consisting of 630 Apple Scab images, 621 Black Rot images, 275 Cedar Apple Rust images, 1,645 Healthy images, and 127 Powdery Mildew images. The ResNet50 architecture is able to produce informative image feature representations so that the visual characteristics of each class can be optimally utilized by the BLS algorithm in the classification process. The results of testing the proposed model produce excellent performance of 97%. In addition, the precision, recall and F1-score also each reached 97%, indicating that the model is able to classify accurately and consistently across all classes of apple leaf diseases. Thus, the hybrid method of ResNet50 and BLS has been proven effective and has the potential to be developed as a support system for automatic identification of apple leaf diseases.
Co-Authors Bunga Intan Dewi Sriwani I Gede Olka Santosa Lukman Hakim Nelly Khairani Daulay Rio Rio Risan Sandora Shelfia Shepty