Article Per Year (5 Year)
p-Index From 2021 - 2026
3.527
P-Index
This Author published in this journals
All Journal Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Techno Nusa Mandiri : Journal of Computing and Information Technology IJISTECH (International Journal Of Information System & Technology) JISICOM (Journal of Information System, Infomatics and Computing) EVOLUSI : Jurnal Sains dan Manajemen REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Didaktik : Jurnal Ilmiah PGSD STKIP Subang IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Bulletin of Information Technology (BIT) J-Icon : Jurnal Komputer dan Informatika
Heny Sumarno, Heny
Universitas Bina Sarana Informatika
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
EMPLOYEE PAYROLL INFORMATION SYSTEM (CASE STUDY: PT SURYAMEGA JAYA STEEL) Panjaitan, Herlina; Afni, Nurul; Sumarno, Heny; Maulana, Yana Iqbal; Komarudin, Rachman
Journal of Information System, Informatics and Computing Vol 7 No 2 (2023): JISICOM (December 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v7i2.1256

Abstract

The payroll system is a series of business activities that aim to complete all payments for services that have been made by employees. Payroll is a process within an organization/agency that is prone to problems. Data processing that is still done manually can result in slow preparation of salary reports which results in delays in payment of salaries to employees plus if there is an error in calculating the salary it becomes inaccurate. In this study, the author tries to design a web-based payroll system at PT Suryamega Jaya Steel, which until now has not been computerized. This web-based system was written to simplify the payroll transaction process so that salary calculations are more accurate and employees can print payslips. The author designed a web-based payroll information system using the PHP programming language. The method used in designing the software for this payroll application is in the form of the waterfall method, data collection techniques, observation, and interviews. The design of this information system is the best solution to solve the problems that exist in this company. A computerized system is better than a manual system because a computerized system can run more safely than the system used before.
Rancangan Aplikasi Algoritma C4.5 pada Stunting Balita Menggunakan Bahasa Phyton Susliansyah Susliansyah; Sigit Yugi Wargiyo; Heny Sumarno; Hendro Priyono; Linda Maulida
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14426

Abstract

Stunting pada balita merupakan salah satu masalah kesehatan serius di Indonesia, yang memengaruhi pertumbuhan fisik dan kognitif anak. Dalam upaya memahami dan memprediksi faktor-faktor risiko yang berkaitan dengan stunting pada balita, digunakan teknologi data mining. Penelitian ini bertujuan mengembangkan aplikasi berbasis algoritma C4.5 untuk memprediksi status gizi balita, menggunakan bahasa pemrograman Python dan aplikasi Orange. Data yang berasal dari dataset "Stunting Toddler Detection" di Kaggle, dengan fokus pada variabel umur, tinggi badan, dan status gizi. Data tersebut digunakan sebagai bahan analisis, dengan tahapan preprocessing, integrasi data, hingga penerapan algoritma C4.5. Metode penelitian melibatkan pengolahan data menggunakan Python untuk analisis awal, sementara Orange dimanfaatkan untuk membangun pohon keputusan dan evaluasi model. Hasil pengujian menunjukkan algoritma C4.5 menghasilkan akurasi sebesar 36% di Orange dan 40% di Python, dengan faktor utama yang memengaruhi status gizi balita adalah tinggi badan. Aplikasi yang dikembangkan juga dilengkapi antarmuka visual untuk mempermudah tenaga kesehatan dan pemangku kebijakan dalam menganalisis risiko stunting.
Penerapan Klastering pada Data Mining dalam Menentukan Status Gizi Anak Balita dengan Menggunakan Algoritma K-Medoids Susliansyah Susliansyah; Heny Sumarno; Hendro Priyono; Linda Maulida; Fintri Indriyani
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15710

Abstract

Status gizi balita merupakan indikator penting yang mencerminkan kesehatan dan perkembangan anak. Penilaian gizi biasanya dilakukan melalui pengukuran berat badan, tinggi badan, serta perhitungan Indeks Massa Tubuh (IMT). Namun, proses klasifikasi secara manual seringkali membutuhkan waktu dan berisiko menimbulkan ketidaktepatan, sehingga diperlukan metode yang lebih efisien. Penelitian ini menggunakan pendekatan data mining dengan algoritma k-medoids untuk mengelompokkan status gizi balita. Algoritma ini bekerja dengan menentukan medoid sebagai pusat kelompok yang mewakili karakteristik balita berdasarkan tinggi, berat, dan IMT. Balita lain kemudian diklasifikasikan sesuai jarak terdekat dengan medoid tersebut. Hasil penelitian menunjukkan bahwa penerapan k-medoids mampu mengelompokkan balita ke dalam kategori normal, kurang gizi, dan obesitas secara lebih sistematis. Temuan ini diharapkan dapat membantu tenaga kesehatan dalam mengidentifikasi balita yang membutuhkan tindakan secara khusus, sehingga mendukung tumbuh kembang anak secara optimal.
Pengelompokan Data Penjualan Produk Cetakan Pada Algoritma K-Means Dengan Bantuan Tool Orange Susliansyah; Muhammad Ridho Caroko; Heny Sumarno; Hendro Priyono; Linda Maulida
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2659

Abstract

The main problem faced is the large amount of unstructured sales data, making it difficult to perform manual analysis. With the application of the K-Means algorithm, sales data can be grouped into clusters representing products with high and low sales. The research process begins with the stages of problem identification, data collection, preprocessing, application of the K-Means algorithm, evaluation of clustering results, and then analysis and interpretation. Iteration results show that cluster C1 consists of a number of high-selling sales data, while cluster C2 encompasses the majority of low-selling sales data. Evaluation using the Davies-Bouldin Index (DBI) yields a value of 0.2818, indicating fairly good cluster quality, while the Silhouette Plot provides values of 0.082 for C1 and 0.276 for C2, indicating that cluster C2 is more stable compared to C1. Scatter Plot visualization shows the data distribution forming a slanted pattern from C1 to C2. The result of this research is that by using the K-Means algorithm, it can effectively cluster sales data of printed products, so it can be used as a basis for business decision-making related to marketing strategies, stock control, and product performance evaluation.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN RASA GELATO BEST SELLER MENGGUNAKAN METODE ALGORITMA APRIORI PADA CAFE LOUKOUMANNA : Penelitian Menggunakan Metode Algoritma Apriori Aldo Alfiyanto; Azania Baruna; Nadira; Arief Rusman; Heny Sumarno
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 12 No. 3 (2026): Volume 12 No. 3, September 2026 Produce
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v12i3.17119

Abstract

The increasing amount of sales transaction data at Cafe Loukoumanna has not been optimally utilized as a basis for decision making. Sales data is generally only used as an archive without further analysis to identify customer purchasing patterns. This study aims to implement the Apriori Algorithm in a web-based Decision Support System to determine the best-selling gelato flavors based on historical transaction data. The research method adopted the CRISP-DM framework consisting of Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The association rule mining process was conducted using RapidMiner with a minimum support value of 0.10 and a minimum confidence value of 0.80. The generated association rules were then implemented into a web-based application developed using Laravel and MySQL. The results indicate that the Apriori Algorithm successfully identifies purchasing patterns based on customer age, purchase day, cup size, scoop quantity, and flavor combinations. The developed system assists Cafe Loukoumanna in determining best-selling flavors and supports promotional and inventory management strategies based on transaction data.
Co-Authors Aditia Dwinanto Aldo Alfiyanto Arief Rusman Azania Baruna Deleaniara. M, Chyntia Marianna Dinda Ayu Muthia Dyah Wulandari Fadillah, Khoirul Fintri Indriyani Hendro Priyon Hendro Priyono Hikmah, Noer I Gusti Bagus Wiksuana Indra Riyana Rahadjeng Linda Maulida Maulida , Linda Maulida, Linda Muhammad Ridho Caroko Nadira Nisan Nisan Nur Lutfiyana Nur, Muarif Zamzam Nurul Afni Panjaitan, Herlina Petrus Christo Priyono, Hendro Rachman Komarudin Sigit Yugi Wargiyo Susliansyah Susliansyah, Susliansyah WAHYUDI, DIKI Widi Atmaja Wijayanti, Annisa Dwi Yahdi Kusnadi Yana Iqbal Maulana