Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal Jurnal ULTIMATICS Journal of Information System, Applied, Management, Accounting and Research Sistematis
Saputra, Muhammad Ikhwani
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
IMPLEMENTASI ALGORITMA DECISION TREE DALAM OPTIMASI PENILAIAN KINERJA OPERASIONAL Maulana, Rijwan; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
Journal of Information System, Applied, Management, Accounting and Research Vol 8 No 1 (2024): JISAMAR (December-February 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v8i1.1432

Abstract

Companies nowadays derive significant benefits from adopting digital systems in their operations. Politeknik LP3I Jakarta, for example, an educational institution, has implemented digitalization to optimize operational performance assessments by auditors. Manual audit assessments consume significant time and effort. Applying intelligent algorithms, such as Decision Tree, has become key to enhancing efficiency and accuracy in decision-making during assessments. The objective of this research is to evaluate the effectiveness of operational performance assessments by identifying and addressing these issues, implementing the Decision Tree algorithm. Decision Tree, which has evolved through a series of algorithms like C5.0, is the appropriate choice as it can make decisions based on specific conditions from the input data. The Decision Tree method and C5.0 Algorithm will help in rapidly and accurately calculating assessments based on criteria. This implementation can minimize manual audit activities, save time, and improve the accuracy of operational performance evaluations. The outcome of this research is higher efficiency in the operational performance assessment process, allowing auditors to focus on strategic aspects and in-depth analysis. Thus, Politeknik LP3I Jakarta and similar institutions can experience positive impacts from technology integration in their operational management, supporting progress and enhancing the quality of educational services.
Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3874

Abstract

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.
Prediksi Penjualan Untuk Optimasi Stock Produk Menggunakan Algoritma Long Short Term Memory Susilo, Dani; Ahmad Chusyairi; Saputra, Muhammad Ikhwani
Sistematis Vol. 1 No. 2 (2025): April 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/56gyat30

Abstract

Perusahaan distribusi menjadi pihak yang bertanggung jawab atas proses penyaluran barang dan menjadi perantara antara produsen dengan konsumen. Permasalahan utama yang sering dihadapi perusahaan distribusi adalah terkait dalam pengadaan stok barang yang dapat menyebabkan kelebihan atau kekurangan  stok. Penelitian terkait distribusi barang lebih fokus pada pendekatan sederhana atau pemodelan berbasis metode klasik, yang kurang efektif dalam meramalkan penjualan dengan tingkat akurasi yang tinggi. Oleh karena itu, pengembangan model prediksi berbasis algoritma deep learning, seperti Long Short-Term Memory (LSTM), yang dapat menangani dependensi jangka panjang dalam data time series, masih terbatas dalam konteks perusahaan distribusi, khususnya dalam meningkatkan efisiensi pengelolaan stok barang dan pengurangan kesalahan pengadaan stok.Tujuan penelitian ini untuk mengembangkan model prediksi penjualan menggunakan algoritma Long Short Term Memory (LSTM) guna meningkatkan efisiensi pengelolaan stok barang pada perusahaan distribusi XYZ. Dengan memanfaatkan data historis penjualan yang berbentuk time series, penelitian ini memprediksi penjualan di masa depan dan menghasilkan prediksi per produk dan per bulan. Evaluasi model menggunakan Mean Absolute Percentage Error (MAPE) menghasilkan tingkat kesalahan rata-rata sebesar 3,60%, hal ini menunjukkan bahwa model memiliki akurasi yang sangat akurat. Hasil prediksi ini diintegrasikan kedalam sistem pengadaan stok untuk mengoptimalkan rekomendasi pengadaan stok dalam proses pembelian barang. Penelitian ini menunjukkan bahwa penerapan  LSTM dalam prediksi penjualan dapat menjadi solusi efektif bagi perusahaan distribusi dalam pengelolaan stok dan efisiensi biaya operasional.
Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3874

Abstract

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.
Co-Authors Chusyairi, Ahmad Cian Ramadhona Hassolthine Kiswara, Ade Maulana, Rijwan Susilo, Dani Tobing, Fenina Adline Twince