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All Journal Jurnal Ilmiah Sains dan Teknologi Jurnal Abdimas Bina Bangsa
Hilman, Mohamad
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Jurnal Ilmiah Sains dan Teknologi

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Sistem Informasi Permintaan Barang Dan Jasa Fasilitas Umum Hilman, Mohamad; Hidayanti, Nur; Nuryani, Ely; Budiman, Ramdani
Jurnal Ilmiah Sains dan Teknologi Vol 7 No 2 (2023): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v7i2.2642

Abstract

The Banten Province Land Transportation Management Office (BPTD) Region VIII oversees many Service Units (SATPEL) such as type A bus terminals, crossing ports, and weigh stations in Banten Province. The General Bureau is a Work Unit (Satker) tasked with handling requests for goods/services. Some problems occur in the process of requesting goods/services in the Office of the Land Transportation Management Center (BPTD) Region VIII of Banten Province, namely the submission process is still manual, so it takes a long time and the status of approval from each party who has a decision-making policy cannot be known directly. So to overcome the problems faced, it is necessary to design an information system for requests for goods and services that use the Waterfall method and the PHP and MySQL programming languages ​​to create an expected information system, so that data processing can be processed more quickly and in control. Thus each Service Unit (Satpel) can submit a letter of submission of goods by accessing the goods/services procurement web online. Keywords: Service Goods, Demand, Information Systems, Transportation, Waterfall.
Perancangan Aplikasi Surat Keteranggan Pengantar Ijazasah Berbasis Web Sukarna, Royan Habibie; Krisdianto, Nanang; Hilman, Mohamad; Holilah, Holilah; Januriana, Andi Moch; Umam, Ahmad Khaerul
Jurnal Ilmiah Sains dan Teknologi Vol 8 No 1 (2024): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v8i1.2977

Abstract

This study explains how to create and implement a web-based application for Certificate of Introduction to Diplomas (SKPI) using the Laravel framework and the Extreme Programming (XP) method. With SKPI as a confirmation of competency after graduation, this application aims to record student competency during the course. The XP method is used to ensure flexibility, collaboration and sustainability in application development. The Laravel framework was chosen for its ease of use and strength in backend/API creation, which allows integration with other applications. The research results show that the SKPI application was successfully created with features that meet the need for recording and verifying student competency. This research contributes to a practical understanding of web-based application development with a focus on student competency track records
Analisis Prediksi Kelulusan Mahasiswa Universitas Sultan Ageng Tirtayasa Menggunakan Algoritma Machine Learning dan Feature Selection Sukarna, Royan Habibie; Holilah, Holilah; Damyati, Fitri; Hilman, Mohamad
Jurnal Ilmiah Sains dan Teknologi Vol 8 No 2 (2024): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v8i2.3468

Abstract

The KNN algorithm with feature selection achieved the highest accuracy of 74.44% and an Area Under the Curve (AUC) of 0.8212. This model showed a balanced accuracy improvement compared to its performance using the dataset with complete features, which had an accuracy of 72.83% and an AUC of 0.8071. Similarly, the Random Forest model with feature selection showed an accuracy of 72.00% and an AUC of 0.7741, compared to an accuracy of 70.52% and an AUC of 0.7672 with all features. The SVM model with feature selection also improved, reaching an accuracy of 72.28% and an AUC of 0.7812, compared to an accuracy of 69.80% and an AUC of 0.774 with all features. Logistic Regression showed minimal change, with an accuracy of 69.14% and an AUC of 0.7644 after feature selection, compared to an accuracy of 69.25% and an AUC of 0.7645 with all features.
Co-Authors Budiman, Ramdani Damyati, Fitri Hidayanti, Nur Holilah Holilah, Holilah Januriana, Andi Moch Krisdianto, Nanang Nuryani, Ely Sukarna, Royan Habibie Umam, Ahmad Khaerul