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All Journal Teknika EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Jurnal TAM (Technology Acceptance Model) IndraTech Jurnal Pengabdian kepada Masyarakat Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Mandiri NEAR: Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal PETISI (Pendidikan Teknologi Informasi) INOVTEK Polbeng - Seri Informatika Jurnal TAM (Technology Acceptance Model)
Nursiyanto
Institut Informatika Dan Bisnis Darmajaya
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Assessment of Usability and Acceptance of An Academic Information System Using SUS And TAM Adaptation Nurlistiani, Rini; Romadona, Romadona; Kurniawan, Hendra; Nursiyanto, Nursiyanto
Prosiding International conference on Information Technology and Business (ICITB) 2023: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 9
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Organizations, companies, and the world of education carry out all learning activities using e-learning. There is an important part that requires an academic system with structured data, namely the system at private universities in Indonesia, for example,Informatics and Business Institute Darmajaya. Darmajaya is one of the educational institutes that uses online learning media information technology called e-learning for students and lecturers. The newest information system used at IIB Darmajaya is the academic information system (AIS) which consists of Darmajaya students and lecturers. Result from the assessment showing of lecturers understand how to use AIS with value 56.92, and 65.93 from students of IIB Darmajaya. Keywords :SUS,TAM, Evaluation, Acceptance, Usability
Pelatihan Manajemen Usaha Bagi Pokdarwis Kawasan Strategis Teluk Lampung Kabupaten Pesawaran Lukmanul Hakim; Suhendro Yusuf Irianto; Nursiyanto Nursiyanto; Sushanty Saleh
NEAR: Jurnal Pengabdian kepada Masyarakat Vol. 3 No. 2 (2024): NEAR
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/nr.v3i2.1322

Abstract

Wisata bahari di kawasan strategis Teluk Lampung kabupaten Pesawaran memiliki potensi yang besar untuk wisata provinsi Lampung. Manajemen usaha merupakan salah satu upaya mengatur segala hal dalam menjalankan usaha sehingga dapat mencapai tujuan yang diharapkan. Manajemen usaha yang dimaksudkan antaranya yaitu manajemen sumber daya manusia, manajemen pemasaran, dan manajemen keuangan. Pokdarwis merupakan kelompok masyarakat sadar wisata di kabupaten Pesawaran. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan manajemen usaha sebagai upaya untuk mengedukasi pengelolaan usaha bagi Pokdarwis Kawasan strategis pesisir Teluk Lampung. Hasil kegiatan ini adalah Pokdarwis memiliki kemampuan dalam manajemen usaha yang baik untuk pengelolaan jangka panjang objek wisata Bahari yang dikelola dan peningkatan usaha Pokdarwis.
Pengembangan Dashboard Data Akademik Berbasis Web untuk Monitoring Siswa dan Guru di SMA Negeri 1 Tanjung Bintang Robin Abednego Sudibyo; Nursiyanto Nursiyanto
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 16, No 1 (2026): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v16i1.4699

Abstract

ABSTRACT – The management of academic data, including student data, teacher data, attendance, and grades, is anessential aspect of supporting effective school administration. However, in practice, academic data are often storedin separate systems, making it difficult for schools to monitor information and make decisions quickly and accurately.This study aims to develop a web-based academic data dashboard that presents integrated and easily accessibleacademic information at SMA Negeri 1 Tanjung Bintang. The system was developed using the waterfall method,which consists of requirements analysis, system design, implementation, and testing stages. The developed dashboardpresents key academic information, including student data, teacher data, attendance, and grades, through informativevisualizations. The results indicate that the web-based academic data dashboard facilitates academic monitoring andimproves the efficiency of academic data management to support decision-making processes.
ANALISIS PERBANDINGAN ALGORITMA KLASIFIKASI UNTUK PREDIKSI DIABETES MELLITUS MENGGUNAKAN PENDEKATAN MACHINE LEARNING Shelawati, Erina; Nursiyanto, Nursiyanto
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol. 8 No. 1 (2025): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : LP2M Institut Informatika Dan Bisnis Darmajaya

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Abstract

Diabetes Mellitus is a chronic disease with an increasing prevalence worldwide and poses serious health risks if not detected early. Early prediction of diabetes is crucial to support preventive actions and improve healthcare decision-making. This study aims to analyze and compare the performance of several machine learning classification algorithms in predicting Diabetes Mellitus based on clinical data. The dataset used in this research is the Pima Indians Diabetes dataset, which consists of medical attributes such as glucose level, blood pressure, insulin, body mass index, and age. Data preprocessing was conducted to handle missing values and improve data quality. Three classification algorithms, namely Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were implemented and evaluated using k-fold cross-validation. The performance of each algorithm was measured using accuracy, precision, recall, F1-score, and confusion matrix. The experimental results show that the Random Forest algorithm achieved the highest performance compared to SVM and KNN, with superior accuracy and more balanced classification results. This indicates that ensemble-based methods are more effective in handling medical data with complex patterns. In conclusion, machine learning algorithms can be effectively applied to predict Diabetes Mellitus, and Random Forest is recommended as the most suitable algorithm for this dataset. The results of this study are expected to contribute to the development of intelligent decision support systems in the healthcare domain. Keywords: Classification; Diabetes Mellitus; Machine Learning; Random Forest; Support Vector Machine; K-Nearest Neighbor
Implementasi Platform Digital Berbasis Web Untuk Manajemen Aset dan Inventarisasi BPBD Lampung Yulia Astuti; Halimah Halimah; Nursiyanto Nursiyanto; Ochi Marshella Febriani
TEKNIKA Vol. 19 No. 3 (2025): Teknika September 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15566643

Abstract

Meningkatnya kebutuhan akan transparansi dan efisiensi dalam pengelolaan aset di Badan Penanggulangan Bencana Daerah (BPBD) Provinsi Lampung menjadi latar belakang dilakukannya penelitian ini. Proses inventarisasi secara manual yang selama ini digunakan terbukti kurang efektif, rawan kesalahan, dan menyulitkan dalam pelaporan. Penelitian ini bertujuan untuk mengimplementasikan platform digital berbasis web yang mampu mendukung manajemen aset dan inventarisasi secara terintegrasi. Metode yang digunakan dalam penelitian ini adalah metode pengembangan perangkat lunak dengan pendekatan waterfall, yang meliputi tahap analisis, desain, pengkodean, dan pengujian sistem. Hasil penelitian menunjukkan bahwa sistem yang dibangun dapat digunakan oleh dua jenis pengguna, yaitu admin dan pimpinan, dengan pembagian hak akses yang jelas. Fitur-fitur utama meliputi pengelolaan data aset, inventaris masuk dan keluar, kategori, lokasi, serta dashboard yang menampilkan informasi secara visual dan real-time. Pengujian sistem menggunakan metode black-box testing menunjukkan seluruh fitur berjalan sesuai fungsi yang diharapkan tanpa ditemukan kesalahan, sehingga sistem ini dinyatakan layak digunakan.
Co-Authors Abda Abda AGUS SETIAWAN amnah amnah Anggi Andriyadi Anggi Andriyadi anggi andriyadi Asep Eryana Awang Prayoga Ayu Firdayanti Ayu Firdayanti Ayu Firdhayanti Baehaqi Bobby Bachry Cristian Munthe, Yohanes Deppi Linda Deppi Linda, Deppi Linda Dona Yuliawati Dona Yuliawati Ezra Teofilus Pratama Halimah Halimah Hendra Kurniawan Hendri Purnomo I Gusti Wayan Murjana Yasa Indera Indera Indera Innani, Turzina Melda Agarina Muchirdyantoro, Dede Neni Purwati Nurlistiani, Rini Ochi Marshella Febriani Ossy Dwi Endah Wulansari Ossy Dwi Endah Wulansari, Ossy Dwi Endah Robin Abednego Sudibyo Romadona, Romadona Ronaldo Ronaldo Shelawati, Erina Sri Karnila Sriyanto, Sriyanto Sugeng Santoso Sushanty Saleh Sushanty Shaleh T. M. Zaini TM Zaini Wasilah Wasilah Winda Rika Lestari Wiryawan, Driya Y. Suhendro Yulia Astuti Zaini, Teuku Muhammad Zaini, TM Zeppelin, Zulham Syarifuddin