Article Per Year (5 Year)
p-Index From 2021 - 2026
1.937
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi dan Informasi Jurnal Mnemonic INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Information Systems and Technology (JISTech) Media of Computer Science Jurnal Pusat Kolaborasi Pengabdian Kepada Masyarakat JROCK (Journal Research on Computing Knowledge)
Syaban, Kharis
Universitas Sembilanbelas November Kolaka
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Information Systems and Technology (JISTech)

 1 
Optimizing Information System Utilization through Strategic Planning using Ward-Peppard and Cassidy Methodology Kharis Syaban; Hamsinar, Henny
JISTech : Journal of Information Systems and Technology Vol. 2 No. 1 (2025): Juni 2025
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71234/jistech.v2i1.32

Abstract

This research aims to optimize the utilization of information systems (IS) at Universitas Sembilanbelas November Kolaka through a strategic planning approach using the Ward-Peppard and Cassidy Methods. In the context of globalization and intense competition in higher education, appropriate strategies in IS management are crucial for institutional success. The Ward-Peppard and Cassidy Methods are used to design an IS strategic plan integrated with the organization's goals. The methodological steps include analysing the university's internal and external environment, identifying stakeholder needs and expectations, and formulating IS strategies that consider available resources and future development directions. A case study was conducted at Universitas Sembilanbelas November Kolaka, using a qualitative approach involving in-depth interviews with university leaders, administrative staff, and IS users. The research findings identify challenges in IS utilization, including a lack of integration between existing systems, the need for human resource skills development, and the maintenance of high-quality IT infrastructure. This study provides a comprehensive view of the IS strategies needed to enhance operational effectiveness and achieve the university's strategic goals. The practical implications of this research are the development of an action plan that can help the university manage and optimize their IS investments more effectively
Pengembangan Sistem Informasi Seleksi Penerimaan Bantuan Langsung Tunai Menggunakan Metode K-Nearest Neighbor Hardianto; Kharis Syaban; Ery Muchyar Hasiri
JISTech : Journal of Information Systems and Technology Vol. 1 No. 2 (2024): Desember 2024
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

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

Abstract

The implementation of the Cash Transfer Assistance (BLT) program in Indonesia aims to assist economically disadvantaged communities, especially during crises such as the pandemic. However, the selection process for BLT recipients often faces challenges related to accuracy and efficiency, particularly in determining eligible recipients based on various economic and social criteria. This research develops an information system based on the K-Nearest Neighbor (K-NN) method to address these issues. The system is designed to classify BLT candidates by considering several variables, such as family income, number of dependents, employment status, housing conditions, and family health. The optimal K value was determined through trial and error to achieve the highest accuracy. The system was tested using both training and testing data, and the evaluation results showed an accuracy rate of 85%. This information system not only processes data quickly but also provides transparent and objective results, making it useful for village authorities to efficiently select BLT recipients. By implementing the K-NN algorithm, this system is expected to offer a practical solution for village governments in improving the accuracy of aid distribution to eligible communities.
Analisis Kinerja Algoritma Random Forest untuk Klasifikasi Penyakit Diabetes di Puskesmas Wundulako Nurul Aisyah Fitri; Kharis Syaban; Nisa Miftachurohmah
JISTech : Journal of Information Systems and Technology Vol. 2 No. 2 (2025): Desember 2025
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71234/jistech.v2i2.104

Abstract

Penyakit diabetes merupakan salah satu penyakit kronis dengan prevalensi yang terus meningkat dan memerlukan deteksi dini untuk mencegah komplikasi yang lebih serius. Puskesmas sebagai fasilitas layanan kesehatan tingkat pertama memiliki peran penting dalam melakukan identifikasi awal terhadap pasien berisiko diabetes. Namun, proses klasifikasi penyakit diabetes yang masih dilakukan secara manual berpotensi menimbulkan keterlambatan dan ketidaktepatan dalam pengambilan keputusan. Oleh karena itu, penelitian ini bertujuan untuk menganalisis kinerja algoritma Random Forest dalam mengklasifikasikan penyakit diabetes menggunakan data rekam medis pasien di Puskesmas Wundulako. Data yang digunakan berjumlah 227 data pasien dengan beberapa variabel prediktor, antara lain usia, indeks massa tubuh, gula darah sewaktu, tekanan darah, dan variabel kesehatan lainnya. Proses klasifikasi dilakukan menggunakan algoritma Random Forest dengan skema pengujian 5-Fold Cross Validation untuk memastikan kestabilan model. Hasil penelitian menunjukkan bahwa algoritma Random Forest menghasilkan akurasi rata-rata sebesar 99.57% dengan nilai precision 100%, recall 98.75%, dan F1-score 99.35%, serta error rate yang sangat rendah. Analisis feature importance menunjukkan bahwa gula darah sewaktu merupakan variabel paling dominan dalam menentukan klasifikasi diabetes. Berdasarkan hasil tersebut, algoritma Random Forest terbukti memiliki kinerja yang sangat baik dan berpotensi digunakan sebagai sistem pendukung keputusan dalam deteksi dini penyakit diabetes di tingkat pelayanan kesehatan dasar.
Co-Authors Al Jum'ah, Muhammad Na'im alders paliling Arysespajayadi Arysespajayadi Asriani, Ika Dimas Febriyan Priambodo Dirman Ery Muchyar Hasiri Hamid Wijaya Hardianto Henny Hamsinar Hidayatullah, Muh. Ilham Ismail, Rima Ruktiari Jayanti Yusmah Sari La Ode Hasnuddin S. Sagala Mardiawati Miftachurohmah, Nisa Muh. Na’im Al Jum’ah Muliyadi Mutmainnah Muchtar Nirsal Nirsal Nisa Miftachurohmah Nisa Miftahurochmah Nurjannah Nurjannah Nurul Aisyah Fitri Rabiah Adawiyah Rahmat Karim Resitha Sampe Membunga Rima Ruktiari Sabi, Musini Sarimuddin, Sarimuddin Suci Pricilia Lestari Suharsono Bantun Sunyanti Sunyanti, Sunyanti