Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jambura Journal of Electrical and Electronics Engineering Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
Panna, sudirman S.
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PREDIKSI JUMLAH PRODUKSI KOPRA MENGGUNAKAN METODE REGRESI LINEAR BERGANDA PADA UMKM MANDIRI DESA LION: Indonesia ABDUL_MUHRIZAL_ZULKIFLI_H_MARADA; Haditsah Annur; Yunus, Warid; Panna, sudirman S.
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 4 No 2 (2025)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v4i2.958

Abstract

Abstract This study aims to apply multiple linear regression methods in predicting copra total production at CV. Lion Utama. The results of system testing show that the value of V(G) = CC = 2 indicates that the system meets the requirements of programming logic and is not complex. Black-Box Testing also shows that the system is free of component errors. The prediction of copra total production for January 2022 using the multiple linear regression method gives valid results. The accuracy of this prediction is measured by Mean Absolute Percentage Error (MAPE), resulting in a value of 77.27%, indicating a Fairly High level of accuracy in copra production estimation. Keywords: production prediction, copra, multiple linear regression, CV. Lion Utama, MAPE
Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm Hasan, Maryam; Panna, Sudirman S.; Haba, Abd. Rahmat Karim; Alhamad, Apriyanto
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33145

Abstract

The Province of Gorontalo is highly vulnerable to flood disasters due to its geographical conditions, high rainfall, and uncontrolled land-use changes. This study aims to apply the K-Means Clustering algorithm to classify regions based on flood impact levels to support disaster mitigation and decision-making processes by the National Search and Rescue Agency (BNPP) Gorontalo. The dataset comprises 405 disaster incident records obtained from related institutions, including the number of affected, injured, deceased, and missing individuals. The analysis process involves data collection, preprocessing, distance calculation using the Euclidean Distance method, and the formation of two clusters based on impact levels. The iteration process stopped at the second iteration, indicating that a stable (convergent) condition had been achieved. The results revealed that Cluster 1 (C1) includes areas significantly affected by floods such as Imana, Iloheluma, and Tudi villages, while Cluster 2 (C2) represents unaffected areas like Wapalo, Ilomata, Motihelumo, and others. The implementation of the K-Means algorithm proved effective in identifying disaster-prone regions objectively and data-driven, thus supporting more efficient disaster response planning.
Co-Authors ABDUL_MUHRIZAL_ZULKIFLI_H_MARADA Apriyanto Alhamad Haba, Abd. Rahmat Karim Haditsah Annur Maryam Hasan Warid Yunus