Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Indonesian Journal of Agriculture and Environmental Analytics Journal of Computer Science Artificial Intelligence and Communications
Nainggolan, Theodora MV
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Health Professions Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Effect of Chicken Manure and Magnesium on the Growth of Arabica Coffee (Coffea arabica L.) Sigararutang Siahaan, Lasminar; Siregar, Rolan; Nainggolan, Theodora MV; Oppusunggu, Lastayati
Indonesian Journal of Agriculture and Environmental Analytics Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijaea.v4i1.13174

Abstract

Effect of Chicken Manure and Magnesium on the Growth of Sigararutang Arabica Coffee. The purpose of the study was to determine the effect of chicken manure and magnesium on the growth of sigararutang arabica coffee. The research was conducted on the land of Fak. Unita Agriculture, with an altitude of ± 1400 m above sea level. Factorial Randomized Group Design (RAK), namely 4 levels of chicken manure K0 (control), K1 (1 kg plot), K2 (3 kg plot), K3 (5 kg plot) and 4 levels of magnesium M0 (control), M1 (10 g/ltr water), M2 (20 g/ltr water), M3 (30 g/ltr water). Parameters observed were plant height increase (cm), stem diameter (mm), leaf area (cm2), number of primary branches (branches) and leaf magnesium nutrient content (%). Treatments were tested with Anova on the observed parameters and continued with DMRT at 5% level and regression test. The results showed that chicken manure treatment had a significant effect on the increase in plant height (cm), stem diameter (mm), number of primary branches (branches) and leaf area (cm2). The treatment interaction significantly affected the number of primary branches (branches), leaf area (cm2) and leaf magnesium nutrient content (%). The treatment of chicken manure at the level of 5 kg/plot had the highest results on the increase in plant height, number of primary branches, leaf area. The interaction of 5 kg/plot chicken manure and 30 g/ltr water magnesium treatment resulted in the highest number of primary branches and leaf area.
Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions Damayanti, Fera; Budiman, Arief; Sundari, Siti; Nainggolan, Theodora MV
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.59

Abstract

Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.
Analysis of the Effectiveness of Implementing a Queue Algorithm-Based Leadership Scheduling Information System in Government Agencies Mardiah; Nuranisah; Nainggolan, Theodora MV
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.61

Abstract

This study analyzes the effectiveness of implementing a leadership scheduling information system that utilizes queue algorithms in government agencies. The main objective is to evaluate how the integration of algorithm-based scheduling systems improves efficiency, accuracy, and transparency in managing executive-level appointments and meetings. The research adopts a mixed-method approach, combining quantitative analysis through system performance metrics with qualitative feedback from end-users, including administrative staff and decision-makers. Findings indicate a significant improvement in scheduling efficiency, with reduced conflicts, optimized time slots, and better coordination between departments. Furthermore, the system minimizes manual intervention, thus decreasing administrative errors and enhancing data integrity. The queue algorithm enables a first-come-first-served mechanism that ensures fairness while allowing for priority-based modifications in urgent cases. The implementation also receives positive responses in terms of user satisfaction and perceived usefulness. However, challenges such as user adaptation and technical limitations were identified, suggesting a need for continuous training and system updates. Overall, the integration of a queue algorithm-based scheduling system proves to be an effective solution for improving leadership-level administrative processes in government institutions.
Co-Authors Arief Budiman Damayanti, Fera Jayawarsa, A.A. Ketut Mardiah Nuranisah Oppusunggu, Lastayati Siahaan, Lasminar Siti Sundari