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All Journal IPSIKOM Intechno Journal : Information Technology Journal
Muhammad Arif Kurniawan
Universitas Insan Pembangunan Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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OPTIMALISASI PENGELOLAAN DATA TANAH MENGGUNAKAN ARCGIS DI BADAN BANK TANAH Andi Rukmana; Angger Styo Yuniarti; Samsul Makin; Muhammad Arif Kurniawan; Ferdi Kuswandi
IPSIKOM Vol. 13 No. 1 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i1.419

Abstract

Badan Bank Tanah, which is primarily engaged in the land sector, requires technology that can manage maps centrally and can be used collaboratively with both internal and external providers through service connections to support analysis and monitoring of HPL assets by related stakeholders based on the level of access provided. Currently, Badan Bank Tanah does not have a centralized GIS system, the process of creating shape files and map polygons is still carried out by each staff. This results in the information, database types, and data structures created being non-standard, and results in the shape file and map polygon databases being stored on each staff's work tools. The risk that can occur from this is the loss or misinformation needed, so that if needed, it takes time to prepare the data. The solution to the application being sought is software that has the capability to store data in a standardized manner, can be connected to external GIS applications through service methods, and is centralized so that the data can be stored in a secure environment
ANALISIS SENTIMEN MASYARAKAT TWITTER TERHADAP KEBIJAKAN EFISIENSI ANGGARAN KEMENTERIAN MENGGUNAKAN SVM Muhammad Arif Kurniawan; Samsul Makin; Angger Styo Yuniarti; Andi Rukmana
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.452

Abstract

Sentiment analysis of the ministry's budget efficiency policy is crucial to understanding public responses to government policies. This study employs the support vector machine (SVM) method to classify positive and negative sentiments from 1,418 tweets collected through crawling using Twitter API v2 between February 10 and 22. The text processing steps include case folding, cleaning, tokenizing, stopword removal, stemming, and weighting using the term frequency-inverse document frequency (TF-IDF) method. The analysis results indicate that negative sentiment dominates over positive sentiment, reflecting public criticism and dissatisfaction with the policy. The SVM model was evaluated using k-fold cross-validation with k values ranging from 2 to 10, achieving the best accuracy of 94.76% with 10-fold validation. Evaluation using the confusion matrix showed a precision of 92.85%, a recall of 91.32%, and an AUC of 0.972, indicating excellent model performance in sentiment classification. These findings suggest that the SVM model is effective in analyzing public sentiment toward government policies and can be further developed by enriching features and comparing it with other algorithms to enhance prediction accuracy.
Comparing ARIMA and Single Exponential Smoothing for Spare-Part Demand Forecasting: A Case Study at PT XYZ Angger Styo Yuniarti; Muhammad Arif Kurniawan
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2879

Abstract

Purpose: This study aims to compare the forecasting performance of the Autoregressive Integrated Moving Average (ARIMA) and Single Exponential Smoothing (SES) methods using Battery DYC spare-part demand data from PT XYZ as an industrial case study to identify the most appropriate forecasting approach for inventory planning. Methods: Monthly sales data from January 2020 to May 2026 were analyzed using a quantitative time-series approach. The dataset was divided into training data (65 observations) and testing data (12 observations). The ARIMA model was developed according to the Box-Jenkins procedure, while the SES model used the optimal smoothing parameter estimated by SPSS. The forecast performance was assessed in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Result/Findings: The results show that ARIMA (0,0,1) model has RMSE of 3.8370, MAE of 2.8925 and MAPE of 53.21%. SES (alpha = 0.052) has RMSE of 3.8336, MAE of 3.0000 and MAPE of 55.19%. SES is slightly better than ARIMA in terms of RMSE but ARIMA is better in MAE and MAPE, thus ARIMA is more robust overall. Novelty/Originality/Value: The study provides empirical evidence from an industrial case study that forecasting performance is dependent on demand characteristics, rather than on the universal superiority of one method over another. The findings offer practical guidance for spare-part inventory planning and provide a basis for future comparisons with specialized intermittent demand forecasting methods.
Co-Authors Andi Rukmana Andi Rukmana Angger Styo Yuniarti Ferdi Kuswandi Samsul Makin