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Journal : sebatik

PENCARIAN DRIVER DRY CLEAN TERDEKAT DENGAN METODE HAVERSINE FORMULA Ekawati Yulsilviana; Pitrasacha Adytia; I Nengah Riandika
Sebatik Vol 25 No 1 (2021): Juni 2021
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.005 KB) | DOI: 10.46984/sebatik.v25i1.1210

Abstract

Sistem pemesanan dry cleaning konvensional tidak bisa menemukan driver terdekat dengan pemesan. Penelitian ini bertujuan untuk membangun sistem untuk membantu pencarian driver terdekat dengan pemesan agar cepat dalam mengambil pakaian kotor dan sebaliknya mencari driver terdekat dari dry cleaning jika pakaian sudah selesai. Penelitian ini menerapkan metode haversine formula untuk pencarian driver terdekat, Google Maps sebagai pembangun peta digital, dan dikembangkan berbasis mobile. Sistem perancangan pada penelitian ini menggunakan Unified Modeling Language (UML) yang terdiri dari Use Case Diagram, Activity Diagram, Class Diagram, Sequence Diagram, dan Deployment Diagram. Pengujian sistem dilakukan dengan menggunakan white box testing dan beta testing.
Integration of Field Data and Citizen Science in Spatial-Based Tropical Biodiversity Information Systems Bustomi , Tommy; Adytia, Pitrasacha
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2725

Abstract

Biodiversity monitoring in tropical regions is often constrained by limited field survey coverage and fragmented data. This study develops a spatial information system that integrates field data collected through an Android application, citizen science contributions from iNaturalist, and environmental indicators such as the Normalized Difference Vegetation Index (NDVI) and land cover. The integration process employs a spatial database and ETL mechanisms for data normalization, quality validation, and spatial joins with raster data. The implementation results demonstrate that the system is capable of displaying biodiversity distribution in an integrated interactive map. Although citizen science data provide significant contributions, challenges remain in terms of data quality, participation bias, and the protection of sensitive species. With appropriate methodological approaches, this system has the potential to serve as a supporting tool for biodiversity monitoring and data-driven conservation planning.
Prediction of the Number of New Students Using the Arima Time Series Model Case Study at Stmik Widya Cipta Dharma Eko Jheremy Oktavianus; Pitrasacha Adytia; Muhammad Ibnu Sa’ad
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/pv7pv465

Abstract

Planning for new student admissions is an important aspect in university management because it is closely related to strategic decision-making and institutional resource allocation. This study aims to estimate the number of new students in the Informatics and Information Systems Engineering Study Program at STMIK Widya Cipta Dharma using the Autoregressive Integrated Moving Average (ARIMA) method. The data used is secondary data obtained from PDDIKTI with a period of 2015-2025. The analysis process was carried out through several stages, namely stationary testing using the Augmented Dickey-Fuller (ADF) method, model identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), parameter estimation, diagnostic tests, and forecasting processes. The results showed that the best model obtained was ARIMA (0,1,0) after first-order differentiation. The model produces residual that meets the assumption of white noise and is normally distributed. The forecast results show a tendency to decrease the number of new students in the 2026–2028 period. The model evaluation showed a very good level of accuracy in the Informatics Engineering Study Program with a Mean Absolute Percentage Error (MAPE) value of 9.64% and quite good in the Information Systems Study Program of 24.88%. Thus, the ARIMA model (0,1,0) is considered effective in supporting the planning of new student admissions in a more measurable and systematic manner.