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Measuring The Efficiency of Social Assistance Recipients in the Family Hope Program Using the Data Envelopment Analysis Method Hasibuan, Fadilah Suryani; Abdullah, Dahlan; Nunsina, Nunsina
ITEJ (Information Technology Engineering Journals) Vol 10 No 2 (2025): December (In Progress)
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i2.268

Abstract

This study aims to measure the efficiency of aid distribution in the Family Hope Program (PKH) across six sub-districts in Medan Denai using the Data Envelopment Analysis (DEA) method. DEA allows a relative efficiency evaluation of multiple decision-making units (DMUs) by comparing inputs such as budget, number of officers, and recipients, against outputs like targeted beneficiary rate, distribution timeliness, and satisfaction level. The findings reveal that three sub-districts—Medan Tenggara, Tegal Sari Mandala I, and Tegal Sari Mandala II—achieved full efficiency with a score of 1. In contrast, Binjai, Denai, and Tegal Sari Mandala III were found to be inefficient due to higher input consumption not matched by proportional output. The study suggests that inefficient sub-districts can improve their performance by adopting the practices of efficient ones. These insights are expected to assist local governments in optimizing social assistance programs and ensuring better resource utilization.
Design of an Internet of Things (IoT)-Based Fish Feeder System Using an Android Application Ariyandi, Zulham; Taufiq, Taufiq; Nunsina, Nunsina
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10072

Abstract

Fish farming plays a crucial role in aquaculture, where feed management is a key factor affecting productivity and operational costs. This research presents the design and implementation of an Internet of Things (IoT)-based automatic fish feeder system, integrated with a custom Android application. The system uses an ESP32 microcontroller to control a load cell sensor for accurate feed weighing, an ultrasonic sensor to monitor feed availability, servo motors for feed release mechanisms, and a DC motor for feed dispersion. Firebase Realtime Database serves as the data communication medium between the hardware and mobile application, enabling real-time control and monitoring. A rule-based control logic is implemented to execute scheduled or manual feeding processes. Experimental results show a feed weight accuracy of ±5 grams, with feeding operations completed within 1.5 minutes and an average throw distance of 287.8 cm. The system supports automatic alerts, scheduling, feed history logging, and remote access via the application. Compared to conventional manual methods, the system reduces feed waste, increases portion accuracy, and decreases feeding time by over 75%. These features demonstrate the system’s capability to enhance feeding efficiency, reduce labor dependency, and support sustainable and scalable fish farming practices through automation and real-time monitoring.