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Use of Solar Energy for Street Lighting in Rural Java Soelistianto, Farida Arinie; Khristiana, Harrij Mukti
West Science Interdisciplinary Studies Vol. 4 No. 01 (2026): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v4i01.2596

Abstract

The utilization of renewable energy has become a strategic priority in supporting sustainable development, particularly in rural areas with limited access to conventional energy infrastructure. This study aims to analyze the utilization of solar energy for street lighting in rural areas of Java, focusing on community perceptions and acceptance. A quantitative research approach was employed using a structured questionnaire distributed to 85 respondents residing in villages equipped with solar-powered street lighting systems. Data were collected using a five-point Likert scale and analyzed with SPSS version 25. The analysis included descriptive statistics, reliability testing, and multiple linear regression to examine the influence of perceived effectiveness, efficiency, safety and social impact, and sustainability on community acceptance. The results indicate that all perception dimensions have a positive and statistically significant effect on acceptance of solar-powered street lighting, with perceived safety and social impact emerging as the most influential factor. Overall, the findings demonstrate that solar-powered street lighting is perceived as effective, cost-efficient, socially beneficial, and environmentally sustainable. This study provides empirical evidence to support the expansion of solar energy-based public lighting as a viable strategy for enhancing rural infrastructure and promoting sustainable energy transitions in Indonesia.
Designing an Object Detection System as an Assistive Device for the Visually Impaired Based on Yolo V10 with Dual Camera Nabila, Amalia; Soelistianto, Farida Arinie; Mahfudi, Isa
West Science Information System and Technology Vol. 3 No. 03 (2025): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v3i03.2209

Abstract

This research develops an object detection system to assist visually impaired individuals in navigating dynamic environments, including roads and indoor spaces. The system employs YOLO version 10 (YOLOv10) with dual cameras and provides audio output through a speaker. Using the Research and Development (R&D) method, the system detects six object classes—person, car, motorcycle, bicycle, table, and chair—in real-time. Testing was conducted with variations in distance, lighting conditions, delay, and direct trials with visually impaired users. Results show an effective detection range of up to 5 meters. Under bright indoor lighting, the average error was 8.97%, while outdoor morning conditions yielded 3.95%. In low-light and dark conditions, accuracy decreased significantly, with errors ranging from 60.33% to 100%. Detection delay ranged from 4.3 to 7.4 seconds. The system achieved a Macro F1-Score of 0.74, with the highest performance for cars (0.92) and the lowest for persons (0.62). Direct trials with five visually impaired participants showed an average accuracy of 92.58% and delays around 4.63 seconds. The system effectively delivers precise audio information, helping users recognize objects in front and behind, thereby enhancing safety and confidence during navigation.
The Role of Digital Technology in Realizing the Sustainable Development Goals (SDGs) Soelistianto, Farida Arinie; Muthmainah, Hanifah Nurul
West Science Information System and Technology Vol. 3 No. 03 (2025): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v3i03.2553

Abstract

Digital technology has increasingly become a strategic instrument in accelerating the achievement of the Sustainable Development Goals (SDGs), particularly in developing countries such as Indonesia. This study aims to examine the role of digital technology in supporting the achievement of SDGs in Indonesia using a quantitative research approach. Data were collected from 85 respondents through a structured questionnaire measured using a Likert scale. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25, employing descriptive statistics, validity and reliability testing, and simple linear regression analysis. The results indicate that digital technology has a positive and statistically significant effect on SDG achievement, with digital technology explaining 50.7% of the variance in sustainable development outcomes. These findings suggest that increased utilization of digital platforms and systems contributes to improved economic efficiency, social inclusion, and sustainable development practices. This study provides empirical evidence on the importance of digital transformation in achieving SDGs and offers practical insights for policymakers and stakeholders to strengthen digital-based development strategies in Indonesia
Pengenalan Kualitas Tempe Berbasis YOLOv8 untuk Deteksi Dini Kegagalan Fermentasi Mahfudi, Isa; Kusumawardania, Mila; Moechammad Sarosa, Moechammad; Setiadi, Chandrasena; Riatma, Galih Putra; Soelistianto, Farida Arinie; Muslimah, Nabila Izzatul; Izati, Nadia Yumni
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16091

Abstract

Tempe is a traditional Indonesian food whose fermentation process is highly influenced by temperature, humidity, and soybean quality. Inadequate environmental conditions can lead to fermentation failure, reducing product quality and causing economic losses. Traditionally, quality assessment of tempe has been carried out manually by artisans, which is subjective and inconsistent. This study aims to develop an automatic tempe quality recognition system based on YOLOv8, implemented on a Raspberry Pi 4B with a Logitech C270 camera, monitoring webserver, and buzzer as an early warning system. The YOLOv8 model was trained to detect two main categories, namely good tempe and failed tempe, through real-time visual analysis. Experimental results show system performance with an average accuracy of 93.1%, precision of 93.5%, recall of 91.2%, and mAP@50 of 94.7%. Confidence score analysis indicates that the model is more certain in detecting failed tempe (0.94–0.95) compared to good tempe (0.80–0.86), due to clearer visual differences.