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Effect of IoT Integration in Energy Management System and Grid Responsiveness on Energy Efficiency and Cost Reduction in Jakarta Government Buildings Rohayani, Hetty; Ermaini, Ermaini; Handayani, Rahmi; Rahardian, Rifky Lana; Nanjar, Agi
West Science Interdisciplinary Studies Vol. 2 No. 05 (2024): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v2i05.930

This study investigates the effect of IoT integration in energy management systems (EMS) and network responsiveness on energy efficiency and cost reduction in DKI Jakarta government buildings. A quantitative analysis was conducted using structural equation modeling (SEM) with Partial Least Squares (PLS) 3. Survey questionnaires were administered to building managers, energy engineers, and facility maintenance personnel, while energy consumption data were obtained from relevant authorities. The results demonstrate a significant positive impact of both IoT integration and network responsiveness on energy management outcomes. Specifically, Energy Management System (EMS) and Grid Responsiveness were found to significantly influence both Cost Reduction and Energy Efficiency within government buildings. These findings have important implications for policymakers, building managers, and stakeholders involved in energy management decision-making. By leveraging advanced energy management technologies and practices, DKI Jakarta government buildings can achieve significant cost savings, improve energy efficiency, and advance environmental sustainability.

Building Sustainable Communities: SIMARET Development for Financial Transparency with MDALC Approach Saputro, Rujianto Eko; Nanjar, Agi; safitri feriawan, Titi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14150

The increasing need for financial transparency and efficiency in community-level governance, particularly within Rukun Tetangga (RT) in Indonesia, calls for innovative solutions. This study presents the development of SIMARET, a mobile application designed to enhance the management of RT financial activities and resident participation, using the Mobile Application Development Life Cycle (MDALC) approach. The research aims to address the challenges of manual financial management, such as lack of transparency and difficulties in tracking funds and activities like neighborhood watch (Siskamling). SIMARET incorporates key features such as digital tracking of resident contributions (jimpitan), QR code-based attendance for Siskamling, and automated financial reports. The system was developed through MDALC’s structured phases: identification, design, development, testing, and deployment. Blackbox Testing and User Acceptance Testing (UAT) were conducted to ensure functionality and user satisfaction. The results show a high satisfaction rate of 97%, confirming that SIMARET simplifies financial administration and enhances community participation. The study also highlights the application’s contribution to the United Nations Sustainable Development Goals (SDG) 16 by promoting transparency and effective governance at the local level. Although SIMARET demonstrates significant potential, further research is recommended to improve its user interface design and expand its implementation in other communities.

Machine Learning and Deep Learning Approaches for Energy Prediction: A Systematic Literature Review Nanjar, Agi; Saputro, Rujianto Eko; Berlilana, Berlilana
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14208

This paper offers a literature review on the application of Machine Learning (ML) and Deep Learning (DL) techniques in energy prediction. Contemporary energy systems' challenges, such as load fluctuations and uncertainties linked to renewable energy sources, render traditional methods like ARIMA and linear regression insufficient. The objective of this paper is to identify the most widely used ML and DL approaches, compare their performance against conventional methods, and explore the implementation challenges along with potential solutions. The methodology for this literature review involves analyzing publications from Scopus, IEEE Xplore, and ScienceDirect covering the period from 2019 to 2024. The findings indicate that DL methods, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, are effective in handling sequential data, while hybrid models like CNN-GRU enhance prediction accuracy in innovative grid applications. Challenges identified include overfitting and data complexity, which can be addressed through regularization techniques and computational optimization using GPUs. In conclusion, this paper asserts that ML and DL play a significant role in improving prediction accuracy and facilitating the transition towards sustainable energy and smart grids. To further enhance performance in the future, the paper recommends the development of ensemble models and the integration of attention mechanisms.

Optimizing Marketplace Registration Page Design with Predictive Heatmap Analysis Bagaskoro, Galih; Eko Saputro, Rujianto; Shouni Barkah, Azhari; Nanjar, Agi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14547

Optimizing marketplace registration pages is crucial for improving user experience and conversion rates. This study evaluates the design of registration pages for four leading Indonesian marketplaces Tokopedia, Shopee, Blibli, and Lazada—using Predictive Heatmaps from UX Pilot alongside Heuristic Evaluation and Gestalt Principles. The analysis identifies key usability issues, such as distractions from branding elements, inconsistent visual hierarchy, and a lack of real-time validation and feedback mechanisms. Findings indicate that while branding elements effectively capture user attention, they often divert focus from essential features, a trend observed not only in these marketplaces but also in broader UI design contexts. such as Call-to-Action (CTA) buttons and registration forms. Shopee and Lazada successfully utilize high-contrast CTA buttons to direct user interaction, whereas Tokopedia and Blibli suffer from visual distractions caused by mascots and unnecessary decorative elements. Heatmap results also reveal inconsistent grouping of interface components, reducing page efficiency. To enhance user experience and conversion rates, recommendations include improving CTA button visibility through contrasting colors and strategic placement, minimizing decorative distractions, and implementing real-time validation and feedback. The application of Gestalt Principles further aids in optimizing interface organization by grouping related elements more effectively. This study underscores the importance of a structured design approach incorporating heuristic and predictive analytics to enhance the usability of online registration pages. Future research may explore the impact of interactive elements and A/B testing in refining registration interfaces.

Menjelajahi Tantangan dan Kemajuan Dalam Deep Learning Untuk Readmisi Pasien: Tinjauan Literatur Sistematis Surur, Miftahus; Tahyudin, Imam; Saputra, Dhanar Intan Surya; Nanjar, Agi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.681

Prediksi readmisi pasien telah menjadi tantangan utama dalam meningkatkan kualitas layanan kesehatan. Penelitian ini menyajikan tinjauan sistematis terhadap algoritma deep learning, dengan menganalisis 30 artikel dari database utama seperti Scopus, IEEE Xplore, dan ScienceDirect. Proses pencarian literatur dilakukan menggunakan kombinasi kata kunci seperti 'deep learning', 'readmisi pasien', dan 'prediksi kesehatan' serta mengikuti kerangka PRISMA untuk menyaring studi yang relevan berdasarkan kriteria inklusi dan eksklusi. Hasil penelitian menunjukkan bahwa algoritma Long Short-Term Memory (LSTM) mendominasi dalam menangkap pola temporal dari data Electronic Health Record (EHR), dengan kinerja mencapai Area Under the Curve (AUC) hingga 88,4%. Selain itu, Convolutional Neural Networks (CNN) terbukti efektif untuk menganalisis teks tidak terstruktur, sementara model Transformer menunjukkan potensi dalam menangani dataset berskala besar. Tantangan utama yang ditemukan meliputi ketidakseimbangan data dan heterogenitas data medis, yang dapat mempengaruhi akurasi prediksi. Solusi inovatif seperti federated learning dan Explainable AI (XAI) diusulkan untuk meningkatkan interpretabilitas dan efisiensi algoritma dalam konteks klinis. Penelitian ini memberikan wawasan berharga mengenai potensi dan keterbatasan deep learning dalam prediksi readmisi pasien serta menawarkan rekomendasi strategis untuk pengembangan teknologi kesehatan yang lebih baik.

Deciphering Weather Dynamics and Climate Shifts in Seattle for Informed Risk Management Ramadani, Nevita; Nanjar, Agi
International Journal for Applied Information Management Vol. 5 No. 3 (2025): Regular Issue: September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i3.105

This research presents a comprehensive analysis of the weather characteristics in the city of Seattle over the past few years. Through a detailed understanding of the distribution of maximum and minimum temperatures, the findings indicate significant fluctuations between summer and winter seasons. The increasing temperature trend from year to year provides insights into the potential climate changes in the region. Additionally, rainfall data reveals consistent increases over time, particularly during the winter, with significant impacts on the environment and daily life. Wind speed stability throughout the year provides insights into wind dynamics, influencing the transportation and maritime sectors. Annual averages of rainfall, sunshine hours, snowfall, and foggy days provide foundational information for long-term planning and risk management in Seattle. The percentage of rainy and clear weather throughout the year gives a comprehensive overview of the seasons, facilitating daily activity planning. Through these findings, the research aims to make a significant contribution to the understanding of the general public, natural resource managers, and economic sectors regarding the potential impacts and opportunities arising from future weather changes. It is hoped that this research can serve as a solid foundation in efforts to mitigate and adapt to the continually changing weather dynamics in the city of Seattle

Classification and Prediction of Video Game Sales Levels Using the Naive Bayes Algorithm Based on Platform, Genre, and Regional Market Data Putra, Rafi Pratama; Ramadani, Nevita Cahaya; Nanjar, Agi
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.242

The exponential expansion of the video game industry has resulted in a vast accumulation of market data that can be leveraged to analyze and predict sales performance. This study aims to construct a classification model for video game sales levels by applying the Naïve Bayes algorithm, recognized for its simplicity, efficiency, and strong baseline performance in supervised learning tasks. The research employs a public dataset containing over 13,000 video game entries, encompassing key attributes such as genre, platform, publisher, release year, user and critic ratings, and global sales figures. The target variable global sales was discretized into three categories: Low (1 million units), Medium (1–5 million units), and High (5 million units) to represent distinct tiers of commercial success. Prior to modeling, the dataset underwent a comprehensive preprocessing pipeline involving duplicate removal, handling of missing data, normalization of numerical attributes, and feature selection to ensure optimal model performance. The Multinomial Naïve Bayes classifier was then implemented and assessed using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results revealed an accuracy of 71.82% and an F1-score of 70.03%, signifying strong predictive capability for a probabilistic model of this simplicity. The classifier effectively identified low and medium sales categories, though slightly underperformed on the high sales group due to class imbalance within the dataset. Further analysis of conditional probabilities indicated that game genre, platform popularity (especially PS2 and Wii), and critic scores were the most influential determinants of higher sales outcomes. These findings affirm that the Naïve Bayes algorithm provides a reliable and interpretable foundation for video game sales prediction, serving as a benchmark model in market analytics. Future studies are encouraged to address data imbalance through oversampling or synthetic data generation, incorporate contextual variables such as marketing strategies and release schedules, and explore ensemble or deep learning approaches to enhance predictive accuracy and robustness.

Effect of IoT Integration in Energy Management System and Grid Responsiveness on Energy Efficiency and Cost Reduction in Jakarta Government Buildings Rohayani, Hetty; Ermaini, Ermaini; Handayani, Rahmi; Rahardian, Rifky Lana; Nanjar, Agi
West Science Interdisciplinary Studies Vol. 2 No. 05 (2024): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v2i05.930

This study investigates the effect of IoT integration in energy management systems (EMS) and network responsiveness on energy efficiency and cost reduction in DKI Jakarta government buildings. A quantitative analysis was conducted using structural equation modeling (SEM) with Partial Least Squares (PLS) 3. Survey questionnaires were administered to building managers, energy engineers, and facility maintenance personnel, while energy consumption data were obtained from relevant authorities. The results demonstrate a significant positive impact of both IoT integration and network responsiveness on energy management outcomes. Specifically, Energy Management System (EMS) and Grid Responsiveness were found to significantly influence both Cost Reduction and Energy Efficiency within government buildings. These findings have important implications for policymakers, building managers, and stakeholders involved in energy management decision-making. By leveraging advanced energy management technologies and practices, DKI Jakarta government buildings can achieve significant cost savings, improve energy efficiency, and advance environmental sustainability.

Utilization of Smart Agricultural Technology to Improve Resource Efficiency in Agro-industry Zulfikhar, Rosa; Alaydrus, Ali Zainal Abidin; Sutiharni, Sutiharni; Nanjar, Agi; Hartati , Hartati
West Science Agro Vol. 2 No. 01 (2024): West Science Agro
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsa.v2i01.656

This study investigates the utilization of smart agricultural technologies to improve resource efficiency in the agro-industry, using a quantitative approach with a focus on Structural Equation Modeling - Partial Least Squares (SEM-PLS) analysis. A survey of 250 agro-industry stakeholders produced descriptive statistics showing a high mean adoption score (4.2) and a significant frequency of adoption (75%). Resource efficiency indicators, including average water use (32.5 gallons per hectare), average energy consumption (15.8 kWh per hectare), and average crop yield (2,800 kg per hectare), were also assessed. The SEM-PLS results showed strong reliability and validity of the measurement model, with positive path coefficients indicating substantial impacts of smart technology adoption on water use efficiency, energy consumption optimization, and crop yield. The model showed a satisfactory fit, and bootstrapping confirmed the robustness of the relationships. The discussion highlights practical implications for farmers, policymakers, and technology providers, emphasizing the potential for increased efficiency, reduced costs, and improved yields through the adoption of smart technologies. This study contributes valuable insights to the discourse of sustainable agricultural practices.

Effect of IoT Integration in Energy Management System and Grid Responsiveness on Energy Efficiency and Cost Reduction in Jakarta Government Buildings Rohayani, Hetty; Ermaini, Ermaini; Handayani, Rahmi; Rahardian, Rifky Lana; Nanjar, Agi
West Science Interdisciplinary Studies Vol. 2 No. 05 (2024): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v2i05.930

This study investigates the effect of IoT integration in energy management systems (EMS) and network responsiveness on energy efficiency and cost reduction in DKI Jakarta government buildings. A quantitative analysis was conducted using structural equation modeling (SEM) with Partial Least Squares (PLS) 3. Survey questionnaires were administered to building managers, energy engineers, and facility maintenance personnel, while energy consumption data were obtained from relevant authorities. The results demonstrate a significant positive impact of both IoT integration and network responsiveness on energy management outcomes. Specifically, Energy Management System (EMS) and Grid Responsiveness were found to significantly influence both Cost Reduction and Energy Efficiency within government buildings. These findings have important implications for policymakers, building managers, and stakeholders involved in energy management decision-making. By leveraging advanced energy management technologies and practices, DKI Jakarta government buildings can achieve significant cost savings, improve energy efficiency, and advance environmental sustainability.

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