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All Journal Jurnal Scientia Jurnal Multidisiplin West Science West Science Interdisciplinary Studies The Eastasouth Journal of Information System and Computer Science Science Information System and Technology West Science Nature and Technology
Hanifah Nurul Muthmainah
Universitas Siber Muhammadiyah
Humanities Computer Science & IT Economics, Econometrics & Finance Education Energy Mathematics Social Sciences

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Pengaruh Implementasi IoT, Sistem Manajemen Aset, dan Analisis Data terhadap Efisiensi Operasional pada Perusahaan Start-up di Jakarta Agi Nanjar; Said Hamzali; Hanifah Nurul Muthmainah; Mislan Sihite; Eko Sudarmanto
Jurnal Multidisiplin West Science Vol 3 No 06 (2024): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v3i06.1345

Abstract

Penelitian ini menyelidiki dampak dari implementasi IoT, sistem manajemen aset, dan analitik data terhadap efisiensi operasional dalam perusahaan start-up di Jakarta. Dengan menggunakan Structural Equation Modeling (SEM-PLS), penelitian ini meneliti bagaimana praktik teknologi dan manajerial ini mempengaruhi kinerja bisnis. Temuan menunjukkan hubungan positif yang signifikan antara implementasi IoT, sistem manajemen aset, analisis data, dan efisiensi operasional. Validasi model pengukuran menegaskan keandalan dan validitas konstruk. Implikasi praktis menyoroti pentingnya integrasi teknologi secara strategis untuk mengoptimalkan operasi dan mendapatkan keunggulan kompetitif dalam ekosistem start-up.
The Effect of Artificial Intelligence Adoption, Demand Prediction, and Production Planning on Operational Efficiency in the Textile Industry in Jakarta Loso Judijanto; Khamaludin Khamaludin; Mahmudin Mahmudin; Devi Susiati; Hanifah Nurul Muthmainah
West Science Interdisciplinary Studies Vol. 2 No. 02 (2024): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v2i02.669

Abstract

This research investigates the impact of Artificial Intelligence (AI) adoption, demand prediction, and production planning on operational efficiency within the textile industry in Jakarta. A quantitative approach, employing surveys and statistical analysis, was undertaken with a diverse sample of 150 participants representing various company sizes and industry tenures. The study reveals a moderate level of AI adoption, with machine learning algorithms and predictive analytics being prevalent. While perceived benefits include improved production efficiency and enhanced quality control, challenges such as initial investment costs and the need for skilled personnel underscore the nuanced landscape of AI integration. The effectiveness of demand prediction is moderate, with traditional methods prevailing but advanced analytics demonstrating higher efficacy. Production planning strategies exhibit a positive correlation with Industry 4.0 principles, showcasing their role in enhancing operational efficiency. Participants perceive operational efficiency positively, with significant correlations identified between AI adoption, demand prediction, production planning, and perceived efficiency. Key factors contributing to operational efficiency include streamlined processes, effective resource utilization, and adaptive production planning. The findings provide actionable insights for industry stakeholders, emphasizing the importance of a holistic approach to technology adoption and strategic planning.
The Impact of Scalability and Consistency Management on Database Management System Performance in Big Data Environment in Indonesia Eri Mardiani; Yesi Sriyeni; Astrid Napita Sitorus; Hanifah Nurul Muthmainah
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.181

Abstract

The swift expansion of technology start-up enterprises in Indonesia demands a deep comprehension of the variables impacting the functionality of Big Data Environments (BDE) and Database Management Systems (DMS). In the context of Indonesian start-ups, this study examines the effects of scalability and consistency management on DMS and BDE. Data from 134 participants were examined using Structural Equation Modeling (SEM-PLS) in a quantitative manner. The findings showed a strong favorable correlation between DMS -> BDE, Scalability -> DMS, and Consistency Management -> DMS. Strong reliability was exhibited by the measurement model, and discriminant validity was verified. While the model fit indices revealed places for improvement, the R Square values indicated an effective explanation of variation. An overview of Indonesian start-up characteristics that is representative was given by the demographic sample study. This research adds knowledge for improving big data and database operations in the dynamic startup environment.
The Influence of Data Quality and Machine Learning Algorithms on AI Prediction Performance in Business Analysis in Indonesia Loso Judijanto; Donny Muda Priyangan; Hanifah Nurul Muthmainah; I Wayan Jata
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.182

Abstract

This research investigates the intricate relationships among AI prediction performance, business analysis, data quality, and machine learning algorithms within the manufacturing sector in Indonesia. Through structural equation modeling analysis, the study explores the impact of these variables on one another, shedding light on the dynamics that contribute to successful AI adoption and business decision-making. The findings underscore the pivotal role of data quality in influencing AI prediction performance and machine learning algorithms, ultimately shaping the effectiveness of business analysis. The results provide practical insights for manufacturing companies seeking to optimize their data management practices and harness the potential of advanced technologies for strategic decision-making.
Pengaruh Pengembangan Aplikasi Mobile, Internet of Things (IoT), dan Kualitas Sistem terhadap Kepuasan Pengguna pada Industri Manufaktur di Jakarta Rully Fildansyah; Deddy Hidayat; Hanifah Nurul Muthmainah
Jurnal Multidisiplin West Science Vol 3 No 08 (2024): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v3i08.1582

Abstract

Penelitian ini menyelidiki dampak dari pengembangan aplikasi mobile, integrasi Internet of Things (IoT), dan kualitas sistem terhadap kepuasan pengguna dalam industri manufaktur di Jakarta. Pendekatan penelitian kuantitatif digunakan, dengan mengumpulkan data dari 80 responden melalui kuesioner terstruktur yang menggunakan skala Likert mulai dari 1 hingga 5. Analisis data yang dilakukan dengan menggunakan SPSS versi 26 menunjukkan bahwa semua hipotesis yang diajukan positif dan signifikan secara statistik. Temuan ini menunjukkan bahwa kemajuan dalam pengembangan aplikasi mobile, adopsi teknologi IoT, dan peningkatan kualitas sistem secara signifikan berkontribusi terhadap peningkatan kepuasan pengguna. Di antara faktor-faktor tersebut, kualitas sistem muncul sebagai prediktor terkuat untuk kepuasan, diikuti oleh pengembangan aplikasi mobile dan integrasi IoT. Studi ini menggarisbawahi pentingnya inovasi teknologi dan keandalan sistem dalam mencapai kepuasan pengguna di sektor manufaktur di Jakarta, serta memberikan wawasan yang berharga bagi para praktisi industri dan pembuat kebijakan.
Artificial Intelligence dan Big Data: Analisis Bibliometrik terhadap Inovasi Teknologi dan Tantangan Penelitian Loso Judijanto; Nanny Mayasari; Sri Widiastuti; Dwi Yuniasih Saputri; Hanifah Nurul Muthmainah
Jurnal Multidisiplin West Science Vol 3 No 09 (2024): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v3i09.1606

Abstract

Penelitian ini melakukan analisis bibliometrik untuk mengeksplorasi evolusi dan hubungan antardisiplin dalam penelitian Artificial Intelligence (AI) menggunakan VOSviewer. Hasil studi mengungkapkan bahwa AI telah berintegrasi secara luas dalam berbagai sektor, termasuk kesehatan, keuangan, dan pengembangan kota cerdas, menunjukkan dampak transformasionalnya di berbagai bidang. Analisis ini juga menyoroti pentingnya data science dan cloud computing dalam mendukung kapabilitas AI serta menekankan isu keamanan, privasi, dan etika sebagai pertimbangan penting dalam pengembangan AI. Selanjutnya, temuan menunjukkan kebutuhan kolaborasi interdisipliner untuk mengatasi tantangan kompleks yang dihadapi dalam penerapan AI. Studi ini memberikan panduan yang berguna untuk pengembangan kebijakan dan strategi yang dapat memanfaatkan potensi AI sambil mengatasi risiko yang terkait.
Analysis of Plant Watering Efficiency Using IoT Technology Controlled Through Google Assistant Nur Hakim; Muhammad Hazmi; Syam Gunawan; Arnes Yuli Vandika; Hanifah Nurul Muthmainah
West Science Nature and Technology Vol. 2 No. 03 (2024): West Science Nature and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsnt.v2i03.1296

Abstract

This paper presents a systematic literature review (SLR) of 120 academic documents sourced from the Scopus database, which analyses the efficiency of crop watering systems utilizing Internet of Things (IoT) technology controlled via Google Assistant. The review explores key advances in IoT-based irrigation systems, highlighting how real-time data from sensors and voice-controlled automation improve water efficiency and user experience. The findings reveal that IoT systems can reduce water wastage by 25-30%, optimise plant health, and offer convenience through voice commands. However, challenges such as connectivity issues, high implementation costs, and system maintenance complexity also need to be addressed. This paper discusses potential future research directions, including scalability, AI integration, and cost-effective solutions to expand the adoption of these technologies in agriculture and horticulture.
Co-Authors Abrar Hiswara Aditya Djaini Agi Nanjar Andi Haslinah ARIEF BUDI PRATOMO Arnes Yuli Vandika Astrid Napita Sitorus Deddy Hidayat Devi Susiati Dewantara, Rizki Dianta Mustofa K. Donny Muda Priyangan Eri Mardiani Gogor Christmass Setyawan Hazmi, Muhammad Jata, I Wayan Khamaludin Khamaludin Kristiono, Natal Loso Judijanto Mahmudin Mahmudin Mislan Sihite, Mislan Moh. Erkamim Muh. Aniar Hari Swasono Nanny Mayasari Nur Hakim Purnamasari, Etika Rifky Lana Rahardian Rully Fildansyah Said Hamzali Saputri, Dwi Yuniasih Sri Widiastuti Sriyeni, Yesi Sudarmanto, Eko Supriandi Supriandi Susatyo Adhi Pramono, Susatyo Adhi Syam Gunawan Tobias Duha Wibowo, Gentur Wahyu Nyipto Widyatmoko Widyatmoko Wily Mohammad Zunan Setiawan