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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Indonesian Journal on Computing (Indo-JC) Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Jurnal Pemanfaatan Teknologi untuk Masyarakat
Ikke Dian Oktaviani
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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AI dan Masa Depan Profesi : Bisakah Robot Menggantikan Pekerjaan Manusia? Febri Dawani; Zahwa Salsabila Jhovita Ahmad; Naufal Gazel Akbari; Azalia Zuhria Rahmatul Fatiyah; Muhamad Racka Vrinho; Ikke Dian Oktaviani
JAPATUM: Jurnal Pemanfaatan Teknologi untuk Masyarakat Vol 3 No 1 (2024): Maret 2024
Publisher : MATRADIPTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59328/JAPATUM.2024.3.1.90

Abstract

Kecerdasan buatan atau biasa disebut AI mengalami perkembangan pesat dalam beberapa dekade terakhir dan menjadi teknologi yang mampu meniru, bahkan melampaui, kemampuan manusia dalam sejumlah aspek pekerjaan. Mulai dari sistem otomatisasi di pabrik hingga chatbot layanan pelanggan dan algoritma prediktif di sektor keuangan, AI secara bertahap mulai mengambil alih peran-peran yang sebelumnya dijalankan oleh manusia. Kondisi ini menimbulkan kekhawatiran akan hilangnya lapangan kerja serta perubahan drastis dalam struktur profesi di masa depan. Namun demikian, tidak semua pekerjaan dapat sepenuhnya diambil alih oleh AI. Profesi yang menuntut kreativitas, empati, intuisi, serta interaksi manusia yang kompleks masih sangat bergantung pada kemampuan manusia. Selain itu, munculnya AI juga menciptakan jenis pekerjaan baru yang sebelumnya tidak ada, seperti ahli etika AI, pengembang machine learning, dan analis data. Dengan menyoroti peluang dan risiko secara seimbang, dapat memberikan gambaran menyeluruh mengenai hubungan antara perkembangan AI dan masa depan profesi, serta mengajak pembaca untuk lebih siap menghadapi perubahan dengan membekali diri dengan keterampilan yang relevan dan adaptif.
A Hybrid Genetic Algorithm-Random Forest Regression Method for Optimum Driver Selection in Online Food Delivery Aji Gautama Putrada; Nur Alamsyah; Ikke Dian Oktaviani; Mohamad Nurkamal Fauzan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27014

Abstract

The online food delivery trend has become rapid due to the COVID-19 incident, which limited mobility, while the broader challenge in the online food delivery system is maximizing quality of service (QoS). However, studies show that driver selection and delivery time are important in customer satisfaction. The solution is our research aim, which is the selection of optimal drivers for online food delivery using random forest regression and the genetic algorithm (GA) method. Our research contribution is a novel approach to minimizing delivery time in online food delivery by combining a random forest regression model and genetic algorithms. We compare random forest regression with three other state-of-the-art regression models: linear regression, k-nearest neighbor (KNN), and adaptive boosting (AdaBoost) regression. We compare the four models with metrics including , mean squared error (MSE), root mean squared error (RMSE), mean total error (MAE), and mean absolute percentage error (MAPE). We use the optimum model as the fitness function in GA. The test results show that random forest performs better than linear, KNN, and AdaBoost regression, with an , RMSE, and MAE value of 0.98, 54.3, and 11, respectively. We leverage the optimum random forest regression model as the GA fitness function. The best efficiency is reducing the delivery time from 54 to 15 minutes, achieved through rigorous testing on various cases. In addition, by completing this research, we also achieve some practical implications, such as an increase in customer satisfaction, a reduction in cost, and a paramount finding in the field of data-driven decision-making. The first key finding is an optimum driver selection model in random forest regression, while the second is an optimum driver selection model in GA.
Co-Authors Aji Gautama Putrada Avicena, Ginanjar Ilmi Nur Azalia Zuhria Rahmatul Fatiyah Betari Angeli Danuwijaya, Edghar Dawani, Febri Endro Ariyanto Fauzan, Mohamad Nurkamal Hilal Hudan Nuha Maman Abdurohman Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Racka Vrinho Muhammad Al Makky Muhammad Arief Nugroho Naufal Gazel Akbari Nuha, Hilal H Nur Alamsyah Nur Alamsyah NUR ALAMSYAH Putra, Yohanes Armenian Sidik Prabowo Zahwa Salsabila Jhovita Ahmad