Internet of Things as Engine of Economic Growth in Indonesia

Widagdo, Bambang; Rofik, Mochamad

doi:10.25134/ijbe.v2i1.1625

Cited by 7 publications

(5 citation statements)

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“…In Southeast Asia, Indonesia has captured most of the region's digital transformation. The rapid growth of digitalization in Indonesia has been driven by the growing Internet penetration, not only in terms of user base [79] but also Internet-based digitalization in numerous sectors [80], including governmental affairs. In Indonesia, the shifting to egovernment is undergoing in various public sectors, with urban areas in Java Island being the leading region of digital innovations in the delivery of public services [81,82].…”

Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

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Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.

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Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

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“…Di Indonesia, penggunaan IIoT ini telah terbukti memberikan kontribusi positif pada pertumbuhan ekonomi. Dua sektor yang telah terdampak secara signifikan adalah industri pariwisata dan ritel (Widagdo & Rofik, 2019). Namun sayangnya banyak pelaku industri menghadapi kesulitan untuk menerapkan IIoT pada usahanya.…”

Section: Latar Belakangunclassified

Integrated Marketing Communication Pada Usaha Industrial Internet of Things - Machine Vision

2020

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In this era, the Indonesian government is committed to increase Indonesia competitiveness position towards other countries. To achieve this goal, the Ministry of Industry spur domestic industries to innovate in the face of Industry 4.0. One company that answers this challenge is Machine Vision that focuses on Industrial Internet of Things. It optimizes modernizes, and digitizes the Indonesian industry to help companies grow, maximize productivity, and promote efficiency in business processes. This new and unique type of business has difficulties that must be overcome, where it must be able to penetrate an unusual market. Therefore, this study discussed the strategy of Integrated Marketing Communication (IMC) conducted by Machine Vision. This study uses qualitative descriptive research methods with structured interview data collection techniques. The results of this study are, IMC strategy has been contributed positively to Machine Vision with the division of two groups, namely primary and secondary. The positive impact was assessed from the existence of repeat customers and client interest in content on Machine Vision’s social media. But aside from those positive effects, also found several threats in the future.

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“…Hal ini dikarenakan indistri pengolahan dan industri berbasis teknologi memiliki dampak pengganda yang lebih besaru jika dibandingkan dengan sektor tambang. Industri manufaktur dan industri berbasis teknologi mempunyai potensi lebih besar dalam penyerapan tenaga kerja (Rofik, Lestari, & Septianda, 2018;Widagdo & Rofik, 2019).…”

Section: Pemilihan Model Regresiunclassified

Investasi dan Daya Beli Menurunkan Ekspor? Data dari Asean 5

Pramuja

2019

JIKO

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Tujuan dari penelitian ini untuk mengetahui kinerja investasi, daya beli, dan kurs, terhadap ekspor barang pabrik di ASEAN-5. Adapun hasil penelitian menunjukkan bahwa Investasi berpengaruh signifikan pada taraf 15%, artinya bahwa setiap peningkatan investasi lansung akan menurunkan ekspor pada negara ASEAN-5 di tahun 2010-2016. Begitu juga Paritas Daya Beli berpengaruh signifikan namun negatif, ini menggambarkan bahwa setiap terjadinya peningkatan peritas daya beli masyarakat, maka akan menurunkan ekspor barang, artinya selera masyarakat dalam mengkonsumsi barang lebih cendrung memilih barang produksi dalam negeri. Sedangkan kurs tidak berpengaruh signifikan, artinya bahwa perubahan harga yang terjadi secara gelobal tidak memberikan pengaruh terhadap ekspor barang.

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Internet of Things as Engine of Economic Growth in Indonesia

Cited by 7 publications

References 0 publications

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Integrated Marketing Communication Pada Usaha Industrial Internet of Things - Machine Vision

Investasi dan Daya Beli Menurunkan Ekspor? Data dari Asean 5

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