Analisis Kesiapan Penerapan Pemantauan Kondisi Dan Kesehatan Aset Berbasis Kecerdasan Buatan: Studi Kasus PT Pupuk Kaltim

Tsany, Mutyara Shafa Tsany (2026) Analisis Kesiapan Penerapan Pemantauan Kondisi Dan Kesehatan Aset Berbasis Kecerdasan Buatan: Studi Kasus PT Pupuk Kaltim. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan Industri 4.0 mendorong pemanfaatan Artificial Intelligence (AI) dalam berbagai aspek operasional industri, termasuk pemeliharaan aset. Kompleksitas sistem produksi, keragaman aset, serta keterbatasan pengelolaan data membuat pendekatan konvensional kurang optimal, sehingga diperlukan sistem berbasis AI untuk condition and health asset monitoring. Namun, implementasi sistem tersebut tidak hanya bergantung pada kesiapan teknologi, tetapi juga kesiapan individu dan organisasi. Pada konteks PT Pupuk Kaltim, tantangan ini semakin kompleks akibat variasi kondisi pabrik, keterbatasan integrasi data, serta perbedaan tingkat kesiapan pekerja. Oleh karena itu, Tugas Akhir ini bertujuan menganalisis tingkat kesiapan penerapan AI-based condition and health asset monitoring dari sisi individu dan teknologi secara menyeluruh. Untuk mencapai tujuan tersebut, digunakan kerangka Technology Readiness Index (TRI) untuk mengukur kesiapan individu dalam mengadopsi teknologi baru. Namun, TRI memiliki keterbatasan karena menghitung indeks secara langsung dengan asumsi bobot yang sama pada setiap dimensi, sehingga belum mampu menangkap pengaruh relatif antar faktor. Oleh karena itu, lebih jauh lagi model tersebut dianalisis menggunakan Spearman correlation untuk menganalisis hubungan antar dimensi dalam faktor pembentuk kesiapan. Selain itu, digunakan Fuzzy-Set Qualitative Comparative Analysis (fsQCA) untuk mengidentifikasi kombinasi faktor yang membentuk kesiapan. Kemudian dilakukan segmentasi menggunakan k-means clustering untuk mengelompokkan karakteristik pekerja, yang dapat digunakan sebagai dasar perancangan program pelatihan serta rekomendasi pemerataan tenaga kerja pada masing-masing departemen. Dari sisi teknologi, kesiapan fasilitas dalam penerapan AI-based condition and health asset monitoring dievaluasi berdasarkan functional entities dalam kerangka ISO 23247 untuk mengidentifikasi tingkat kematangan sistem serta kesenjangan terhadap kondisi ideal. Hasil Tugas Akhir ini diharapkan dapat memberikan gambaran menyeluruh mengenai kesiapan implementasi serta menjadi dasar penyusunan strategi yang lebih efektif dan terarah pada PT Pupuk Kaltim.
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The advancement of Industry 4.0 has driven the adoption of Artificial Intelligence (AI) across various industrial operations, including asset maintenance. The increasing complexity of production systems, asset diversity, and limitations in data management have made conventional maintenance approaches less optimal, thereby necessitating AI-based condition and health asset monitoring systems. However, the implementation of such systems depends not only on technological readiness but also on the readiness of individuals and organizations to adopt them. In the context of PT Pupuk Kaltim, these challenges are further intensified by variations in plant conditions, limited data integration, and differences in workers’ readiness levels. Therefore, this undergraduate thesis aims to analyze the readiness level for implementing AI-based condition and health asset monitoring from both individual and technological perspectives in a comprehensive manner. To achieve this objective, the Technology Readiness Index (TRI) framework is used to measure individual readiness in adopting new technologies. However, TRI has limitations as it calculates the index by assuming equal weights across all dimensions, thus failing to capture the relative influence of each factor. Therefore, the model is further analyzed using Spearman correlation to analyze the interdimensional relationships among the factors that shape readiness. In addition, Fuzzy-Set Qualitative Comparative Analysis (fsQCA) is employed to identify combinations of factors that lead to readiness. Furthermore, segmentation using k-means clustering is conducted to classify worker characteristics, which can be utilized as a basis for designing training programs as well as recommending workforce distribution across departments. From the technological perspective, facility readiness for implementing AI-based condition and health asset monitoring is evaluated based on functional entities within the ISO 23247 framework to identify system maturity levels and gaps relative to ideal conditions. The results of this undergraduate thesis are expected to provide a comprehensive understanding of implementation readiness and serve as a basis for developing more effective and targeted strategies at PT Pupuk Kaltim.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Artificial Intelligence (AI), Condition and Health Asset Monitoring, ISO 23247, Technology Readiness Index (TRI), Artificial Intelligence (AI), Condition and Health Asset Monitoring, ISO 23247. Technology Readiness Index (TRI)
Subjects:	H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HD Industries. Land use. Labor > HD30.23 Decision making. Business requirements analysis. H Social Sciences > HD Industries. Land use. Labor > HD30.24 Feasibility studies. Feasibility appraisals
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26201-(S1) Undergraduate Thesis
Depositing User:	Mutyara Shafa Tsany
Date Deposited:	30 Jul 2026 06:13
Last Modified:	30 Jul 2026 06:13
URI:	http://repository.its.ac.id/id/eprint/140155

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