Garuda - Garba Rujukan Digital

Analisis Multidimensi Pada Perkuliahan Untuk Memperbaiki Pencapaian Course Learning Outcome (CLO) Pada Mahasiswa Tingkat 1 (Studi Kasus E-Learning Universitas Telkom) Arfin Al Hafizh; Rachmadita Andreswari; Taufik Nur Adi
Cakrawala Repositori IMWI Vol. 6 No. 5 (2023): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v6i5.513

Dalam mendukung proses pembelajaran campuran (hybrid learning) yang diterapkan saaat ini, diperlukan suatu Learning Management System (LMS). Sebagai suatu sistem komputer, LMS secara otomatis merekam setiap kegiatan yang dilakukan oleh pengguna. Semua akses ini dicatat dalam event log. Informasi yang tersimpan pada event log dapat membantu mengetahui pola belajar yang dilakukan oleh mahasiswa. Process mining digunakan untuk menganalisis proses pembelajaran yang dilakukan oleh mahasiswa yang digambarkan oleh model proses. Data cube merupakan representasi visual dari data yang dapat dilihat dari berbagai sudut pandang dengan menggunakan operasi-operasi seperti slicing, dicing, roll-up, drill-down, dan pivot. Celonis merupakan software commercial process mining yang sangat populer saat ini, dengan memanfaatkan fitur yang tersedia pada aplikasi Celonis diharapkan dapat menggambarkan model proses pembelajaran mahasiswa yang dilihat dari berbagai dimensi antara lain waktu, mata kuliah, CLO, dosen, dan nilai CLO. Dimensi dosen memberikan data tentang status dosen yang sedang mengajar. Dimensi CLO memberikan informasi tentang aktivitas berdasarkan nomor CLO. Sedangkan dimensi nilai CLO memberikan data tentang status nilai mahasiswa. Dengan menerapkan pendekatan tersebut, kita dapat membuat sebuah model proses yang menampilkan informasi dari berbagai perspektif yang ada dalam dimensi-dimensi tersebut. Setelah model proses didapatkan diterapkan evaluasi berupa conformance checking untuk melihat kesesuaian model proses dengan event log yang ada. Model proses dengan nilai conformance terbaik akan diubah menjadi BPMN agar dapat menyampaikan informasi menjadi lebih mudah. Kemudian informasi ini dapat digunakan untuk menyusun rekomendasi proses pembelajaran yang terbaik untuk mahasiswa tingkat 1 pada mata kuliah Dasar Keuangan Sistem Informasi semester berikutnya.

Analisis Sentimen Berbasis Aspek Terhadap Ulasan Pengguna Aplikasi Pegadaian Digital Menggunakan Algoritma Naïve Bayes Syfani Alya Fauziyyah; Faqih Hamami; Rachmadita Andreswari
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.245

Pegadaian. PT. Pegadaian's form of transformation is the launch of Pegadaian Digital application. The application aims to facilitate the community and improve the service of products owned by PT. Pegadaian. Based on the monitoring as of 20 October 2022, the Pegadaian Digital application received 3.5 points on a scale of 5. This score is low because it contains many negative reviews. Therefore, it is necessary to analyse the review section of the application to increase the score. The method that can be used to analyse it is aspect-based sentiment analysis. Aspects are those that relate to the experience felt by users, namely aspects of learnability, efficiency, errors, and satisfaction. Sentiment analysis requires an optimal algorithm, one of which is Naïve Bayes. This algorithm was chosen because it is known as a simple but efficient algorithm when processing large amounts of data. This research uses two test scenarios, the first scenario using different ratios and base parameters and the second scenario using the addition of smoothing parameters. The result of this research is that the model with the ratio of 80:20 and the addition of smoothing is the best model for sentiment analysis because it produces the best performance value, with an accuracy value of 92%, precision of 80%, recall of 70% and f1-score of 73%.

Analisis Sentimen Berbasis Aspek Terhadap Ulasan Pengguna Aplikasi Pegadaian Digital Dengan Multiclass Multioutput Menggunakan Algoritma Support Vector Machine Vina Fadillah; Faqih Hamami; Rachmadita Andreswari
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.248

Badan Usaha Milik Negara (BUMN) are one of the three main economic players in the country, alongside cooperatives and private enterprises, aiming to realize a prosperous society in various fields. One of the BUMN, PT Pegadaian, operates in the financial sector. According to the directorate regulation of PT Pegadaian Number 122 of 2020, to enhance the quality and maturity level of information technology (IT), evaluation and monitoring mechanisms based on the international standard ISO/IEC 25010:2011 for System Software Quality Models are required in managing IT quality. Therefore, PT Pegadaian is currently undergoing a transformation process to expand its business model that was originally only feasible through offline means, making it possible to be done online. To support this Pegadaian transformation process, an application named "Pegadaian Digital" has been developed, containing PT Pegadaian's core businesses, such as buying and selling gold savings, pawn booking, and gold price reviews. To assist digital transformation process, sentiment analysis research is conducted based on various aspects to identify aspects in the application that need to be improved and maintained. The study focuses on user reviews from Google Play Store, utilizing the KDD process and Support vector machine algorithm. The aspects used in this research are Learnability, Efficiency, Errors, and Satisfaction, each aspect labelled as positive, negatif, and neutral (not exist). The testing in this research is divided into two scenarios, focusing on the model with default parameter and parameter with hyperparameter tuning. Subsequently, the model is evaluated with accuracy, precision, recall, F1-score, and K-Fold Cross Validation. The evaluation results show that the scenario with a split data ratio of 80:20 using SVM with basic or default parameters gets the best performance results based on an accuracy value of 86%, recall 80%, f1-score 82%, precision 84%, and model did not overfitting

Analisis Multidimensi Pada Perkuliahan Untuk Memperbaiki Pencapaian CLO Mahasiswa Tingkat 4 Adha, Nizur; Andreswari, Rachmadita; Adi, Taufik Nur
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 4 (2023): Oktober 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i4.952

In this research, process mining methods are utilized for the analysis of the learning processes of fourth-year students. Multidimensional analysis is applied to gain a more comprehensive understanding of the data, and process cubes provide an overview of the data from various dimensions. Supported by Celonis tools, the learning process models are discovered from different perspectives such as time, courses, instructors, Course Learning Outcomes (CLO), and CLO scores. The application of these methods results in process models that provide insights from the perspective of different dimensions. Conformance checking is conducted to assess the alignment of the process models with the event log. The best conformance values for each process model are transformed into BPMN to facilitate information dissemination. The obtained information serves as recommendations for designing the optimal learning processes for fourth-year students in the subsequent semester.

Application of Process Mining in the Process of IT Incidents Management by Utilizing Kaggle’s Dataset Anggraeni Xena Paradita; Fakhri Arya Fadhillah; Kanza Azzahra; Michael Christensen Bonar Kasparov; Rachmadita Andreswari
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383564

The application of process mining in Information Technology (IT) incident management has been carried out. In the face of the increasing complexity of IT systems and a surge of incidents, traditional incident management is no longer effective. This study uses Kaggle's dataset on IT incident management to analyze event logs, identify process bottlenecks, and compare process flows seen in event logs with expected flows. Process mining techniques, such as process discovery and conformance, are applied using PM4PY and Celonis tools. The analysis results with process mining produce a new model process that can be applied in practice with a conformance rate of 81%.

Bottleneck and Rework Analysis of the Budget Approval at University with Process Mining Syakurnia, Barajati; Bryan Ronald Talisman; Sahra Bilqis Fauziyyah; Faturrahman; Rachmadita Andreswari
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383565

Process mining is a new science that focuses on the transparency of an existing process. Especially in a world full of digitalization, of course, many companies, education, health are immediately competing in presenting the most efficient and effective processes to do. in this study the authors used Request for Payment data owned by a university. These requests will later be checked by the travel administration, this budget approval can be done by supervisors, directors, or fund owners. the author uses Celonis tools and algorithms in Celonis to identify bottlenecks and rework in the process. We also attempted to analyze where new insights could be drawn from the resulting process model. The result of this research is to pay special attention to several activities, as well as to provide explanations of the criteria related to the application to be submitted.

Bottleneck and Resource Analysis on IT Help Desk with Process Mining Permana, Muhammad Cekas; Prameswari, Anindya; Ginting, Agriva Detta; Asjad, M. Rifadh; Syakurnia, Barajati; Andreswari , Rachmadita
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491581

This study utilizes data from the help desk log of an Italian company to create a model of the company’s business processes. The primary objectives are to show the benefit of the implementation of process mining to identify bottleneck activites within the process and analyze the workload distribution among resources. The research reveals that the most common bottlenecks occur during the transition from ‘resolve tickets’ to ‘closed’ accounting for 99% of cases, and another activity from ‘assign seriousness’ towards ‘take in charge ticket’ experiences bottlenecks in 91% of cases. Furthermore, a decrease in the number of cases was discovered after October 2012. Prior to this period, the average number of cases per resource was high, leading to a high average number of active resources per day and average number of events per day. However, after October 2012, the average number of cases per resource decreased by approximately 74.6% to 47 cases per resource. The average number of active resources also decreased by 25% to 3 active resources per day. Additionally, the average number of events per resource decreased by 40% to 3 events per resource per day. Regarding the resource workload, the analysis reveals that ‘value 2’ has the highest workload, having worked on 4,235 events. This is followed by ‘value 5’ with 3,748 events, ‘value 1’ with 3,028 events, ‘value 9’ with 2,073 events, and ‘value 13’ with 1,420 events.

Application of Data Mining For Clustering Car Sales Using The K-Means Clustering Algorithm Hutasoit, Michael Nico; Fa’rifah, Riska Yanu; Andreswari, Rachmadita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 2 (2023): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i2.307

In the digital era, data is at the core of business continuity. The need for fast, precise and accurate information is needed. Cars are one of the tertiary needs. This means of transportation is a relatively fast development and innovation business. Car sales in Indonesia recorded a reasonably high number in 2014 - 2018, namely 4.157.580 units sold. The highest sales were MPV car types being the most popular type of car, and there are many types of cars in Indonesia, including Sedans, SUV, 7 Seater SUV, and City Car types, and the enthusiasts need to play more. Hence, it is exciting to study. The variety of car brands with competing prices makes it difficult for consumers to choose the right car to buy according to their needs. This can be solved by applying data mining to cluster car sales using the k-means clustering algorithm. The goal is to know the characteristics of the car from each attribute. The k-Means algorithm is used for cluster formation based on five attributes: CC, Tank Capacity, GVW (Kg), Seater, and Door. The elbow and silhouette score methods determine the optimal number of clusters (k). The result is 4 clusters, cluster 0 (High-Performance Heavy Car), cluster 1 (Small Family Car), cluster 2 (High-Performance Small Car), and cluster 3 (Medium Performance Car). The 4 Cluster results are based on the evaluation/validation of the Elbow Method and Silhouette.

Application of Data Mining For Clustering Car Sales Using The K-Means Clustering Algorithm Hutasoit, Michael Nico; Fa’rifah, Riska Yanu; Andreswari, Rachmadita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 2 (2023): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i2.307

In the digital era, data is at the core of business continuity. The need for fast, precise and accurate information is needed. Cars are one of the tertiary needs. This means of transportation is a relatively fast development and innovation business. Car sales in Indonesia recorded a reasonably high number in 2014 - 2018, namely 4.157.580 units sold. The highest sales were MPV car types being the most popular type of car, and there are many types of cars in Indonesia, including Sedans, SUV, 7 Seater SUV, and City Car types, and the enthusiasts need to play more. Hence, it is exciting to study. The variety of car brands with competing prices makes it difficult for consumers to choose the right car to buy according to their needs. This can be solved by applying data mining to cluster car sales using the k-means clustering algorithm. The goal is to know the characteristics of the car from each attribute. The k-Means algorithm is used for cluster formation based on five attributes: CC, Tank Capacity, GVW (Kg), Seater, and Door. The elbow and silhouette score methods determine the optimal number of clusters (k). The result is 4 clusters, cluster 0 (High-Performance Heavy Car), cluster 1 (Small Family Car), cluster 2 (High-Performance Small Car), and cluster 3 (Medium Performance Car). The 4 Cluster results are based on the evaluation/validation of the Elbow Method and Silhouette.

Analisis Multidimensi Pada Perkuliahan Untuk Memperbaiki Pencapaian Course Learning Outcome (CLO) Pada Mahasiswa Tingkat 1 (Studi Kasus E-Learning Universitas Telkom) Arfin Al Hafizh; Rachmadita Andreswari; Taufik Nur Adi
Cakrawala Repositori IMWI Vol. 6 No. 5 (2023): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v6i5.513

Dalam mendukung proses pembelajaran campuran (hybrid learning) yang diterapkan saaat ini, diperlukan suatu Learning Management System (LMS). Sebagai suatu sistem komputer, LMS secara otomatis merekam setiap kegiatan yang dilakukan oleh pengguna. Semua akses ini dicatat dalam event log. Informasi yang tersimpan pada event log dapat membantu mengetahui pola belajar yang dilakukan oleh mahasiswa. Process mining digunakan untuk menganalisis proses pembelajaran yang dilakukan oleh mahasiswa yang digambarkan oleh model proses. Data cube merupakan representasi visual dari data yang dapat dilihat dari berbagai sudut pandang dengan menggunakan operasi-operasi seperti slicing, dicing, roll-up, drill-down, dan pivot. Celonis merupakan software commercial process mining yang sangat populer saat ini, dengan memanfaatkan fitur yang tersedia pada aplikasi Celonis diharapkan dapat menggambarkan model proses pembelajaran mahasiswa yang dilihat dari berbagai dimensi antara lain waktu, mata kuliah, CLO, dosen, dan nilai CLO. Dimensi dosen memberikan data tentang status dosen yang sedang mengajar. Dimensi CLO memberikan informasi tentang aktivitas berdasarkan nomor CLO. Sedangkan dimensi nilai CLO memberikan data tentang status nilai mahasiswa. Dengan menerapkan pendekatan tersebut, kita dapat membuat sebuah model proses yang menampilkan informasi dari berbagai perspektif yang ada dalam dimensi-dimensi tersebut. Setelah model proses didapatkan diterapkan evaluasi berupa conformance checking untuk melihat kesesuaian model proses dengan event log yang ada. Model proses dengan nilai conformance terbaik akan diubah menjadi BPMN agar dapat menyampaikan informasi menjadi lebih mudah. Kemudian informasi ini dapat digunakan untuk menyusun rekomendasi proses pembelajaran yang terbaik untuk mahasiswa tingkat 1 pada mata kuliah Dasar Keuangan Sistem Informasi semester berikutnya.

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