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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science JRSI (Jurnal Rekayasa Sistem dan Industri) IJISTECH (International Journal Of Information System & Technology) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Cakrawala Repositori Imwi IJISTECH Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Rachmadita Andreswari
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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 Terhadap Objek Wisata Di Provinsi Jawa Timur, Jawa Tengah, Jawa Barat, Banten, Dan Dki Jakarta Pada Platform Google My Business Menggunakan Algoritma Decision Tree Ramdani, Dwi Fickri Insan; Andreswari, Rachmadita; Hamami , Faqih
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak—Pariwisata merupakan salah satu sektor pentingdan unggulan yang memberikan kontribusi terhadap ekonominasional. Tingkat kepuasan wisatawan terhadap sebuah objekwisata dapat dilihat dari review yang diberikan, salah satunyaplatform yang dapat digunakan untuk melihat reviewwisatawan adalah Google My Business. Penerapan analisissentimen mengunakan tiga sentimen, positif, negatif, dan netral.Dengan penerapan sentiment analysis dan multidimensionalmenggunakan metode Decision tree dapat diketahui sentimenyang diberikan wisatawan terhadap sebuah objek wisata.Seperti pada penelitian ini, review terhadap objek wisata yangada di provinsi Jawa Timur, Jawa Tengah, Jawa Barat, Banten,dan DKI Jakarta didapatkan data sebanyak 12.680. kemudiansetelah dilakukan preprocessing dan labeling menghasilkandata bersih sebanyak 8.615. pelabelan menggunakan dualibrary yaitu transformer dan textblob serta untuk setiaplibrary akan dicoba dengan tiga split data yang berbeda yaitu70:30, 80:20, dan 90:10 yang bertujuan untuk mengetahuikombinas yang pas untuk pembuatan model machine learningini. Dari hasil analisis sentiment menggunakan algoritmadecision tree dengan pelabelan menggunakan librarytransformer dan split data 70:30 didapatkan nilai akurasisebesar 78%. Hasil prediksi akan ditampilakan pada dashboardmenggunakan Power BI untuk memudahkan dalam memahami data Kata kunci— Sentiment Analisis, Multidimensional Analisis, Decision tree, Transformer, Textblob, Power BI
Implementasi Dan Analisis Robotic Process Automation Menggunakan Aplikasi Uipath Untuk Pengecekan Pendaftaran Sidang Online (Studi Kasus: Universitas Telkom) Arkhan M , Mochammad Alifha; Andreswari, Rachmadita; Adi , Taufik Nur
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak — Di era modern, proses bisnis yang digunakanberulang kali sangatlah tidak efisien. Otomatisasi proses robotik(RPA) adalah jenis perangkat lunak yang mempercepat tugasyang berulang dan memakan waktu dengan membantu manusia.Menurut studi Infosys, penerapan RPA dapat memangkaspekerjaan manual sebesar 58% dan mengurangi Setara WaktuPenuh (FTE) sebesar 50%. Efektivitas biaya dan kepatuhanterhadap persyaratan perizinan tidak lagi menjadi beban yangmemberatkan saat menerapkan integrasi sistem berkat RPA.UiPath adalah salah satu alat yang memfasilitasi web scraping.Proses bisnis dalam suatu organisasi dapat dirancang dandiotomatisasi dengan bantuan teknologi RPA UiPath [1]. Akandilakukan web scraping pada registrasi uji coba pada aplikasiSOFI menggunakan UiPath yang akan digunakan untukmenghasilkan dokumen laporan pengajuan uji coba. Kata kunci : RPA, Web Scraping, E-learning, UiPath
Co-Authors Adha, Nizur Adi , Taufik Nur Adi Purnomo Sidik Aisya Hanifa Alvi Syahrina Anadia Salsabella Syakhina Andhy Bhaskoro Andrini Hanariana Andyani Chris Thalia Udiono Anggraeni Xena Paradita Annisa Umaira Arrahim Arfin Al Hafizh Ari Yanuar Ari Yanuar Ridwan Ari Yanuar Yanuar Arkhan M , Mochammad Alifha Asjad, M. Rifadh Asti Amalia Nur Fajrillah ATIK NOVIANTI Axel Devino Aipassa Ayu Cahyani Febryanti Ayu Cahyani Febryanti Ayu Cahyani Febryanti, Ayu Cahyani Bagas Rezkita Bayu Ariantika Irsan Bayu Pradana Berlian Maulidya Izzati Bryan Ronald Talisman Budi Santosa Budiwari Rizki Fadhilah Deden Witarsyah Dewi Rahmayanti Dewi Rahmayanti Dhany Nurdiansyah Dhiya Afwan Taufiq Dianaros Pakel Dita Pramesti Ekky Novriza Alam Elang Maulana Jauhari Fa'rifah, Riska Yanu Fadhilah Fazrin Fadhilah, Budiwari Rizki Faisal Mufied Al Anshary Faishal Mufied Al Anshary Fakhri Arya Fadhillah Faqih Hamami Faturrahman Fauzi, Rokhman Fitriyana Dewi Fransiska Pinem Ginanjar Dewi Girang Ginting, Agriva Detta Girang, Ginanjar Dewi Harri Margono Hasibuan, Muhammad Hidayatul Aji Adika Putra Hutasoit, Michael Nico I Made Dwima Gita Dirtana Indha Lukitaningtyas Iqbal Aditya Salam Irfan Darmawan Iskandar Agung Isye S. Adhiwinaya Jagur Pria Abiyyu Kanza Azzahra Kemal Indra Kusuma M Firman Helmi Ariyansyah Margareta Hardiyanti Melinsye Herliani Ahab Michael Christensen Bonar Kasparov Muhamad Alshofien Gautama Muhamad Azani Hasibuan Muhammad Azani Hasibuan Muhammad Hasibuan Muhammad Shaufi Imanulhaq Mutiara, Nabila Nabila Mutiara Narita Ayu Prahastiwi Nassyfa Alfirda Riani Nia Ambarsari Oktariani Nurul Pratiwi Permana, Muhammad Cekas Pinem, Fransiska Prameswari, Anindya Rahmat Fauzi Ramdani, Dwi Fickri Insan Regina Ayu Prameswari Wade Revo Faris Saifuddin Ridha Hanafi Rinaldi Tambunan Rini Nur’aini Riza Agustiansyah Rizka Nursyahdilla Puspitasari Rizky Alamsyah Sahra Bilqis Fauziyyah Satrio Wibowo Selvyananda Adelita Vanesia Silvia Firdaus Sinung Suakanto Soni Fajar Surya Gumilang Sukrina Herman Sukrina Herman, Sukrina Sutoyo, Edi Syakurnia, Barajati Syfani Alya Fauziyyah Taufik Nur Adi Umar Yunan Kurnia Septo Hediyanto Vandha Pradwiyasma Widartha Vina Fadillah Warih Puspitasari Wibowo, Satrio Widyatasya Agustika Nurtrisha Yudha Aditya Ramadhana